---
_id: '1424'
abstract:
- lang: eng
text: We consider the problem of statistical computations with persistence diagrams,
a summary representation of topological features in data. These diagrams encode
persistent homology, a widely used invariant in topological data analysis. While
several avenues towards a statistical treatment of the diagrams have been explored
recently, we follow an alternative route that is motivated by the success of methods
based on the embedding of probability measures into reproducing kernel Hilbert
spaces. In fact, a positive definite kernel on persistence diagrams has recently
been proposed, connecting persistent homology to popular kernel-based learning
techniques such as support vector machines. However, important properties of that
kernel enabling a principled use in the context of probability measure embeddings
remain to be explored. Our contribution is to close this gap by proving universality
of a variant of the original kernel, and to demonstrate its effective use in twosample
hypothesis testing on synthetic as well as real-world data.
acknowledgement: This work was partially supported by the Austrian Science FUnd, project
no. KLI 00012.
alternative_title:
- Advances in Neural Information Processing Systems
author:
- first_name: Roland
full_name: Kwitt, Roland
last_name: Kwitt
- first_name: Stefan
full_name: Huber, Stefan
id: 4700A070-F248-11E8-B48F-1D18A9856A87
last_name: Huber
orcid: 0000-0002-8871-5814
- first_name: Marc
full_name: Niethammer, Marc
last_name: Niethammer
- first_name: Weili
full_name: Lin, Weili
last_name: Lin
- first_name: Ulrich
full_name: Bauer, Ulrich
id: 2ADD483A-F248-11E8-B48F-1D18A9856A87
last_name: Bauer
orcid: 0000-0002-9683-0724
citation:
ama: 'Kwitt R, Huber S, Niethammer M, Lin W, Bauer U. Statistical topological data
analysis-A kernel perspective. In: Vol 28. Neural Information Processing Systems;
2015:3070-3078.'
apa: 'Kwitt, R., Huber, S., Niethammer, M., Lin, W., & Bauer, U. (2015). Statistical
topological data analysis-A kernel perspective (Vol. 28, pp. 3070–3078). Presented
at the NIPS: Neural Information Processing Systems, Montreal, Canada: Neural Information
Processing Systems.'
chicago: Kwitt, Roland, Stefan Huber, Marc Niethammer, Weili Lin, and Ulrich Bauer.
“Statistical Topological Data Analysis-A Kernel Perspective,” 28:3070–78. Neural
Information Processing Systems, 2015.
ieee: 'R. Kwitt, S. Huber, M. Niethammer, W. Lin, and U. Bauer, “Statistical topological
data analysis-A kernel perspective,” presented at the NIPS: Neural Information
Processing Systems, Montreal, Canada, 2015, vol. 28, pp. 3070–3078.'
ista: 'Kwitt R, Huber S, Niethammer M, Lin W, Bauer U. 2015. Statistical topological
data analysis-A kernel perspective. NIPS: Neural Information Processing Systems,
Advances in Neural Information Processing Systems, vol. 28, 3070–3078.'
mla: Kwitt, Roland, et al. Statistical Topological Data Analysis-A Kernel Perspective.
Vol. 28, Neural Information Processing Systems, 2015, pp. 3070–78.
short: R. Kwitt, S. Huber, M. Niethammer, W. Lin, U. Bauer, in:, Neural Information
Processing Systems, 2015, pp. 3070–3078.
conference:
end_date: 2015-12-12
location: Montreal, Canada
name: 'NIPS: Neural Information Processing Systems'
start_date: 2015-12-07
date_created: 2018-12-11T11:51:56Z
date_published: 2015-12-01T00:00:00Z
date_updated: 2021-01-12T06:50:38Z
day: '01'
department:
- _id: HeEd
intvolume: ' 28'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://papers.nips.cc/paper/5887-statistical-topological-data-analysis-a-kernel-perspective
month: '12'
oa: 1
oa_version: Submitted Version
page: 3070 - 3078
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '5782'
quality_controlled: '1'
status: public
title: Statistical topological data analysis-A kernel perspective
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 28
year: '2015'
...
