---
_id: '14105'
abstract:
- lang: eng
text: "Despite their recent success, deep neural networks continue to perform poorly
when they encounter distribution shifts at test time. Many recently proposed approaches
try to counter this by aligning the model to the new distribution prior to inference.
With no labels available this requires unsupervised objectives to adapt the model
on the observed test data. In this paper, we propose Test-Time SelfTraining (TeST):
a technique that takes as input a model trained on some source data and a novel
data distribution at test time, and learns invariant and robust representations
using a student-teacher framework. We find that models adapted using TeST significantly
improve over baseline testtime adaptation algorithms. TeST achieves competitive
performance to modern domain adaptation algorithms [4, 43], while having access
to 5-10x less data at time of adaption. We thoroughly evaluate a variety of baselines
on two tasks:\r\nobject detection and image segmentation and find that models
adapted with TeST. We find that TeST sets the new stateof-the art for test-time
domain adaptation algorithms. "
article_processing_charge: No
author:
- first_name: Samarth
full_name: Sinha, Samarth
last_name: Sinha
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bernt
full_name: Schiele, Bernt
last_name: Schiele
citation:
ama: 'Sinha S, Gehler P, Locatello F, Schiele B. TeST: Test-time Self-Training under
distribution shift. In: 2023 IEEE/CVF Winter Conference on Applications of
Computer Vision. Institute of Electrical and Electronics Engineers; 2023.
doi:10.1109/wacv56688.2023.00278'
apa: 'Sinha, S., Gehler, P., Locatello, F., & Schiele, B. (2023). TeST: Test-time
Self-Training under distribution shift. In 2023 IEEE/CVF Winter Conference
on Applications of Computer Vision. Waikoloa, HI, United States: Institute
of Electrical and Electronics Engineers. https://doi.org/10.1109/wacv56688.2023.00278'
chicago: 'Sinha, Samarth, Peter Gehler, Francesco Locatello, and Bernt Schiele.
“TeST: Test-Time Self-Training under Distribution Shift.” In 2023 IEEE/CVF
Winter Conference on Applications of Computer Vision. Institute of Electrical
and Electronics Engineers, 2023. https://doi.org/10.1109/wacv56688.2023.00278.'
ieee: 'S. Sinha, P. Gehler, F. Locatello, and B. Schiele, “TeST: Test-time Self-Training
under distribution shift,” in 2023 IEEE/CVF Winter Conference on Applications
of Computer Vision, Waikoloa, HI, United States, 2023.'
ista: 'Sinha S, Gehler P, Locatello F, Schiele B. 2023. TeST: Test-time Self-Training
under distribution shift. 2023 IEEE/CVF Winter Conference on Applications of Computer
Vision. WACV: Winter Conference on Applications of Computer Vision.'
mla: 'Sinha, Samarth, et al. “TeST: Test-Time Self-Training under Distribution Shift.”
2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Institute
of Electrical and Electronics Engineers, 2023, doi:10.1109/wacv56688.2023.00278.'
short: S. Sinha, P. Gehler, F. Locatello, B. Schiele, in:, 2023 IEEE/CVF Winter
Conference on Applications of Computer Vision, Institute of Electrical and Electronics
Engineers, 2023.
conference:
end_date: 2023-01-07
location: Waikoloa, HI, United States
name: 'WACV: Winter Conference on Applications of Computer Vision'
start_date: 2023-01-02
date_created: 2023-08-21T12:11:38Z
date_published: 2023-02-06T00:00:00Z
date_updated: 2023-09-06T10:26:56Z
day: '06'
department:
- _id: FrLo
doi: 10.1109/wacv56688.2023.00278
extern: '1'
external_id:
arxiv:
- '2209.11459'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2209.11459
month: '02'
oa: 1
oa_version: Preprint
publication: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision
publication_identifier:
eissn:
- 2642-9381
isbn:
- '9781665493475'
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'TeST: Test-time Self-Training under distribution shift'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14256'
abstract:
- lang: eng
text: "Context. Space asteroseismology is revolutionizing our knowledge of the internal
structure and dynamics of stars. A breakthrough is ongoing with the recent discoveries
of signatures of strong magnetic fields in the core of red giant stars. The key
signature for such a detection is the asymmetry these fields induce in the frequency
splittings of observed dipolar mixed gravito-acoustic modes.\r\nAims. We investigate
the ability of the observed asymmetries of the frequency splittings of dipolar
mixed modes to constrain the geometrical properties of deep magnetic fields.\r\nMethods.
We used the powerful analytical Racah-Wigner algebra used in quantum mechanics
to characterize the geometrical couplings of dipolar mixed oscillation modes with
various realistically plausible topologies of fossil magnetic fields. We also
computed the induced perturbation of their frequencies.\r\nResults. First, in
the case of an oblique magnetic dipole, we provide the exact analytical expression
of the asymmetry as a function of the angle between the rotation and magnetic
axes. Its value provides a direct measure of this angle. Second, considering a
combination of axisymmetric dipolar and quadrupolar fields, we show how the asymmetry
is blind to the unraveling of the relative strength and sign of each component.
Finally, in the case of a given multipole, we show that a negative asymmetry is
a signature of non-axisymmetric topologies.\r\nConclusions. Asymmetries of dipolar
mixed modes provide a key bit of information on the geometrical topology of deep
fossil magnetic fields, but this is insufficient on its own. Asteroseismic constraints
should therefore be combined with spectropolarimetric observations and numerical
simulations, which aim to predict the more probable stable large-scale geometries."
acknowledgement: The authors are grateful to the referee for her/his detailed and
constructive report, which has allowed us to improve our article. S. M. acknowledges
support from the CNES GOLF-SOHO and PLATO grants at CEA/DAp and PNPS (CNRS/INSU).
We thank R. A. Garcia for fruitful discussions and suggestions.
article_number: L9
article_processing_charge: Yes (in subscription journal)
article_type: letter_note
author:
- first_name: S.
full_name: Mathis, S.
last_name: Mathis
- first_name: Lisa Annabelle
full_name: Bugnet, Lisa Annabelle
id: d9edb345-f866-11ec-9b37-d119b5234501
last_name: Bugnet
orcid: 0000-0003-0142-4000
citation:
ama: 'Mathis S, Bugnet LA. Asymmetries of frequency splittings of dipolar mixed
modes: A window on the topology of deep magnetic fields. Astronomy and Astrophysics.
2023;676. doi:10.1051/0004-6361/202346832'
apa: 'Mathis, S., & Bugnet, L. A. (2023). Asymmetries of frequency splittings
of dipolar mixed modes: A window on the topology of deep magnetic fields. Astronomy
and Astrophysics. EDP Sciences. https://doi.org/10.1051/0004-6361/202346832'
chicago: 'Mathis, S., and Lisa Annabelle Bugnet. “Asymmetries of Frequency Splittings
of Dipolar Mixed Modes: A Window on the Topology of Deep Magnetic Fields.” Astronomy
and Astrophysics. EDP Sciences, 2023. https://doi.org/10.1051/0004-6361/202346832.'
ieee: 'S. Mathis and L. A. Bugnet, “Asymmetries of frequency splittings of dipolar
mixed modes: A window on the topology of deep magnetic fields,” Astronomy and
Astrophysics, vol. 676. EDP Sciences, 2023.'
ista: 'Mathis S, Bugnet LA. 2023. Asymmetries of frequency splittings of dipolar
mixed modes: A window on the topology of deep magnetic fields. Astronomy and Astrophysics.
676, L9.'
mla: 'Mathis, S., and Lisa Annabelle Bugnet. “Asymmetries of Frequency Splittings
of Dipolar Mixed Modes: A Window on the Topology of Deep Magnetic Fields.” Astronomy
and Astrophysics, vol. 676, L9, EDP Sciences, 2023, doi:10.1051/0004-6361/202346832.'
short: S. Mathis, L.A. Bugnet, Astronomy and Astrophysics 676 (2023).
date_created: 2023-09-03T22:01:15Z
date_published: 2023-08-01T00:00:00Z
date_updated: 2023-09-06T11:05:58Z
day: '01'
ddc:
- '520'
department:
- _id: LiBu
doi: 10.1051/0004-6361/202346832
external_id:
arxiv:
- '2306.11587'
isi:
- '001046037700007'
file:
- access_level: open_access
checksum: 7b30d26fb2b7bcb5b5be1414950615f9
content_type: application/pdf
creator: dernst
date_created: 2023-09-06T07:13:19Z
date_updated: 2023-09-06T07:13:19Z
file_id: '14271'
file_name: 2023_AstronomyAstrophysics_Mathis.pdf
file_size: 458120
relation: main_file
success: 1
file_date_updated: 2023-09-06T07:13:19Z
has_accepted_license: '1'
intvolume: ' 676'
isi: 1
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
publication: Astronomy and Astrophysics
publication_identifier:
eissn:
- 1432-0746
issn:
- 0004-6361
publication_status: published
publisher: EDP Sciences
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Asymmetries of frequency splittings of dipolar mixed modes: A window on the
topology of deep magnetic fields'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 676
year: '2023'
...
---
_id: '14261'
abstract:
- lang: eng
text: In this work, a generalized, adapted Numerov implementation capable of determining
band structures of periodic quantum systems is outlined. Based on the input potential,
the presented approach numerically solves the Schrödinger equation in position
space at each momentum space point. Thus, in addition to the band structure, the
method inherently provides information about the state functions and probability
densities in position space at each momentum space point considered. The generalized,
adapted Numerov framework provided reliable estimates for a variety of increasingly
complex test suites in one, two, and three dimensions. The accuracy of the proposed
methodology was benchmarked against results obtained for the analytically solvable
Kronig-Penney model. Furthermore, the presented numerical solver was applied to
a model potential representing a 2D optical lattice being a challenging application
relevant, for example, in the field of quantum computing.
acknowledgement: Financial supports for this work via a PhD scholarship for J. Gamper
issued by the Leopold-Franzens-University of Innsbruck (Vicerector Prof. Dr Ulrike
Tanzer) are gratefully acknowledged. The computational results presented have been
achieved (in part) using the HPC infrastructure of the University of Innsbruck.
