--- _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' ...