---
_id: '1430'
abstract:
- lang: eng
text: Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired
by natural evolution. In recent years the field of evolutionary computation has
developed a rigorous analytical theory to analyse their runtime on many illustrative
problems. Here we apply this theory to a simple model of natural evolution. In
the Strong Selection Weak Mutation (SSWM) evolutionary regime the time between
occurrence of new mutations is much longer than the time it takes for a new beneficial
mutation to take over the population. In this situation, the population only contains
copies of one genotype and evolution can be modelled as a (1+1)-type process where
the probability of accepting a new genotype (improvements or worsenings) depends
on the change in fitness. We present an initial runtime analysis of SSWM, quantifying
its performance for various parameters and investigating differences to the (1+1)
EA. We show that SSWM can have a moderate advantage over the (1+1) EA at crossing
fitness valleys and study an example where SSWM outperforms the (1+1) EA by taking
advantage of information on the fitness gradient.
author:
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
- first_name: Jorge
full_name: Heredia, Jorge
last_name: Heredia
- first_name: Barbora
full_name: Trubenova, Barbora
id: 42302D54-F248-11E8-B48F-1D18A9856A87
last_name: Trubenova
orcid: 0000-0002-6873-2967
citation:
ama: 'Paixao T, Sudholt D, Heredia J, Trubenova B. First steps towards a runtime
comparison of natural and artificial evolution. In: Proceedings of the 2015
Annual Conference on Genetic and Evolutionary Computation. ACM; 2015:1455-1462.
doi:10.1145/2739480.2754758'
apa: 'Paixao, T., Sudholt, D., Heredia, J., & Trubenova, B. (2015). First steps
towards a runtime comparison of natural and artificial evolution. In Proceedings
of the 2015 Annual Conference on Genetic and Evolutionary Computation (pp.
1455–1462). Madrid, Spain: ACM. https://doi.org/10.1145/2739480.2754758'
chicago: Paixao, Tiago, Dirk Sudholt, Jorge Heredia, and Barbora Trubenova. “First
Steps towards a Runtime Comparison of Natural and Artificial Evolution.” In Proceedings
of the 2015 Annual Conference on Genetic and Evolutionary Computation, 1455–62.
ACM, 2015. https://doi.org/10.1145/2739480.2754758.
ieee: T. Paixao, D. Sudholt, J. Heredia, and B. Trubenova, “First steps towards
a runtime comparison of natural and artificial evolution,” in Proceedings of
the 2015 Annual Conference on Genetic and Evolutionary Computation, Madrid,
Spain, 2015, pp. 1455–1462.
ista: 'Paixao T, Sudholt D, Heredia J, Trubenova B. 2015. First steps towards a
runtime comparison of natural and artificial evolution. Proceedings of the 2015
Annual Conference on Genetic and Evolutionary Computation. GECCO: Genetic and
evolutionary computation conference, 1455–1462.'
mla: Paixao, Tiago, et al. “First Steps towards a Runtime Comparison of Natural
and Artificial Evolution.” Proceedings of the 2015 Annual Conference on Genetic
and Evolutionary Computation, ACM, 2015, pp. 1455–62, doi:10.1145/2739480.2754758.
short: T. Paixao, D. Sudholt, J. Heredia, B. Trubenova, in:, Proceedings of the
2015 Annual Conference on Genetic and Evolutionary Computation, ACM, 2015, pp.
1455–1462.
conference:
end_date: 2015-07-15
location: Madrid, Spain
name: 'GECCO: Genetic and evolutionary computation conference'
start_date: 2015-07-11
date_created: 2018-12-11T11:51:58Z
date_published: 2015-07-11T00:00:00Z
date_updated: 2021-01-12T06:50:41Z
day: '11'
department:
- _id: NiBa
- _id: CaGu
doi: 10.1145/2739480.2754758
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1504.06260
month: '07'
oa: 1
oa_version: Preprint
page: 1455 - 1462
project:
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
publication: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary
Computation
publication_status: published
publisher: ACM
publist_id: '5768'
quality_controlled: '1'
scopus_import: 1
status: public
title: First steps towards a runtime comparison of natural and artificial evolution
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2015'
...
---
_id: '1474'
abstract:
- lang: eng
text: Cryptographic access control offers selective access to encrypted data via
a combination of key management and functionality-rich cryptographic schemes,
such as attribute-based encryption. Using this approach, publicly available meta-data
may inadvertently leak information on the access policy that is enforced by cryptography,
which renders cryptographic access control unusable in settings where this information
is highly sensitive. We begin to address this problem by presenting rigorous definitions
for policy privacy in cryptographic access control. For concreteness we set our
results in the model of Role-Based Access Control (RBAC), where we identify and
formalize several different flavors of privacy, however, our framework should
serve as inspiration for other models of access control. Based on our insights
we propose a new system which significantly improves on the privacy properties
of state-of-the-art constructions. Our design is based on a novel type of privacy-preserving
attribute-based encryption, which we introduce and show how to instantiate. We
present our results in the context of a cryptographic RBAC system by Ferrara et
al. (CSF'13), which uses cryptography to control read access to files, while write
access is still delegated to trusted monitors. We give an extension of the construction
that permits cryptographic control over write access. Our construction assumes
that key management uses out-of-band channels between the policy enforcer and
the users but eliminates completely the need for monitoring read/write access
to the data.