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Jakob
full_name: Gamper, Jakob
last_name: Gamper
- first_name: Florian
full_name: Kluibenschedl, Florian
id: 7499e70e-eb2c-11ec-b98b-f925648bc9d9
last_name: Kluibenschedl
- first_name: Alexander K.H.
full_name: Weiss, Alexander K.H.
last_name: Weiss
- first_name: Thomas S.
full_name: Hofer, Thomas S.
last_name: Hofer
citation:
ama: Gamper J, Kluibenschedl F, Weiss AKH, Hofer TS. Accessing position space wave
functions in band structure calculations of periodic systems - a generalized,
adapted numerov implementation for one-, two-, and three-dimensional quantum problems.
Journal of Physical Chemistry Letters. 2023;14(33):7395-7403. doi:10.1021/acs.jpclett.3c01707
apa: Gamper, J., Kluibenschedl, F., Weiss, A. K. H., & Hofer, T. S. (2023).
Accessing position space wave functions in band structure calculations of periodic
systems - a generalized, adapted numerov implementation for one-, two-, and three-dimensional
quantum problems. Journal of Physical Chemistry Letters. American Chemical
Society. https://doi.org/10.1021/acs.jpclett.3c01707
chicago: Gamper, Jakob, Florian Kluibenschedl, Alexander K.H. Weiss, and Thomas
S. Hofer. “Accessing Position Space Wave Functions in Band Structure Calculations
of Periodic Systems - a Generalized, Adapted Numerov Implementation for One-,
Two-, and Three-Dimensional Quantum Problems.” Journal of Physical Chemistry
Letters. American Chemical Society, 2023. https://doi.org/10.1021/acs.jpclett.3c01707.
ieee: J. Gamper, F. Kluibenschedl, A. K. H. Weiss, and T. S. Hofer, “Accessing position
space wave functions in band structure calculations of periodic systems - a generalized,
adapted numerov implementation for one-, two-, and three-dimensional quantum problems,”
Journal of Physical Chemistry Letters, vol. 14, no. 33. American Chemical
Society, pp. 7395–7403, 2023.
ista: Gamper J, Kluibenschedl F, Weiss AKH, Hofer TS. 2023. Accessing position space
wave functions in band structure calculations of periodic systems - a generalized,
adapted numerov implementation for one-, two-, and three-dimensional quantum problems.
Journal of Physical Chemistry Letters. 14(33), 7395–7403.
mla: Gamper, Jakob, et al. “Accessing Position Space Wave Functions in Band Structure
Calculations of Periodic Systems - a Generalized, Adapted Numerov Implementation
for One-, Two-, and Three-Dimensional Quantum Problems.” Journal of Physical
Chemistry Letters, vol. 14, no. 33, American Chemical Society, 2023, pp. 7395–403,
doi:10.1021/acs.jpclett.3c01707.
short: J. Gamper, F. Kluibenschedl, A.K.H. Weiss, T.S. Hofer, Journal of Physical
Chemistry Letters 14 (2023) 7395–7403.
date_created: 2023-09-03T22:01:16Z
date_published: 2023-08-11T00:00:00Z
date_updated: 2023-09-06T11:04:31Z
day: '11'
ddc:
- '530'
- '540'
department:
- _id: GradSch
doi: 10.1021/acs.jpclett.3c01707
external_id:
isi:
- '001048165800001'
pmid:
- '37566743'
file:
- access_level: open_access
checksum: 637454e2b3a357498d8d622d241c4bf6
content_type: application/pdf
creator: dernst
date_created: 2023-09-06T07:32:39Z
date_updated: 2023-09-06T07:32:39Z
file_id: '14272'
file_name: 2023_JourPhysChemistry_Gamper.pdf
file_size: 4986859
relation: main_file
success: 1
file_date_updated: 2023-09-06T07:32:39Z
has_accepted_license: '1'
intvolume: ' 14'
isi: 1
issue: '33'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
page: 7395-7403
pmid: 1
publication: Journal of Physical Chemistry Letters
publication_identifier:
eissn:
- 1948-7185
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Accessing position space wave functions in band structure calculations of periodic
systems - a generalized, adapted numerov implementation for one-, two-, and three-dimensional
quantum problems
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 14
year: '2023'
...
---
_id: '14208'
abstract:
- lang: eng
text: This paper focuses on over-parameterized deep neural networks (DNNs) with
ReLU activation functions and proves that when the data distribution is well-separated,
DNNs can achieve Bayes-optimal test error for classification while obtaining (nearly)
zero-training error under the lazy training regime. For this purpose, we unify
three interrelated concepts of overparameterization, benign overfitting, and the
Lipschitz constant of DNNs. Our results indicate that interpolating with smoother
functions leads to better generalization. Furthermore, we investigate the special
case where interpolating smooth ground-truth functions is performed by DNNs under
the Neural Tangent Kernel (NTK) regime for generalization. Our result demonstrates
that the generalization error converges to a constant order that only depends
on label noise and initialization noise, which theoretically verifies benign overfitting.
Our analysis provides a tight lower bound on the normalized margin under non-smooth
activation functions, as well as the minimum eigenvalue of NTK under high-dimensional
settings, which has its own interest in learning theory.
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Zhenyu
full_name: Zhu, Zhenyu
last_name: Zhu
- first_name: Fanghui
full_name: Liu, Fanghui
last_name: Liu
- first_name: Grigorios G
full_name: Chrysos, Grigorios G
last_name: Chrysos
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Volkan
full_name: Cevher, Volkan
last_name: Cevher
citation:
ama: 'Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. Benign overfitting in deep
neural networks under lazy training. In: Proceedings of the 40th International
Conference on Machine Learning. Vol 202. ML Research Press; 2023:43105-43128.'
apa: 'Zhu, Z., Liu, F., Chrysos, G. G., Locatello, F., & Cevher, V. (2023).
Benign overfitting in deep neural networks under lazy training. In Proceedings
of the 40th International Conference on Machine Learning (Vol. 202, pp. 43105–43128).
Honolulu, Hawaii, United States: ML Research Press.'
chicago: Zhu, Zhenyu, Fanghui Liu, Grigorios G Chrysos, Francesco Locatello, and
Volkan Cevher. “Benign Overfitting in Deep Neural Networks under Lazy Training.”
In Proceedings of the 40th International Conference on Machine Learning,
202:43105–28. ML Research Press, 2023.
ieee: Z. Zhu, F. Liu, G. G. Chrysos, F. Locatello, and V. Cevher, “Benign overfitting
in deep neural networks under lazy training,” in Proceedings of the 40th International
Conference on Machine Learning, Honolulu, Hawaii, United States, 2023, vol.
202, pp. 43105–43128.
ista: Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. 2023. Benign overfitting
in deep neural networks under lazy training. Proceedings of the 40th International
Conference on Machine Learning. International Conference on Machine Learning,
PMLR, vol. 202, 43105–43128.
mla: Zhu, Zhenyu, et al. “Benign Overfitting in Deep Neural Networks under Lazy
Training.” Proceedings of the 40th International Conference on Machine Learning,
vol. 202, ML Research Press, 2023, pp. 43105–28.
short: Z. Zhu, F. Liu, G.G. Chrysos, F. Locatello, V. Cevher, in:, Proceedings of
the 40th International Conference on Machine Learning, ML Research Press, 2023,
pp. 43105–43128.
conference:
end_date: 2023-07-29
location: Honolulu, Hawaii, United States
name: International Conference on Machine Learning
start_date: 2023-07-23
date_created: 2023-08-22T14:18:18Z
date_published: 2023-05-30T00:00:00Z
date_updated: 2023-09-13T08:46:46Z
day: '30'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2305.19377'
intvolume: ' 202'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2305.19377
month: '05'
oa: 1
oa_version: Preprint
page: 43105-43128
publication: Proceedings of the 40th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
status: public
title: Benign overfitting in deep neural networks under lazy training
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 202
year: '2023'
...
---
_id: '14209'
abstract:
- lang: eng
text: Diffusion models excel at generating photorealistic images from text-queries.
Naturally, many approaches have been proposed to use these generative abilities
to augment training datasets for downstream tasks, such as classification. However,
diffusion models are themselves trained on large noisily supervised, but nonetheless,
annotated datasets. It is an open question whether the generalization capabilities
of diffusion models beyond using the additional data of the pre-training process
for augmentation lead to improved downstream performance. We perform a systematic
evaluation of existing methods to generate images from diffusion models and study
new extensions to assess their benefit for data augmentation. While we find that
personalizing diffusion models towards the target data outperforms simpler prompting
strategies, we also show that using the training data of the diffusion model alone,
via a simple nearest neighbor retrieval procedure, leads to even stronger downstream
performance. Overall, our study probes the limitations of diffusion models for
data augmentation but also highlights its potential in generating new training
data to improve performance on simple downstream vision tasks.
article_number: '2304.10253'
article_processing_charge: No
author:
- first_name: Max F.
full_name: Burg, Max F.
last_name: Burg
- first_name: Florian
full_name: Wenzel, Florian
last_name: Wenzel
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Osama
full_name: Makansi, Osama
last_name: Makansi
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Chris
full_name: Russell, Chris
last_name: Russell
citation:
ama: Burg MF, Wenzel F, Zietlow D, et al. A data augmentation perspective on diffusion
models and retrieval. arXiv. doi:10.48550/arXiv.2304.10253
apa: Burg, M. F., Wenzel, F., Zietlow, D., Horn, M., Makansi, O., Locatello, F.,
& Russell, C. (n.d.). A data augmentation perspective on diffusion models
and retrieval. arXiv. https://doi.org/10.48550/arXiv.2304.10253
chicago: Burg, Max F., Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi,
Francesco Locatello, and Chris Russell. “A Data Augmentation Perspective on Diffusion
Models and Retrieval.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2304.10253.
ieee: M. F. Burg et al., “A data augmentation perspective on diffusion models
and retrieval,” arXiv. .
ista: Burg MF, Wenzel F, Zietlow D, Horn M, Makansi O, Locatello F, Russell C. A
data augmentation perspective on diffusion models and retrieval. arXiv, 2304.10253.
