---
_id: '12750'
abstract:
- lang: eng
text: Quantum kinetically constrained models have recently attracted significant
attention due to their anomalous dynamics and thermalization. In this work, we
introduce a hitherto unexplored family of kinetically constrained models featuring
a conserved particle number and strong inversion-symmetry breaking due to facilitated
hopping. We demonstrate that these models provide a generic example of so-called
quantum Hilbert space fragmentation, that is manifested in disconnected sectors
in the Hilbert space that are not apparent in the computational basis. Quantum
Hilbert space fragmentation leads to an exponential in system size number of eigenstates
with exactly zero entanglement entropy across several bipartite cuts. These eigenstates
can be probed dynamically using quenches from simple initial product states. In
addition, we study the particle spreading under unitary dynamics launched from
the domain wall state, and find faster than diffusive dynamics at high particle
densities, that crosses over into logarithmically slow relaxation at smaller densities.
Using a classically simulable cellular automaton, we reproduce the logarithmic
dynamics observed in the quantum case. Our work suggests that particle conserving
constrained models with inversion symmetry breaking realize so far unexplored
universality classes of dynamics and invite their further theoretical and experimental
studies.
article_number: '2210.15607'
article_processing_charge: No
arxiv: 1
author:
- first_name: Pietro
full_name: Brighi, Pietro
id: 4115AF5C-F248-11E8-B48F-1D18A9856A87
last_name: Brighi
orcid: 0000-0002-7969-2729
- first_name: Marko
full_name: Ljubotina, Marko
id: F75EE9BE-5C90-11EA-905D-16643DDC885E
last_name: Ljubotina
orcid: 0000-0003-0038-7068
- first_name: Maksym
full_name: Serbyn, Maksym
id: 47809E7E-F248-11E8-B48F-1D18A9856A87
last_name: Serbyn
orcid: 0000-0002-2399-5827
citation:
ama: Brighi P, Ljubotina M, Serbyn M. Hilbert space fragmentation and slow dynamics
in particle-conserving quantum East models. arXiv. doi:10.48550/arXiv.2210.15607
apa: Brighi, P., Ljubotina, M., & Serbyn, M. (n.d.). Hilbert space fragmentation
and slow dynamics in particle-conserving quantum East models. arXiv. https://doi.org/10.48550/arXiv.2210.15607
chicago: Brighi, Pietro, Marko Ljubotina, and Maksym Serbyn. “Hilbert Space Fragmentation
and Slow Dynamics in Particle-Conserving Quantum East Models.” ArXiv, n.d.
https://doi.org/10.48550/arXiv.2210.15607.
ieee: P. Brighi, M. Ljubotina, and M. Serbyn, “Hilbert space fragmentation and slow
dynamics in particle-conserving quantum East models,” arXiv. .
ista: Brighi P, Ljubotina M, Serbyn M. Hilbert space fragmentation and slow dynamics
in particle-conserving quantum East models. arXiv, 2210.15607.
mla: Brighi, Pietro, et al. “Hilbert Space Fragmentation and Slow Dynamics in Particle-Conserving
Quantum East Models.” ArXiv, 2210.15607, doi:10.48550/arXiv.2210.15607.
short: P. Brighi, M. Ljubotina, M. Serbyn, ArXiv (n.d.).
date_created: 2023-03-23T14:33:13Z
date_published: 2022-11-07T00:00:00Z
date_updated: 2023-09-20T10:46:29Z
day: '07'
department:
- _id: GradSch
- _id: MaSe
doi: 10.48550/arXiv.2210.15607
external_id:
arxiv:
- '2210.15607'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-sa/4.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2210.15607
month: '11'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
related_material:
record:
- id: '12732'
relation: dissertation_contains
status: public
- id: '14334'
relation: later_version
status: public
status: public
title: Hilbert space fragmentation and slow dynamics in particle-conserving quantum
East models
tmp:
image: /images/cc_by_nc_sa.png
legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC
BY-NC-SA 4.0)
short: CC BY-NC-SA (4.0)
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12775'
abstract:
- lang: eng
text: "We consider the problem of approximating the reachability probabilities in
Markov decision processes (MDP) with uncountable (continuous) state and action
spaces. While there are algorithms that, for special classes of such MDP, provide
a sequence of approximations converging to the true value in the limit, our aim
is to obtain an algorithm with guarantees on the precision of the approximation.\r\nAs
this problem is undecidable in general, assumptions on the MDP are necessary.
Our main contribution is to identify sufficient assumptions that are as weak as
possible, thus approaching the \"boundary\" of which systems can be correctly
and reliably analyzed. To this end, we also argue why each of our assumptions
is necessary for algorithms based on processing finitely many observations.\r\nWe
present two solution variants. The first one provides converging lower bounds
under weaker assumptions than typical ones from previous works concerned with
guarantees. The second one then utilizes stronger assumptions to additionally
provide converging upper bounds. Altogether, we obtain an anytime algorithm, i.e.
yielding a sequence of approximants with known and iteratively improving precision,
converging to the true value in the limit. Besides, due to the generality of our
assumptions, our algorithms are very general templates, readily allowing for various
heuristics from literature in contrast to, e.g., a specific discretization algorithm.