article_processing_charge: No
author:
- first_name: Anna
full_name: Ferrara, Anna
last_name: Ferrara
- first_name: Georg
full_name: Fuchsbauer, Georg
id: 46B4C3EE-F248-11E8-B48F-1D18A9856A87
last_name: Fuchsbauer
- first_name: Bin
full_name: Liu, Bin
last_name: Liu
- first_name: Bogdan
full_name: Warinschi, Bogdan
last_name: Warinschi
citation:
ama: 'Ferrara A, Fuchsbauer G, Liu B, Warinschi B. Policy privacy in cryptographic
access control. In: IEEE; 2015:46-60. doi:10.1109/CSF.2015.11'
apa: 'Ferrara, A., Fuchsbauer, G., Liu, B., & Warinschi, B. (2015). Policy privacy
in cryptographic access control (pp. 46–60). Presented at the CSF: Computer Security
Foundations, Verona, Italy: IEEE. https://doi.org/10.1109/CSF.2015.11'
chicago: Ferrara, Anna, Georg Fuchsbauer, Bin Liu, and Bogdan Warinschi. “Policy
Privacy in Cryptographic Access Control,” 46–60. IEEE, 2015. https://doi.org/10.1109/CSF.2015.11.
ieee: 'A. Ferrara, G. Fuchsbauer, B. Liu, and B. Warinschi, “Policy privacy in cryptographic
access control,” presented at the CSF: Computer Security Foundations, Verona,
Italy, 2015, pp. 46–60.'
ista: 'Ferrara A, Fuchsbauer G, Liu B, Warinschi B. 2015. Policy privacy in cryptographic
access control. CSF: Computer Security Foundations, 46–60.'
mla: Ferrara, Anna, et al. Policy Privacy in Cryptographic Access Control.
IEEE, 2015, pp. 46–60, doi:10.1109/CSF.2015.11.
short: A. Ferrara, G. Fuchsbauer, B. Liu, B. Warinschi, in:, IEEE, 2015, pp. 46–60.
conference:
end_date: 2015-07-17
location: Verona, Italy
name: 'CSF: Computer Security Foundations'
start_date: 2015-07-13
date_created: 2018-12-11T11:52:14Z
date_published: 2015-09-04T00:00:00Z
date_updated: 2021-01-12T06:50:59Z
day: '04'
department:
- _id: KrPi
doi: 10.1109/CSF.2015.11
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://epubs.surrey.ac.uk/808055/
month: '09'
oa: 1
oa_version: Submitted Version
page: 46-60
project:
- _id: 258C570E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '259668'
name: Provable Security for Physical Cryptography
publication_status: published
publisher: IEEE
publist_id: '5722'
quality_controlled: '1'
status: public
title: Policy privacy in cryptographic access control
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2015'
...
---
_id: '1473'
abstract:
- lang: eng
text: In this paper we survey geometric and arithmetic techniques to study the cohomology
of semiprojective hyperkähler manifolds including toric hyperkähler varieties,
Nakajima quiver varieties and moduli spaces of Higgs bundles on Riemann surfaces.
The resulting formulae for their Poincaré polynomials are combinatorial and representation
theoretical in nature. In particular we will look at their Betti numbers and will
establish some results and state some expectations on their asymptotic shape.
author:
- first_name: Tamas
full_name: Tamas Hausel
id: 4A0666D8-F248-11E8-B48F-1D18A9856A87
last_name: Hausel
- first_name: Fernando
full_name: Rodríguez Villegas, Fernando
last_name: Rodríguez Villegas
citation:
ama: Hausel T, Rodríguez Villegas F. Cohomology of large semiprojective hyperkähler
varieties. Asterisque. 2015;2015(370):113-156.
apa: Hausel, T., & Rodríguez Villegas, F. (2015). Cohomology of large semiprojective
hyperkähler varieties. Asterisque. Societe Mathematique de France.
chicago: Hausel, Tamás, and Fernando Rodríguez Villegas. “Cohomology of Large Semiprojective
Hyperkähler Varieties.” Asterisque. Societe Mathematique de France, 2015.
ieee: T. Hausel and F. Rodríguez Villegas, “Cohomology of large semiprojective hyperkähler
varieties,” Asterisque, vol. 2015, no. 370. Societe Mathematique de France,
pp. 113–156, 2015.
ista: Hausel T, Rodríguez Villegas F. 2015. Cohomology of large semiprojective hyperkähler
varieties. Asterisque. 2015(370), 113–156.