mla: Burg, Max F., et al. “A Data Augmentation Perspective on Diffusion Models and
Retrieval.” ArXiv, 2304.10253, doi:10.48550/arXiv.2304.10253.
short: M.F. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell,
ArXiv (n.d.).
date_created: 2023-08-22T14:18:43Z
date_published: 2023-04-20T00:00:00Z
date_updated: 2023-09-13T08:51:56Z
day: '20'
department:
- _id: FrLo
doi: 10.48550/arXiv.2304.10253
extern: '1'
external_id:
arxiv:
- '2304.10253'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2304.10253
month: '04'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: A data augmentation perspective on diffusion models and retrieval
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14211'
abstract:
- lang: eng
text: 'Causal discovery methods are intrinsically constrained by the set of assumptions
needed to ensure structure identifiability. Moreover additional restrictions are
often imposed in order to simplify the inference task: this is the case for the
Gaussian noise assumption on additive non-linear models, which is common to many
causal discovery approaches. In this paper we show the shortcomings of inference
under this hypothesis, analyzing the risk of edge inversion under violation of
Gaussianity of the noise terms. Then, we propose a novel method for inferring
the topological ordering of the variables in the causal graph, from data generated
according to an additive non-linear model with a generic noise distribution. This
leads to NoGAM (Not only Gaussian Additive noise Models), a causal discovery algorithm
with a minimal set of assumptions and state of the art performance, experimentally
benchmarked on synthetic data.'
article_processing_charge: No
author:
- first_name: Francesco
full_name: Montagna, Francesco
last_name: Montagna
- first_name: Nicoletta
full_name: Noceti, Nicoletta
last_name: Noceti
- first_name: Lorenzo
full_name: Rosasco, Lorenzo
last_name: Rosasco
- first_name: Kun
full_name: Zhang, Kun
last_name: Zhang
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Causal discovery with
score matching on additive models with arbitrary noise. In: 2nd Conference
on Causal Learning and Reasoning. ; 2023.'
apa: Montagna, F., Noceti, N., Rosasco, L., Zhang, K., & Locatello, F. (2023).
Causal discovery with score matching on additive models with arbitrary noise.
In 2nd Conference on Causal Learning and Reasoning. Tübingen, Germany.
chicago: Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and
Francesco Locatello. “Causal Discovery with Score Matching on Additive Models
with Arbitrary Noise.” In 2nd Conference on Causal Learning and Reasoning,
2023.
ieee: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Causal discovery
with score matching on additive models with arbitrary noise,” in 2nd Conference
on Causal Learning and Reasoning, Tübingen, Germany, 2023.
ista: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. 2023. Causal discovery
with score matching on additive models with arbitrary noise. 2nd Conference on
Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning.'
mla: Montagna, Francesco, et al. “Causal Discovery with Score Matching on Additive
Models with Arbitrary Noise.” 2nd Conference on Causal Learning and Reasoning,
2023.
short: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference
on Causal Learning and Reasoning, 2023.
conference:
end_date: 2023-04-14
location: Tübingen, Germany
name: 'CLeaR: Conference on Causal Learning and Reasoning'
start_date: 2023-04-11
date_created: 2023-08-22T14:19:21Z
date_published: 2023-04-01T00:00:00Z
date_updated: 2023-09-13T09:00:31Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2304.03265'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2304.03265
month: '04'
oa: 1
oa_version: Preprint
publication: 2nd Conference on Causal Learning and Reasoning
publication_status: published
quality_controlled: '1'
scopus_import: '1'
status: public
title: Causal discovery with score matching on additive models with arbitrary noise
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14212'
abstract:
- lang: eng
text: This paper demonstrates how to discover the whole causal graph from the second
derivative of the log-likelihood in observational non-linear additive Gaussian
noise models. Leveraging scalable machine learning approaches to approximate the
score function ∇logp(X), we extend the work of Rolland et al. (2022) that only
recovers the topological order from the score and requires an expensive pruning
step removing spurious edges among those admitted by the ordering. Our analysis
leads to DAS (acronym for Discovery At Scale), a practical algorithm that reduces
the complexity of the pruning by a factor proportional to the graph size. In practice,
DAS achieves competitive accuracy with current state-of-the-art while being over
an order of magnitude faster. Overall, our approach enables principled and scalable
causal discovery, significantly lowering the compute bar.
article_processing_charge: No
author:
- first_name: Francesco
full_name: Montagna, Francesco
last_name: Montagna
- first_name: Nicoletta
full_name: Noceti, Nicoletta
last_name: Noceti
- first_name: Lorenzo
full_name: Rosasco, Lorenzo
last_name: Rosasco
- first_name: Kun
full_name: Zhang, Kun
last_name: Zhang
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Scalable causal discovery
with score matching. In: 2nd Conference on Causal Learning and Reasoning.
; 2023.'
apa: Montagna, F., Noceti, N., Rosasco, L., Zhang, K., & Locatello, F. (2023).
Scalable causal discovery with score matching. In 2nd Conference on Causal
Learning and Reasoning. Tübingen, Germany.
chicago: Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and
Francesco Locatello. “Scalable Causal Discovery with Score Matching.” In 2nd
Conference on Causal Learning and Reasoning, 2023.
ieee: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Scalable
causal discovery with score matching,” in 2nd Conference on Causal Learning
and Reasoning, Tübingen, Germany, 2023.
ista: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. 2023. Scalable causal
discovery with score matching. 2nd Conference on Causal Learning and Reasoning.
CLeaR: Conference on Causal Learning and Reasoning.'
mla: Montagna, Francesco, et al. “Scalable Causal Discovery with Score Matching.”
2nd Conference on Causal Learning and Reasoning, 2023.
short: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference
on Causal Learning and Reasoning, 2023.
conference:
end_date: 2023-04-14
location: Tübingen, Germany
name: 'CLeaR: Conference on Causal Learning and Reasoning'
start_date: 2023-04-11
date_created: 2023-08-22T14:19:40Z
date_published: 2023-04-01T00:00:00Z
date_updated: 2023-09-13T09:03:24Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2304.03382'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2304.03382
month: '04'
oa: 1
oa_version: Preprint
publication: 2nd Conference on Causal Learning and Reasoning
publication_status: published
quality_controlled: '1'
scopus_import: '1'
status: public
title: Scalable causal discovery with score matching
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14214'
abstract:
- lang: eng
text: 'Recent years have seen a surge of interest in learning high-level causal
representations from low-level image pairs under interventions. Yet, existing
efforts are largely limited to simple synthetic settings that are far away from
real-world problems. In this paper, we present Causal Triplet, a causal representation
learning benchmark featuring not only visually more complex scenes, but also two
crucial desiderata commonly overlooked in previous works: (i) an actionable counterfactual
setting, where only certain object-level variables allow for counterfactual observations
whereas others do not; (ii) an interventional downstream task with an emphasis
on out-of-distribution robustness from the independent causal mechanisms principle.
Through extensive experiments, we find that models built with the knowledge of
disentangled or object-centric representations significantly outperform their
distributed counterparts. However, recent causal representation learning methods
still struggle to identify such latent structures, indicating substantial challenges
and opportunities for future work.'
article_processing_charge: No
author:
- first_name: Yuejiang
full_name: Liu, Yuejiang
last_name: Liu
- first_name: Alexandre
full_name: Alahi, Alexandre
last_name: Alahi
- first_name: Chris
full_name: Russell, Chris
last_name: Russell
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Liu Y, Alahi A, Russell C, et al. Causal triplet: An open challenge for intervention-centric
causal representation learning. In: 2nd Conference on Causal Learning and Reasoning.
; 2023.'
apa: 'Liu, Y., Alahi, A., Russell, C., Horn, M., Zietlow, D., Schölkopf, B., &
Locatello, F. (2023). Causal triplet: An open challenge for intervention-centric
causal representation learning. In 2nd Conference on Causal Learning and Reasoning.
Tübingen, Germany.'
chicago: 'Liu, Yuejiang, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow,
Bernhard Schölkopf, and Francesco Locatello. “Causal Triplet: An Open Challenge
for Intervention-Centric Causal Representation Learning.” In 2nd Conference
on Causal Learning and Reasoning, 2023.'
ieee: 'Y. Liu et al., “Causal triplet: An open challenge for intervention-centric
causal representation learning,” in 2nd Conference on Causal Learning and Reasoning,
Tübingen, Germany, 2023.'
ista: 'Liu Y, Alahi A, Russell C, Horn M, Zietlow D, Schölkopf B, Locatello F. 2023.
Causal triplet: An open challenge for intervention-centric causal representation
learning. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on
Causal Learning and Reasoning.'
mla: 'Liu, Yuejiang, et al. “Causal Triplet: An Open Challenge for Intervention-Centric
Causal Representation Learning.” 2nd Conference on Causal Learning and Reasoning,
2023.'
short: Y. Liu, A. Alahi, C. Russell, M. Horn, D. Zietlow, B. Schölkopf, F. Locatello,
in:, 2nd Conference on Causal Learning and Reasoning, 2023.
conference:
end_date: 2023-04-14
location: Tübingen, Germany
name: 'CLeaR: Conference on Causal Learning and Reasoning'
start_date: 2023-04-11
date_created: 2023-08-22T14:20:18Z
date_published: 2023-04-12T00:00:00Z
date_updated: 2023-09-13T09:23:08Z
day: '12'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2301.05169'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2301.05169
month: '04'
oa: 1
oa_version: Preprint
publication: 2nd Conference on Causal Learning and Reasoning
publication_status: published
quality_controlled: '1'
status: public
title: 'Causal triplet: An open challenge for intervention-centric causal representation
learning'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14217'
abstract:
- lang: eng
text: 'Neural networks embed the geometric structure of a data manifold lying in
a high-dimensional space into latent representations. Ideally, the distribution
of the data points in the latent space should depend only on the task, the data,
the loss, and other architecture-specific constraints. However, factors such as
the random weights initialization, training hyperparameters, or other sources
of randomness in the training phase may induce incoherent latent spaces that hinder
any form of reuse. Nevertheless, we empirically observe that, under the same data
and modeling choices, the angles between the encodings within distinct latent
spaces do not change. In this work, we propose the latent similarity between each
sample and a fixed set of anchors as an alternative data representation, demonstrating
that it can enforce the desired invariances without any additional training. We
show how neural architectures can leverage these relative representations to guarantee,
in practice, invariance to latent isometries and rescalings, effectively enabling
latent space communication: from zero-shot model stitching to latent space comparison
between diverse settings. We extensively validate the generalization capability
of our approach on different datasets, spanning various modalities (images, text,
graphs), tasks (e.g., classification, reconstruction) and architectures (e.g.,
CNNs, GCNs, transformers).'
article_processing_charge: No
author:
- first_name: Luca
full_name: Moschella, Luca
last_name: Moschella
- first_name: Valentino
full_name: Maiorca, Valentino
last_name: Maiorca
- first_name: Marco
full_name: Fumero, Marco
last_name: Fumero
- first_name: Antonio
full_name: Norelli, Antonio
last_name: Norelli
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Emanuele
full_name: Rodolà, Emanuele
last_name: Rodolà
citation:
ama: 'Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. Relative
representations enable zero-shot latent space communication. In: The 11th International
Conference on Learning Representations. ; 2023.'
apa: Moschella, L., Maiorca, V., Fumero, M., Norelli, A., Locatello, F., & Rodolà,
E. (2023). Relative representations enable zero-shot latent space communication.
In The 11th International Conference on Learning Representations. Kigali,
Rwanda.
chicago: Moschella, Luca, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco
Locatello, and Emanuele Rodolà. “Relative Representations Enable Zero-Shot Latent
Space Communication.” In The 11th International Conference on Learning Representations,
2023.
ieee: L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, and E. Rodolà,
“Relative representations enable zero-shot latent space communication,” in The
11th International Conference on Learning Representations, Kigali, Rwanda,
2023.
ista: Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. 2023.
Relative representations enable zero-shot latent space communication. The 11th
International Conference on Learning Representations. International Conference
on Machine Learning Representations.
mla: Moschella, Luca, et al. “Relative Representations Enable Zero-Shot Latent Space
Communication.” The 11th International Conference on Learning Representations,
2023.
short: L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, E. Rodolà,
in:, The 11th International Conference on Learning Representations, 2023.
conference:
end_date: 2023-05-05
location: Kigali, Rwanda
name: International Conference on Machine Learning Representations
start_date: 2023-05-01
date_created: 2023-08-22T14:22:20Z
date_published: 2023-05-01T00:00:00Z
date_updated: 2023-09-13T09:44:26Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2209.15430'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2209.15430
month: '05'
oa: 1
oa_version: Preprint
publication: The 11th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: Relative representations enable zero-shot latent space communication
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14222'
abstract:
- lang: eng
text: Learning generative object models from unlabelled videos is a long standing
problem and required for causal scene modeling. We decompose this problem into
three easier subtasks, and provide candidate solutions for each of them. Inspired
by the Common Fate Principle of Gestalt Psychology, we first extract (noisy) masks
of moving objects via unsupervised motion segmentation. Second, generative models
are trained on the masks of the background and the moving objects, respectively.
Third, background and foreground models are combined in a conditional "dead leaves"
scene model to sample novel scene configurations where occlusions and depth layering
arise naturally. To evaluate the individual stages, we introduce the Fishbowl
dataset positioned between complex real-world scenes and common object-centric
benchmarks of simplistic objects. We show that our approach allows learning generative
models that generalize beyond the occlusions present in the input videos, and
represent scenes in a modular fashion that allows sampling plausible scenes outside
the training distribution by permitting, for instance, object numbers or densities
not observed in the training set.
article_number: '2110.06562'
article_processing_charge: No
author:
- first_name: Matthias
full_name: Tangemann, Matthias
last_name: Tangemann
- first_name: Steffen
full_name: Schneider, Steffen
last_name: Schneider
- first_name: Julius von
full_name: Kügelgen, Julius von
last_name: Kügelgen
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
- first_name: Matthias
full_name: Kümmerer, Matthias
last_name: Kümmerer
- first_name: Matthias
full_name: Bethge, Matthias
last_name: Bethge
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
citation:
ama: 'Tangemann M, Schneider S, Kügelgen J von, et al. Unsupervised object learning
via common fate. In: 2nd Conference on Causal Learning and Reasoning. ;
2023.'
apa: Tangemann, M., Schneider, S., Kügelgen, J. von, Locatello, F., Gehler, P.,
Brox, T., … Schölkopf, B. (2023). Unsupervised object learning via common fate.
In 2nd Conference on Causal Learning and Reasoning. Tübingen, Germany.
chicago: Tangemann, Matthias, Steffen Schneider, Julius von Kügelgen, Francesco
Locatello, Peter Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, and
Bernhard Schölkopf. “Unsupervised Object Learning via Common Fate.” In 2nd
Conference on Causal Learning and Reasoning, 2023.
ieee: M. Tangemann et al., “Unsupervised object learning via common fate,”
in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany,
2023.
ista: 'Tangemann M, Schneider S, Kügelgen J von, Locatello F, Gehler P, Brox T,
Kümmerer M, Bethge M, Schölkopf B. 2023. Unsupervised object learning via common
fate. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal
Learning and Reasoning, 2110.06562.'
mla: Tangemann, Matthias, et al. “Unsupervised Object Learning via Common Fate.”
2nd Conference on Causal Learning and Reasoning, 2110.06562, 2023.
short: M. Tangemann, S. Schneider, J. von Kügelgen, F. Locatello, P. Gehler, T.
Brox, M. Kümmerer, M. Bethge, B. Schölkopf, in:, 2nd Conference on Causal Learning
and Reasoning, 2023.
conference:
end_date: 2023-04-14
location: Tübingen, Germany
name: 'CLeaR: Conference on Causal Learning and Reasoning'
start_date: 2023-04-11
date_created: 2023-08-22T14:23:54Z
date_published: 2023-04-15T00:00:00Z
date_updated: 2023-09-13T11:31:14Z
day: '15'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2110.06562'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2110.06562
month: '04'
oa: 1
oa_version: Preprint
publication: 2nd Conference on Causal Learning and Reasoning
publication_status: published
quality_controlled: '1'
status: public
title: Unsupervised object learning via common fate
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14218'
abstract:
- lang: eng
text: Humans naturally decompose their environment into entities at the appropriate
level of abstraction to act in the world. Allowing machine learning algorithms
to derive this decomposition in an unsupervised way has become an important line
of research. However, current methods are restricted to simulated data or require
additional information in the form of motion or depth in order to successfully
discover objects. In this work, we overcome this limitation by showing that reconstructing
features from models trained in a self-supervised manner is a sufficient training
signal for object-centric representations to arise in a fully unsupervised way.
Our approach, DINOSAUR, significantly out-performs existing image-based object-centric
learning models on simulated data and is the first unsupervised object-centric
model that scales to real-world datasets such as COCO and PASCAL VOC. DINOSAUR
is conceptually simple and shows competitive performance compared to more involved
pipelines from the computer vision literature.
article_processing_charge: No
author:
- first_name: Maximilian
full_name: Seitzer, Maximilian
last_name: Seitzer
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Andrii
full_name: Zadaianchuk, Andrii
last_name: Zadaianchuk
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Tianjun
full_name: Xiao, Tianjun
last_name: Xiao
- first_name: Carl-Johann Simon-Gabriel
full_name: Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel
last_name: Carl-Johann Simon-Gabriel
- first_name: Tong
full_name: He, Tong
last_name: He
- first_name: Zheng
full_name: Zhang, Zheng
last_name: Zhang
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Seitzer M, Horn M, Zadaianchuk A, et al. Bridging the gap to real-world object-centric
learning. In: The 11th International Conference on Learning Representations.
; 2023.'
apa: Seitzer, M., Horn, M., Zadaianchuk, A., Zietlow, D., Xiao, T., Carl-Johann
Simon-Gabriel, C.-J. S.-G., … Locatello, F. (2023). Bridging the gap to real-world
object-centric learning. In The 11th International Conference on Learning Representations.
Kigali, Rwanda.
chicago: Seitzer, Maximilian, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun
Xiao, Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, Tong He, et al. “Bridging
the Gap to Real-World Object-Centric Learning.” In The 11th International Conference
on Learning Representations, 2023.
ieee: M. Seitzer et al., “Bridging the gap to real-world object-centric learning,”
in The 11th International Conference on Learning Representations, Kigali,
Rwanda, 2023.
ista: 'Seitzer M, Horn M, Zadaianchuk A, Zietlow D, Xiao T, Carl-Johann Simon-Gabriel
C-JS-G, He T, Zhang Z, Schölkopf B, Brox T, Locatello F. 2023. Bridging the gap
to real-world object-centric learning. The 11th International Conference on Learning
Representations. ICLR: International Conference on Learning Representations.'
mla: Seitzer, Maximilian, et al. “Bridging the Gap to Real-World Object-Centric
Learning.” The 11th International Conference on Learning Representations,
2023.
short: M. Seitzer, M. Horn, A. Zadaianchuk, D. Zietlow, T. Xiao, C.-J.S.-G. Carl-Johann
Simon-Gabriel, T. He, Z. Zhang, B. Schölkopf, T. Brox, F. Locatello, in:, The
11th International Conference on Learning Representations, 2023.
conference:
end_date: 2023-05-05
location: Kigali, Rwanda
name: 'ICLR: International Conference on Learning Representations'
start_date: 2023-05-01
date_created: 2023-08-22T14:22:41Z
date_published: 2023-05-10T00:00:00Z
date_updated: 2023-09-13T11:37:03Z
day: '10'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2209.14860'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2209.14860
month: '05'
oa: 1
oa_version: Preprint
publication: The 11th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: Bridging the gap to real-world object-centric learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14219'
abstract:
- lang: eng
text: "In this paper, we show that recent advances in self-supervised feature\r\nlearning
enable unsupervised object discovery and semantic segmentation with a\r\nperformance
that matches the state of the field on supervised semantic\r\nsegmentation 10
years ago. We propose a methodology based on unsupervised\r\nsaliency masks and
self-supervised feature clustering to kickstart object\r\ndiscovery followed by
training a semantic segmentation network on pseudo-labels\r\nto bootstrap the
system on images with multiple objects. We present results on\r\nPASCAL VOC that
go far beyond the current state of the art (50.0 mIoU), and we\r\nreport for the
first time results on MS COCO for the whole set of 81 classes:\r\nour method discovers
34 categories with more than $20\\%$ IoU, while obtaining\r\nan average IoU of
19.6 for all 81 categories."
article_processing_charge: No
author:
- first_name: Andrii
full_name: Zadaianchuk, Andrii
last_name: Zadaianchuk
- first_name: Matthaeus
full_name: Kleindessner, Matthaeus
last_name: Kleindessner
- first_name: Yi
full_name: Zhu, Yi
last_name: Zhu
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
citation:
ama: 'Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. Unsupervised semantic
segmentation with self-supervised object-centric representations. In: The 11th
International Conference on Learning Representations. ; 2023.'
apa: Zadaianchuk, A., Kleindessner, M., Zhu, Y., Locatello, F., & Brox, T. (2023).