mla: Hausel, Tamás, and Fernando Rodríguez Villegas. “Cohomology of Large Semiprojective
Hyperkähler Varieties.” Asterisque, vol. 2015, no. 370, Societe Mathematique
de France, 2015, pp. 113–56.
short: T. Hausel, F. Rodríguez Villegas, Asterisque 2015 (2015) 113–156.
date_created: 2018-12-11T11:52:13Z
date_published: 2015-01-01T00:00:00Z
date_updated: 2021-01-12T06:50:59Z
day: '01'
extern: 1
intvolume: ' 2015'
issue: '370'
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1309.4914
month: '01'
oa: 1
page: 113 - 156
publication: Asterisque
publication_status: published
publisher: Societe Mathematique de France
publist_id: '5723'
quality_controlled: 0
status: public
title: Cohomology of large semiprojective hyperkähler varieties
type: review
volume: 2015
year: '2015'
...
---
_id: '1483'
abstract:
- lang: eng
text: Topological data analysis offers a rich source of valuable information to
study vision problems. Yet, so far we lack a theoretically sound connection to
popular kernel-based learning techniques, such as kernel SVMs or kernel PCA. In
this work, we establish such a connection by designing a multi-scale kernel for
persistence diagrams, a stable summary representation of topological features
in data. We show that this kernel is positive definite and prove its stability
with respect to the 1-Wasserstein distance. Experiments on two benchmark datasets
for 3D shape classification/retrieval and texture recognition show considerable
performance gains of the proposed method compared to an alternative approach that
is based on the recently introduced persistence landscapes.
author:
- first_name: Jan
full_name: Reininghaus, Jan
id: 4505473A-F248-11E8-B48F-1D18A9856A87
last_name: Reininghaus
- first_name: Stefan
full_name: Huber, Stefan
id: 4700A070-F248-11E8-B48F-1D18A9856A87
last_name: Huber
orcid: 0000-0002-8871-5814
- first_name: Ulrich
full_name: Bauer, Ulrich
id: 2ADD483A-F248-11E8-B48F-1D18A9856A87
last_name: Bauer
orcid: 0000-0002-9683-0724
- first_name: Roland
full_name: Kwitt, Roland
last_name: Kwitt
citation:
ama: 'Reininghaus J, Huber S, Bauer U, Kwitt R. A stable multi-scale kernel for
topological machine learning. In: IEEE; 2015:4741-4748. doi:10.1109/CVPR.2015.7299106'
apa: 'Reininghaus, J., Huber, S., Bauer, U., & Kwitt, R. (2015). A stable multi-scale
kernel for topological machine learning (pp. 4741–4748). Presented at the CVPR:
Computer Vision and Pattern Recognition, Boston, MA, USA: IEEE. https://doi.org/10.1109/CVPR.2015.7299106'
chicago: Reininghaus, Jan, Stefan Huber, Ulrich Bauer, and Roland Kwitt. “A Stable
Multi-Scale Kernel for Topological Machine Learning,” 4741–48. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7299106.
ieee: 'J. Reininghaus, S. Huber, U. Bauer, and R. Kwitt, “A stable multi-scale kernel
for topological machine learning,” presented at the CVPR: Computer Vision and
Pattern Recognition, Boston, MA, USA, 2015, pp. 4741–4748.'
ista: 'Reininghaus J, Huber S, Bauer U, Kwitt R. 2015. A stable multi-scale kernel
for topological machine learning. CVPR: Computer Vision and Pattern Recognition,
4741–4748.'
mla: Reininghaus, Jan, et al. A Stable Multi-Scale Kernel for Topological Machine
Learning. IEEE, 2015, pp. 4741–48, doi:10.1109/CVPR.2015.7299106.
short: J. Reininghaus, S. Huber, U. Bauer, R. Kwitt, in:, IEEE, 2015, pp. 4741–4748.
conference:
end_date: 2015-06-12
location: Boston, MA, USA
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2015-06-07
date_created: 2018-12-11T11:52:17Z
date_published: 2015-10-14T00:00:00Z
date_updated: 2021-01-12T06:51:03Z
day: '14'
department:
- _id: HeEd
doi: 10.1109/CVPR.2015.7299106
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1412.6821
month: '10'
oa: 1
oa_version: Preprint
page: 4741 - 4748
publication_identifier:
eisbn:
- '978-1-4673-6964-0 '
publication_status: published
publisher: IEEE
publist_id: '5709'
scopus_import: 1
status: public
title: A stable multi-scale kernel for topological machine learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2015'
...