Unsupervised semantic segmentation with self-supervised object-centric representations.
In The 11th International Conference on Learning Representations. Kigali,
Rwanda.
chicago: Zadaianchuk, Andrii, Matthaeus Kleindessner, Yi Zhu, Francesco Locatello,
and Thomas Brox. “Unsupervised Semantic Segmentation with Self-Supervised Object-Centric
Representations.” In The 11th International Conference on Learning Representations,
2023.
ieee: A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, and T. Brox, “Unsupervised
semantic segmentation with self-supervised object-centric representations,” in
The 11th International Conference on Learning Representations, Kigali,
Rwanda, 2023.
ista: 'Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. 2023. Unsupervised
semantic segmentation with self-supervised object-centric representations. The
11th International Conference on Learning Representations. ICLR: International
Conference on Learning Representations.'
mla: Zadaianchuk, Andrii, et al. “Unsupervised Semantic Segmentation with Self-Supervised
Object-Centric Representations.” The 11th International Conference on Learning
Representations, 2023.
short: A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, T. Brox, in:, The
11th International Conference on Learning Representations, 2023.
conference:
end_date: 2023-05-05
location: Kigali, Rwanda
name: 'ICLR: International Conference on Learning Representations'
start_date: 2023-05-01
date_created: 2023-08-22T14:22:58Z
date_published: 2023-05-01T00:00:00Z
date_updated: 2023-09-13T11:25:43Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2207.05027'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2207.05027
month: '05'
oa: 1
oa_version: Preprint
publication: The 11th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: Unsupervised semantic segmentation with self-supervised object-centric representations
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14333'
abstract:
- lang: eng
text: "As causal ground truth is incredibly rare, causal discovery algorithms are\r\ncommonly
only evaluated on simulated data. This is concerning, given that\r\nsimulations
reflect common preconceptions about generating processes regarding\r\nnoise distributions,
model classes, and more. In this work, we propose a novel\r\nmethod for falsifying
the output of a causal discovery algorithm in the absence\r\nof ground truth.
Our key insight is that while statistical learning seeks\r\nstability across subsets
of data points, causal learning should seek stability\r\nacross subsets of variables.
Motivated by this insight, our method relies on a\r\nnotion of compatibility between
causal graphs learned on different subsets of\r\nvariables. We prove that detecting
incompatibilities can falsify wrongly\r\ninferred causal relations due to violation
of assumptions or errors from finite\r\nsample effects. Although passing such
compatibility tests is only a necessary\r\ncriterion for good performance, we
argue that it provides strong evidence for\r\nthe causal models whenever compatibility
entails strong implications for the\r\njoint distribution. We also demonstrate
experimentally that detection of\r\nincompatibilities can aid in causal model
selection."
article_number: '2307.09552'
article_processing_charge: No
author:
- first_name: Philipp M.
full_name: Faller, Philipp M.
last_name: Faller
- first_name: Leena Chennuru
full_name: Vankadara, Leena Chennuru
last_name: Vankadara
- first_name: Atalanti A.
full_name: Mastakouri, Atalanti A.
last_name: Mastakouri
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Dominik
full_name: Janzing, Dominik
last_name: Janzing
citation:
ama: 'Faller PM, Vankadara LC, Mastakouri AA, Locatello F, Janzing D. Self-compatibility:
Evaluating causal discovery without ground truth. arXiv. doi:10.48550/arXiv.2307.09552'
apa: 'Faller, P. M., Vankadara, L. C., Mastakouri, A. A., Locatello, F., & Janzing,
D. (n.d.). Self-compatibility: Evaluating causal discovery without ground truth.
arXiv. https://doi.org/10.48550/arXiv.2307.09552'
chicago: 'Faller, Philipp M., Leena Chennuru Vankadara, Atalanti A. Mastakouri,
Francesco Locatello, and Dominik Janzing. “Self-Compatibility: Evaluating Causal
Discovery without Ground Truth.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2307.09552.'
ieee: 'P. M. Faller, L. C. Vankadara, A. A. Mastakouri, F. Locatello, and D. Janzing,
“Self-compatibility: Evaluating causal discovery without ground truth,” arXiv.
.'
ista: 'Faller PM, Vankadara LC, Mastakouri AA, Locatello F, Janzing D. Self-compatibility:
Evaluating causal discovery without ground truth. arXiv, 2307.09552.'
mla: 'Faller, Philipp M., et al. “Self-Compatibility: Evaluating Causal Discovery
without Ground Truth.” ArXiv, 2307.09552, doi:10.48550/arXiv.2307.09552.'
short: P.M. Faller, L.C. Vankadara, A.A. Mastakouri, F. Locatello, D. Janzing, ArXiv
(n.d.).
date_created: 2023-09-13T12:44:59Z
date_published: 2023-07-18T00:00:00Z
date_updated: 2023-09-13T12:47:53Z
day: '18'
department:
- _id: FrLo
doi: 10.48550/arXiv.2307.09552
extern: '1'
external_id:
arxiv:
- '2307.09552'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2307.09552
month: '07'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: 'Self-compatibility: Evaluating causal discovery without ground truth'
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14277'
abstract:
- lang: eng
text: Living tissues are characterized by an intrinsically mechanochemical interplay
of active physical forces and complex biochemical signaling pathways. Either feature
alone can give rise to complex emergent phenomena, for example, mechanically driven
glassy dynamics and rigidity transitions, or chemically driven reaction-diffusion
instabilities. An important question is how to quantitatively assess the contribution
of these different cues to the large-scale dynamics of biological materials. We
address this in Madin-Darby canine kidney (MDCK) monolayers, considering both
mechanochemical feedback between extracellular signal-regulated kinase (ERK) signaling
activity and cellular density as well as a mechanically active tissue rheology
via a self-propelled vertex model. We show that the relative strength of active
migration forces to mechanochemical couplings controls a transition from a uniform
active glass to periodic spatiotemporal waves. We parametrize the model from published
experimental data sets on MDCK monolayers and use it to make new predictions on
the correlation functions of cellular dynamics and the dynamics of topological
defects associated with the oscillatory phase of cells. Interestingly, MDCK monolayers
are best described by an intermediary parameter region in which both mechanochemical
couplings and noisy active propulsion have a strong influence on the dynamics.
Finally, we study how tissue rheology and ERK waves produce feedback on one another
and uncover a mechanism via which tissue fluidity can be controlled by mechanochemical
waves at both the local and global levels.
acknowledgement: We thank all members of the Hannezo group for discussions and suggestions,
as well as Sound Wai Phow for technical assistance. This work received funding from
the European Research Council under the EU Horizon 2020 research and innovation
program Grant Agreement No. 851288 (E.H.), JSPS KAKENHI Grant No. 21H05290, and
the Ministry of Education under the Research Centres of Excellence program through
the MBI at NUS.
article_number: '013001'
article_processing_charge: Yes
article_type: original
author:
- first_name: Daniel R
full_name: Boocock, Daniel R
id: 453AF628-F248-11E8-B48F-1D18A9856A87
last_name: Boocock
orcid: 0000-0002-1585-2631
- first_name: Tsuyoshi
full_name: Hirashima, Tsuyoshi
last_name: Hirashima
- first_name: Edouard B
full_name: Hannezo, Edouard B
id: 3A9DB764-F248-11E8-B48F-1D18A9856A87
last_name: Hannezo
orcid: 0000-0001-6005-1561
citation:
ama: Boocock DR, Hirashima T, Hannezo EB. Interplay between mechanochemical patterning
and glassy dynamics in cellular monolayers. PRX Life. 2023;1(1). doi:10.1103/prxlife.1.013001
apa: Boocock, D. R., Hirashima, T., & Hannezo, E. B. (2023). Interplay between
mechanochemical patterning and glassy dynamics in cellular monolayers. PRX
Life. American Physical Society. https://doi.org/10.1103/prxlife.1.013001
chicago: Boocock, Daniel R, Tsuyoshi Hirashima, and Edouard B Hannezo. “Interplay
between Mechanochemical Patterning and Glassy Dynamics in Cellular Monolayers.”
PRX Life. American Physical Society, 2023. https://doi.org/10.1103/prxlife.1.013001.
ieee: D. R. Boocock, T. Hirashima, and E. B. Hannezo, “Interplay between mechanochemical
patterning and glassy dynamics in cellular monolayers,” PRX Life, vol.
1, no. 1. American Physical Society, 2023.
ista: Boocock DR, Hirashima T, Hannezo EB. 2023. Interplay between mechanochemical
patterning and glassy dynamics in cellular monolayers. PRX Life. 1(1), 013001.
mla: Boocock, Daniel R., et al. “Interplay between Mechanochemical Patterning and
Glassy Dynamics in Cellular Monolayers.” PRX Life, vol. 1, no. 1, 013001,
American Physical Society, 2023, doi:10.1103/prxlife.1.013001.
short: D.R. Boocock, T. Hirashima, E.B. Hannezo, PRX Life 1 (2023).
date_created: 2023-09-06T08:30:59Z
date_published: 2023-07-20T00:00:00Z
date_updated: 2023-09-15T06:39:17Z
day: '20'
ddc:
- '570'
department:
- _id: EdHa
doi: 10.1103/prxlife.1.013001
ec_funded: 1
file:
- access_level: open_access
checksum: f881d98c89eb9f1aa136d7b781511553
content_type: application/pdf
creator: dernst
date_created: 2023-09-15T06:30:50Z
date_updated: 2023-09-15T06:30:50Z
file_id: '14335'
file_name: 2023_PRXLife_Boocock.pdf
file_size: 2559520
relation: main_file
success: 1
file_date_updated: 2023-09-15T06:30:50Z
has_accepted_license: '1'
intvolume: ' 1'
issue: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
project:
- _id: 05943252-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '851288'
name: Design Principles of Branching Morphogenesis
publication: PRX Life
publication_identifier:
issn:
- 2835-8279
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
status: public
title: Interplay between mechanochemical patterning and glassy dynamics in cellular
monolayers
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 1
year: '2023'
...
---
_id: '14314'
abstract:
- lang: eng
text: The execution of cognitive functions requires coordinated circuit activity
across different brain areas that involves the associated firing of neuronal assemblies.
Here, we tested the circuit mechanism behind assembly interactions between the
hippocampus and the medial prefrontal cortex (mPFC) of adult rats by recording
neuronal populations during a rule-switching task. We identified functionally
coupled CA1-mPFC cells that synchronized their activity beyond that expected from
common spatial coding or oscillatory firing. When such cell pairs fired together,
the mPFC cell strongly phase locked to CA1 theta oscillations and maintained consistent
theta firing phases, independent of the theta timing of their CA1 counterpart.
These functionally connected CA1-mPFC cells formed interconnected assemblies.
While firing together with their CA1 assembly partners, mPFC cells fired along
specific theta sequences. Our results suggest that upregulated theta oscillatory
firing of mPFC cells can signal transient interactions with specific CA1 assemblies,
thus enabling distributed computations.
acknowledgement: We thank A. Cumpelik, H. Chiossi, and L. Bollman for comments on
an earlier version of this manuscript. This work was funded by EU-FP7 MC-ITN IN-SENS
(grant 607616).
article_number: '113015'
article_processing_charge: Yes
article_type: original
author:
- first_name: Michele
full_name: Nardin, Michele
id: 30BD0376-F248-11E8-B48F-1D18A9856A87
last_name: Nardin
orcid: 0000-0001-8849-6570
- first_name: Karola
full_name: Käfer, Karola
id: 2DAA49AA-F248-11E8-B48F-1D18A9856A87
last_name: Käfer
- first_name: Federico
full_name: Stella, Federico
id: 39AF1E74-F248-11E8-B48F-1D18A9856A87
last_name: Stella
orcid: 0000-0001-9439-3148
- first_name: Jozsef L
full_name: Csicsvari, Jozsef L
id: 3FA14672-F248-11E8-B48F-1D18A9856A87
last_name: Csicsvari
orcid: 0000-0002-5193-4036
citation:
ama: Nardin M, Käfer K, Stella F, Csicsvari JL. Theta oscillations as a substrate
for medial prefrontal-hippocampal assembly interactions. Cell Reports.
2023;42(9). doi:10.1016/j.celrep.2023.113015
apa: Nardin, M., Käfer, K., Stella, F., & Csicsvari, J. L. (2023). Theta oscillations
as a substrate for medial prefrontal-hippocampal assembly interactions. Cell
Reports. Elsevier. https://doi.org/10.1016/j.celrep.2023.113015
chicago: Nardin, Michele, Karola Käfer, Federico Stella, and Jozsef L Csicsvari.
“Theta Oscillations as a Substrate for Medial Prefrontal-Hippocampal Assembly
Interactions.” Cell Reports. Elsevier, 2023. https://doi.org/10.1016/j.celrep.2023.113015.
ieee: M. Nardin, K. Käfer, F. Stella, and J. L. Csicsvari, “Theta oscillations as
a substrate for medial prefrontal-hippocampal assembly interactions,” Cell
Reports, vol. 42, no. 9. Elsevier, 2023.
ista: Nardin M, Käfer K, Stella F, Csicsvari JL. 2023. Theta oscillations as a substrate
for medial prefrontal-hippocampal assembly interactions. Cell Reports. 42(9),
113015.
mla: Nardin, Michele, et al. “Theta Oscillations as a Substrate for Medial Prefrontal-Hippocampal
Assembly Interactions.” Cell Reports, vol. 42, no. 9, 113015, Elsevier,
2023, doi:10.1016/j.celrep.2023.113015.
short: M. Nardin, K. Käfer, F. Stella, J.L. Csicsvari, Cell Reports 42 (2023).
date_created: 2023-09-10T22:01:11Z
date_published: 2023-09-26T00:00:00Z
date_updated: 2023-09-15T07:14:12Z
day: '26'
ddc:
- '570'
department:
- _id: JoCs
doi: 10.1016/j.celrep.2023.113015
ec_funded: 1
external_id:
pmid:
- '37632747'
file:
- access_level: open_access
checksum: ca77a304fb813c292550b8604b0fb41d
content_type: application/pdf
creator: dernst
date_created: 2023-09-15T07:12:46Z
date_updated: 2023-09-15T07:12:46Z
file_id: '14337'
file_name: 2023_CellPress_Nardin.pdf
file_size: 4879455
relation: main_file
success: 1
file_date_updated: 2023-09-15T07:12:46Z
has_accepted_license: '1'
intvolume: ' 42'
issue: '9'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 257BBB4C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '607616'
name: Inter-and intracellular signalling in schizophrenia
publication: Cell Reports
publication_identifier:
eissn:
- 2211-1247
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Theta oscillations as a substrate for medial prefrontal-hippocampal assembly
interactions
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 42
year: '2023'
...
---
_id: '14315'
abstract:
- lang: eng
text: During apoptosis, caspases degrade 8 out of ~30 nucleoporins to irreversibly
demolish the nuclear pore complex. However, for poorly understood reasons, caspases
are also activated during cell differentiation. Here, we show that sublethal activation
of caspases during myogenesis results in the transient proteolysis of four peripheral
Nups and one transmembrane Nup. ‘Trimmed’ NPCs become nuclear export-defective,
and we identified in an unbiased manner several classes of cytoplasmic, plasma
membrane, and mitochondrial proteins that rapidly accumulate in the nucleus. NPC
trimming by non-apoptotic caspases was also observed in neurogenesis and endoplasmic
reticulum stress. Our results suggest that caspases can reversibly modulate nuclear
transport activity, which allows them to function as agents of cell differentiation
and adaptation at sublethal levels.
acknowledgement: 'We thank the members of the Hetzer laboratory, Tony Hunter (Salk),
Lorenzo Puri (Sanford Burnham Prebys), and Jongmin Kim (Massachusetts General Hospital)
for the critical reading of the manuscript; Kenneth Diffenderfer and Aimee Pankonin
(Stem Cell Core at the Salk Institute) for help with neurogenesis; Carol Marchetto
and Fred Gage (Salk) for providing H9 embryonic stem cells; Lorenzo Puri, Alexandra
Sacco, and Luca Caputo (Sanford Burnham Prebys) for helpful discussions and sharing
mouse primary myoblasts. This work was supported by a Glenn Foundation for Medical
Research Postdoctoral Fellowship in Aging Research (UHC), the NOMIS foundation (MWH),
and the National Institutes of Health (R01 NS096786 to MWH and K01 AR080828 to UHC).
This work was also supported by the Mass Spectrometry Core of the Salk Institute
with funding from NIH-NCI CCSG: P30 014195 and the Helmsley Center for Genomic Medicine.
We thank Jolene Diedrich and Antonio Pinto for technical support.'
article_number: RP89066
article_processing_charge: Yes
article_type: original
author:
- first_name: Ukrae H.
full_name: Cho, Ukrae H.
last_name: Cho
- first_name: Martin W
full_name: Hetzer, Martin W
id: 86c0d31b-b4eb-11ec-ac5a-eae7b2e135ed
last_name: Hetzer
orcid: 0000-0002-2111-992X
citation:
ama: Cho UH, Hetzer M. Caspase-mediated nuclear pore complex trimming in cell differentiation
and endoplasmic reticulum stress. eLife. 2023;12. doi:10.7554/eLife.89066
apa: Cho, U. H., & Hetzer, M. (2023). Caspase-mediated nuclear pore complex
trimming in cell differentiation and endoplasmic reticulum stress. ELife.
eLife Sciences Publications. https://doi.org/10.7554/eLife.89066
chicago: Cho, Ukrae H., and Martin Hetzer. “Caspase-Mediated Nuclear Pore Complex
Trimming in Cell Differentiation and Endoplasmic Reticulum Stress.” ELife.
eLife Sciences Publications, 2023. https://doi.org/10.7554/eLife.89066.
ieee: U. H. Cho and M. Hetzer, “Caspase-mediated nuclear pore complex trimming in
cell differentiation and endoplasmic reticulum stress,” eLife, vol. 12.
eLife Sciences Publications, 2023.
ista: Cho UH, Hetzer M. 2023. Caspase-mediated nuclear pore complex trimming in
cell differentiation and endoplasmic reticulum stress. eLife. 12, RP89066.
mla: Cho, Ukrae H., and Martin Hetzer. “Caspase-Mediated Nuclear Pore Complex Trimming
in Cell Differentiation and Endoplasmic Reticulum Stress.” ELife, vol.
12, RP89066, eLife Sciences Publications, 2023, doi:10.7554/eLife.89066.
short: U.H. Cho, M. Hetzer, ELife 12 (2023).
date_created: 2023-09-10T22:01:11Z
date_published: 2023-09-04T00:00:00Z
date_updated: 2023-09-15T07:07:10Z
day: '04'
ddc:
- '570'
department:
- _id: MaHe
doi: 10.7554/eLife.89066
external_id:
pmid:
- '37665327'
file:
- access_level: open_access
checksum: db24bf3d595507387b48d3799c33e289
content_type: application/pdf
creator: dernst
date_created: 2023-09-15T06:59:10Z
date_updated: 2023-09-15T06:59:10Z
file_id: '14336'
file_name: 2023_eLife_Cho.pdf
file_size: 3703097
relation: main_file
success: 1
file_date_updated: 2023-09-15T06:59:10Z
has_accepted_license: '1'
intvolume: ' 12'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
pmid: 1
publication: eLife
publication_identifier:
eissn:
- 2050-084X
publication_status: published
publisher: eLife Sciences Publications
quality_controlled: '1'
scopus_import: '1'
status: public
title: Caspase-mediated nuclear pore complex trimming in cell differentiation and
endoplasmic reticulum stress
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 12
year: '2023'
...
---
_id: '14319'
abstract:
- lang: eng
text: "We study multigraphs whose edge-sets are the union of three perfect matchings,
M1, M2, and M3. Given such a graph G and any a1; a2; a3 2 N with a1 +a2 +a3 6
n - 2, we show there exists a matching M of G with jM \\ Mij = ai for each i 2
f1; 2; 3g. The bound n - 2 in the theorem is best possible in general. We conjecture
however that if G is bipartite, the same result holds with n - 2 replaced by n
- 1. We give a construction that shows such a result would be tight. We\r\nalso
make a conjecture generalising the Ryser-Brualdi-Stein conjecture with colour\r\nmultiplicities."
acknowledgement: Anastos has received funding from the European Union’s Horizon 2020
research and in-novation programme under the Marie Sk lodowska-Curie grant agreement
No 101034413.Fabian’s research is supported by the Deutsche Forschungsgemeinschaft
(DFG, GermanResearch Foundation) Graduiertenkolleg “Facets of Complexity” (GRK 2434).
article_number: P3.10
article_processing_charge: Yes
article_type: original
author:
- first_name: Michael
full_name: Anastos, Michael
id: 0b2a4358-bb35-11ec-b7b9-e3279b593dbb
last_name: Anastos
- first_name: David
full_name: Fabian, David
last_name: Fabian
- first_name: Alp
full_name: Müyesser, Alp
last_name: Müyesser
- first_name: Tibor
full_name: Szabó, Tibor
last_name: Szabó
citation:
ama: Anastos M, Fabian D, Müyesser A, Szabó T. Splitting matchings and the Ryser-Brualdi-Stein
conjecture for multisets. Electronic Journal of Combinatorics. 2023;30(3).
doi:10.37236/11714
apa: Anastos, M., Fabian, D., Müyesser, A., & Szabó, T. (2023). Splitting matchings
and the Ryser-Brualdi-Stein conjecture for multisets. Electronic Journal of
Combinatorics. Electronic Journal of Combinatorics. https://doi.org/10.37236/11714
chicago: Anastos, Michael, David Fabian, Alp Müyesser, and Tibor Szabó. “Splitting
Matchings and the Ryser-Brualdi-Stein Conjecture for Multisets.” Electronic
Journal of Combinatorics. Electronic Journal of Combinatorics, 2023. https://doi.org/10.37236/11714.
ieee: M. Anastos, D. Fabian, A. Müyesser, and T. Szabó, “Splitting matchings and
the Ryser-Brualdi-Stein conjecture for multisets,” Electronic Journal of Combinatorics,
vol. 30, no. 3. Electronic Journal of Combinatorics, 2023.
ista: Anastos M, Fabian D, Müyesser A, Szabó T. 2023. Splitting matchings and the
Ryser-Brualdi-Stein conjecture for multisets. Electronic Journal of Combinatorics.
30(3), P3.10.
mla: Anastos, Michael, et al. “Splitting Matchings and the Ryser-Brualdi-Stein Conjecture
for Multisets.” Electronic Journal of Combinatorics, vol. 30, no. 3, P3.10,
Electronic Journal of Combinatorics, 2023, doi:10.37236/11714.
short: M. Anastos, D. Fabian, A. Müyesser, T. Szabó, Electronic Journal of Combinatorics
30 (2023).
date_created: 2023-09-10T22:01:12Z
date_published: 2023-07-28T00:00:00Z
date_updated: 2023-09-15T08:12:30Z
day: '28'
ddc:
- '510'
department:
- _id: MaKw
doi: 10.37236/11714
ec_funded: 1
external_id:
arxiv:
- '2212.03100'
file:
- access_level: open_access
checksum: 52c46c8cb329f9aaee9ade01525f317b
content_type: application/pdf
creator: dernst
date_created: 2023-09-15T08:02:09Z
date_updated: 2023-09-15T08:02:09Z
file_id: '14338'
file_name: 2023_elecJournCombinatorics_Anastos.pdf
file_size: 247917
relation: main_file
success: 1
file_date_updated: 2023-09-15T08:02:09Z
has_accepted_license: '1'
intvolume: ' 30'
issue: '3'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nd/4.0/
month: '07'
oa: 1
oa_version: Published Version
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Electronic Journal of Combinatorics
publication_identifier:
eissn:
- 1077-8926
publication_status: published
publisher: Electronic Journal of Combinatorics
quality_controlled: '1'
scopus_import: '1'
status: public
title: Splitting matchings and the Ryser-Brualdi-Stein conjecture for multisets
tmp:
image: /image/cc_by_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nd/4.0/legalcode
name: Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)
short: CC BY-ND (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 30
year: '2023'
...
---
_id: '14318'
abstract:
- lang: eng
text: "Probabilistic recurrence relations (PRRs) are a standard formalism for describing
the runtime of a randomized algorithm. Given a PRR and a time limit κ, we consider
the tail probability Pr[T≥κ], i.e., the probability that the randomized runtime
T of the PRR exceeds κ. Our focus is the formal analysis of tail bounds that aims
at finding a tight asymptotic upper bound u≥Pr[T≥κ]. To address this problem,
the classical and most well-known approach is the cookbook method by Karp (JACM
1994), while other approaches are mostly limited to deriving tail bounds of specific
PRRs via involved custom analysis.\r\nIn this work, we propose a novel approach
for deriving the common exponentially-decreasing tail bounds for PRRs whose preprocessing
time and random passed sizes observe discrete or (piecewise) uniform distribution
and whose recursive call is either a single procedure call or a divide-and-conquer.
We first establish a theoretical approach via Markov’s inequality, and then instantiate
the theoretical approach with a template-based algorithmic approach via a refined
treatment of exponentiation. Experimental evaluation shows that our algorithmic
approach is capable of deriving tail bounds that are (i) asymptotically tighter
than Karp’s method, (ii) match the best-known manually-derived asymptotic tail
bound for QuickSelect, and (iii) is only slightly worse (with a loglogn factor)
than the manually-proven optimal asymptotic tail bound for QuickSort. Moreover,
our algorithmic approach handles all examples (including realistic PRRs such as
QuickSort, QuickSelect, DiameterComputation, etc.) in less than 0.1 s, showing
that our approach is efficient in practice."
acknowledgement: We thank Prof. Bican Xia for valuable information on the exponential
theory of reals. The work is partially supported by the National Natural Science
Foundation of China (NSFC) with Grant No. 62172271, ERC CoG 863818 (ForM-SMArt),
the Hong Kong Research Grants Council ECS Project Number 26208122, the HKUST-Kaisa
Joint Research Institute Project Grant HKJRI3A-055 and the HKUST Startup Grant R9272.
alternative_title:
- LNCS
article_processing_charge: Yes (in subscription journal)
author:
- first_name: Yican
full_name: Sun, Yican
last_name: Sun
- first_name: Hongfei
full_name: Fu, Hongfei
last_name: Fu
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Amir Kafshdar
full_name: Goharshady, Amir Kafshdar
id: 391365CE-F248-11E8-B48F-1D18A9856A87
last_name: Goharshady
orcid: 0000-0003-1702-6584
citation:
ama: 'Sun Y, Fu H, Chatterjee K, Goharshady AK. Automated tail bound analysis for probabilistic
recurrence relations. In: Computer Aided Verification. Vol 13966. Springer
Nature; 2023:16-39. doi:10.1007/978-3-031-37709-9_2'
apa: 'Sun, Y., Fu, H., Chatterjee, K., & Goharshady, A. K. (2023). Automated
tail bound analysis for probabilistic recurrence relations. In Computer Aided
Verification (Vol. 13966, pp. 16–39). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-37709-9_2'
chicago: Sun, Yican, Hongfei Fu, Krishnendu Chatterjee, and Amir Kafshdar Goharshady.
“Automated Tail Bound Analysis for Probabilistic Recurrence Relations.” In Computer
Aided Verification, 13966:16–39. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-37709-9_2.
ieee: Y. Sun, H. Fu, K. Chatterjee, and A. K. Goharshady, “Automated tail bound
analysis for probabilistic recurrence relations,” in Computer Aided Verification,
Paris, France, 2023, vol. 13966, pp. 16–39.
ista: 'Sun Y, Fu H, Chatterjee K, Goharshady AK. 2023. Automated tail bound analysis
for probabilistic recurrence relations. Computer Aided Verification. CAV: Computer
Aided Verification, LNCS, vol. 13966, 16–39.'
mla: Sun, Yican, et al. “Automated Tail Bound Analysis for Probabilistic Recurrence
Relations.” Computer Aided Verification, vol. 13966, Springer Nature, 2023,
pp. 16–39, doi:10.1007/978-3-031-37709-9_2.
short: Y. Sun, H. Fu, K. Chatterjee, A.K. Goharshady, in:, Computer Aided Verification,
Springer Nature, 2023, pp. 16–39.
conference:
end_date: 2023-07-22
location: Paris, France
name: 'CAV: Computer Aided Verification'
start_date: 2023-07-17
date_created: 2023-09-10T22:01:12Z
date_published: 2023-07-17T00:00:00Z
date_updated: 2023-09-20T08:25:57Z
day: '17'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.1007/978-3-031-37709-9_2
ec_funded: 1
file:
- access_level: open_access
checksum: 42917e086f8c7699f3bccf84f74fe000
content_type: application/pdf
creator: dernst
date_created: 2023-09-20T08:24:47Z
date_updated: 2023-09-20T08:24:47Z
file_id: '14348'
file_name: 2023_LNCS_Sun.pdf
file_size: 624647
relation: main_file
success: 1
file_date_updated: 2023-09-20T08:24:47Z
has_accepted_license: '1'
intvolume: ' 13966'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 16-39
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: Computer Aided Verification
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783031377082'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/boyvolcano/PRR
scopus_import: '1'
status: public
title: Automated tail bound analysis for probabilistic recurrence relations
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13966
year: '2023'
...
---
_id: '14317'
abstract:
- lang: eng
text: "Markov decision processes can be viewed as transformers of probability distributions.
While this view is useful from a practical standpoint to reason about trajectories
of distributions, basic reachability and safety problems are known to be computationally
intractable (i.e., Skolem-hard) to solve in such models. Further, we show that
even for simple examples of MDPs, strategies for safety objectives over distributions
can require infinite memory and randomization.\r\nIn light of this, we present
a novel overapproximation approach to synthesize strategies in an MDP, such that
a safety objective over the distributions is met. More precisely, we develop a
new framework for template-based synthesis of certificates as affine distributional
and inductive invariants for safety objectives in MDPs. We provide two algorithms
within this framework. One can only synthesize memoryless strategies, but has
relative completeness guarantees, while the other can synthesize general strategies.
The runtime complexity of both algorithms is in PSPACE. We implement these algorithms
and show that they can solve several non-trivial examples."
acknowledgement: This work was supported in part by the ERC CoG 863818 (FoRM-SMArt)
and the European Union’s Horizon 2020 research and innovation programme under the
Marie Skłodowska-Curie Grant Agreement No. 665385 as well as DST/CEFIPRA/INRIA project
EQuaVE and SERB Matrices grant MTR/2018/00074.
alternative_title:
- LNCS
article_processing_charge: Yes (in subscription journal)
author:
- first_name: S.
full_name: Akshay, S.
last_name: Akshay
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Tobias
full_name: Meggendorfer, Tobias
id: b21b0c15-30a2-11eb-80dc-f13ca25802e1
last_name: Meggendorfer
orcid: 0000-0002-1712-2165
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
citation:
ama: 'Akshay S, Chatterjee K, Meggendorfer T, Zikelic D. MDPs as distribution transformers:
Affine invariant synthesis for safety objectives. In: International Conference
on Computer Aided Verification. Vol 13966. Springer Nature; 2023:86-112. doi:10.1007/978-3-031-37709-9_5'
apa: 'Akshay, S., Chatterjee, K., Meggendorfer, T., & Zikelic, D. (2023). MDPs
as distribution transformers: Affine invariant synthesis for safety objectives.
In International Conference on Computer Aided Verification (Vol. 13966,
pp. 86–112). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-37709-9_5'
chicago: 'Akshay, S., Krishnendu Chatterjee, Tobias Meggendorfer, and Dorde Zikelic.
“MDPs as Distribution Transformers: Affine Invariant Synthesis for Safety Objectives.”
In International Conference on Computer Aided Verification, 13966:86–112.
Springer Nature, 2023. https://doi.org/10.1007/978-3-031-37709-9_5.'
ieee: 'S. Akshay, K. Chatterjee, T. Meggendorfer, and D. Zikelic, “MDPs as distribution
transformers: Affine invariant synthesis for safety objectives,” in International
Conference on Computer Aided Verification, Paris, France, 2023, vol. 13966,
pp. 86–112.'
ista: 'Akshay S, Chatterjee K, Meggendorfer T, Zikelic D. 2023. MDPs as distribution
transformers: Affine invariant synthesis for safety objectives. International
Conference on Computer Aided Verification. CAV: Computer Aided Verification, LNCS,
vol. 13966, 86–112.'
mla: 'Akshay, S., et al. “MDPs as Distribution Transformers: Affine Invariant Synthesis
for Safety Objectives.” International Conference on Computer Aided Verification,
vol. 13966, Springer Nature, 2023, pp. 86–112, doi:10.1007/978-3-031-37709-9_5.'
short: S. Akshay, K. Chatterjee, T. Meggendorfer, D. Zikelic, in:, International
Conference on Computer Aided Verification, Springer Nature, 2023, pp. 86–112.
conference:
end_date: 2023-07-22
location: Paris, France
name: 'CAV: Computer Aided Verification'
start_date: 2023-07-17
date_created: 2023-09-10T22:01:12Z
date_published: 2023-07-17T00:00:00Z
date_updated: 2023-09-20T09:04:40Z
day: '17'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.1007/978-3-031-37709-9_5
ec_funded: 1
file:
- access_level: open_access
checksum: f143c8eedf609f20f2aad2eeb496d53f
content_type: application/pdf
creator: dernst
date_created: 2023-09-20T08:46:43Z
date_updated: 2023-09-20T08:46:43Z
file_id: '14349'
file_name: 2023_LNCS_Akshay.pdf
file_size: 531745
relation: main_file
success: 1
file_date_updated: 2023-09-20T08:46:43Z
has_accepted_license: '1'
intvolume: ' 13966'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 86-112
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: International Conference on Computer Aided Verification
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783031377082'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'MDPs as distribution transformers: Affine invariant synthesis for safety objectives'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13966
year: '2023'
...
---
_id: '14316'
abstract:
- lang: eng
text: Clathrin-mediated vesicle trafficking plays central roles in post-Golgi transport.
In yeast (Saccharomyces cerevisiae), the AP-1 complex and GGA adaptors are predicted
to generate distinct transport vesicles at the trans-Golgi network (TGN), and
the epsin-related proteins Ent3p and Ent5p (collectively Ent3p/5p) act as accessories
for these adaptors. Recently, we showed that vesicle transport from the TGN is
crucial for yeast Rab5 (Vps21p)-mediated endosome formation, and that Ent3p/5p
are crucial for this process, whereas AP-1 and GGA adaptors are dispensable. However,
these observations were incompatible with previous studies showing that these
adaptors are required for Ent3p/5p recruitment to the TGN, and thus the overall
mechanism responsible for regulation of Vps21p activity remains ambiguous. Here,
we investigated the functional relationships between clathrin adaptors in post-Golgi-mediated
Vps21p activation. We show that AP-1 disruption in the ent3Δ5Δ mutant impaired
transport of the Vps21p guanine nucleotide exchange factor Vps9p transport to
the Vps21p compartment and severely reduced Vps21p activity. Additionally, GGA
adaptors, the phosphatidylinositol-4-kinase Pik1p and Rab11 GTPases Ypt31p and
Ypt32p were found to have partially overlapping functions for recruitment of AP-1
and Ent3p/5p to the TGN. These findings suggest a distinct role of clathrin adaptors
for Vps21p activation in the TGN–endosome trafficking pathway.
article_number: jcs261448
article_processing_charge: No
article_type: original
author:
- first_name: Makoto
full_name: Nagano, Makoto
last_name: Nagano
- first_name: Kaito
full_name: Aoshima, Kaito
last_name: Aoshima
- first_name: Hiroki
full_name: Shimamura, Hiroki
last_name: Shimamura
- first_name: Daria E
full_name: Siekhaus, Daria E
id: 3D224B9E-F248-11E8-B48F-1D18A9856A87
last_name: Siekhaus
orcid: 0000-0001-8323-8353
- first_name: Junko Y.
full_name: Toshima, Junko Y.
last_name: Toshima
- first_name: Jiro
full_name: Toshima, Jiro
last_name: Toshima
citation:
ama: Nagano M, Aoshima K, Shimamura H, Siekhaus DE, Toshima JY, Toshima J. Distinct
role of TGN-resident clathrin adaptors for Vps21p activation in the TGN-endosome
trafficking pathway. Journal of Cell Science. 2023;136(17). doi:10.1242/jcs.261448
apa: Nagano, M., Aoshima, K., Shimamura, H., Siekhaus, D. E., Toshima, J. Y., &
Toshima, J. (2023). Distinct role of TGN-resident clathrin adaptors for Vps21p
activation in the TGN-endosome trafficking pathway. Journal of Cell Science.
The Company of Biologists. https://doi.org/10.1242/jcs.261448
chicago: Nagano, Makoto, Kaito Aoshima, Hiroki Shimamura, Daria E Siekhaus, Junko
Y. Toshima, and Jiro Toshima. “Distinct Role of TGN-Resident Clathrin Adaptors
for Vps21p Activation in the TGN-Endosome Trafficking Pathway.” Journal of
Cell Science. The Company of Biologists, 2023. https://doi.org/10.1242/jcs.261448.
ieee: M. Nagano, K. Aoshima, H. Shimamura, D. E. Siekhaus, J. Y. Toshima, and J.
Toshima, “Distinct role of TGN-resident clathrin adaptors for Vps21p activation
in the TGN-endosome trafficking pathway,” Journal of Cell Science, vol.
136, no. 17. The Company of Biologists, 2023.
ista: Nagano M, Aoshima K, Shimamura H, Siekhaus DE, Toshima JY, Toshima J. 2023.
Distinct role of TGN-resident clathrin adaptors for Vps21p activation in the TGN-endosome
trafficking pathway. Journal of Cell Science. 136(17), jcs261448.
mla: Nagano, Makoto, et al. “Distinct Role of TGN-Resident Clathrin Adaptors for
Vps21p Activation in the TGN-Endosome Trafficking Pathway.” Journal of Cell
Science, vol. 136, no. 17, jcs261448, The Company of Biologists, 2023, doi:10.1242/jcs.261448.
short: M. Nagano, K. Aoshima, H. Shimamura, D.E. Siekhaus, J.Y. Toshima, J. Toshima,
Journal of Cell Science 136 (2023).
date_created: 2023-09-10T22:01:12Z
date_published: 2023-09-01T00:00:00Z
date_updated: 2023-09-20T09:14:15Z
day: '01'
department:
- _id: DaSi
doi: 10.1242/jcs.261448
external_id:
pmid:
- '37539494'
intvolume: ' 136'
issue: '17'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1101/2023.03.27.534325
month: '09'
oa: 1
oa_version: Preprint
pmid: 1
publication: Journal of Cell Science
publication_identifier:
eissn:
- 1477-9137
issn:
- 0021-9533
publication_status: published
publisher: The Company of Biologists
quality_controlled: '1'
scopus_import: '1'
status: public
title: Distinct role of TGN-resident clathrin adaptors for Vps21p activation in the
TGN-endosome trafficking pathway
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 136
year: '2023'
...