Our theoretical contribution thus paves the way for future practical improvements
without sacrificing correctness guarantees."
acknowledgement: "Kush Grover: The author has been supported by the DFG research training
group GRK\r\n2428 ConVeY.\r\nMaximilian Weininger: The author has been partially
supported by DFG projects 383882557\r\nStatistical Unbounded Verification (SUV)
and 427755713 Group-By Objectives in Probabilistic\r\nVerification (GOPro)"
alternative_title:
- LIPIcs
article_number: '11'
article_processing_charge: No
arxiv: 1
author:
- first_name: Kush
full_name: Grover, Kush
last_name: Grover
- first_name: Jan
full_name: Kretinsky, Jan
id: 44CEF464-F248-11E8-B48F-1D18A9856A87
last_name: Kretinsky
orcid: 0000-0002-8122-2881
- first_name: Tobias
full_name: Meggendorfer, Tobias
id: b21b0c15-30a2-11eb-80dc-f13ca25802e1
last_name: Meggendorfer
orcid: 0000-0002-1712-2165
- first_name: Maimilian
full_name: Weininger, Maimilian
last_name: Weininger
citation:
ama: 'Grover K, Kretinsky J, Meggendorfer T, Weininger M. Anytime guarantees for
reachability in uncountable Markov decision processes. In: 33rd International
Conference on Concurrency Theory . Vol 243. Schloss Dagstuhl - Leibniz-Zentrum
für Informatik; 2022. doi:10.4230/LIPIcs.CONCUR.2022.11'
apa: 'Grover, K., Kretinsky, J., Meggendorfer, T., & Weininger, M. (2022). Anytime
guarantees for reachability in uncountable Markov decision processes. In 33rd
International Conference on Concurrency Theory (Vol. 243). Warsaw, Poland:
Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.CONCUR.2022.11'
chicago: Grover, Kush, Jan Kretinsky, Tobias Meggendorfer, and Maimilian Weininger.
“Anytime Guarantees for Reachability in Uncountable Markov Decision Processes.”
In 33rd International Conference on Concurrency Theory , Vol. 243. Schloss
Dagstuhl - Leibniz-Zentrum für Informatik, 2022. https://doi.org/10.4230/LIPIcs.CONCUR.2022.11.
ieee: K. Grover, J. Kretinsky, T. Meggendorfer, and M. Weininger, “Anytime guarantees
for reachability in uncountable Markov decision processes,” in 33rd International
Conference on Concurrency Theory , Warsaw, Poland, 2022, vol. 243.
ista: 'Grover K, Kretinsky J, Meggendorfer T, Weininger M. 2022. Anytime guarantees
for reachability in uncountable Markov decision processes. 33rd International
Conference on Concurrency Theory . CONCUR: Conference on Concurrency Theory, LIPIcs,
vol. 243, 11.'
mla: Grover, Kush, et al. “Anytime Guarantees for Reachability in Uncountable Markov
Decision Processes.” 33rd International Conference on Concurrency Theory ,
vol. 243, 11, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022, doi:10.4230/LIPIcs.CONCUR.2022.11.
short: K. Grover, J. Kretinsky, T. Meggendorfer, M. Weininger, in:, 33rd International
Conference on Concurrency Theory , Schloss Dagstuhl - Leibniz-Zentrum für Informatik,
2022.
conference:
end_date: 2022-09-16
location: Warsaw, Poland
name: 'CONCUR: Conference on Concurrency Theory'
start_date: 2022-09-13
date_created: 2023-03-28T08:09:32Z
date_published: 2022-09-15T00:00:00Z
date_updated: 2023-09-26T10:43:30Z
day: '15'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.4230/LIPIcs.CONCUR.2022.11
external_id:
arxiv:
- '2008.04824'
file:
- access_level: open_access
checksum: e282e43d3ae0ba6e067b72f4583e13c0
content_type: application/pdf
creator: dernst
date_created: 2023-09-26T10:43:15Z
date_updated: 2023-09-26T10:43:15Z
file_id: '14372'
file_name: 2022_LIPIcS_Grover.pdf
file_size: 960036
relation: main_file
success: 1
file_date_updated: 2023-09-26T10:43:15Z
has_accepted_license: '1'
intvolume: ' 243'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '09'
oa: 1
oa_version: Published Version
publication: '33rd International Conference on Concurrency Theory '
publication_identifier:
issn:
- 1868-8969
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
scopus_import: '1'
status: public
title: Anytime guarantees for reachability in uncountable Markov decision processes
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: 243
year: '2022'
...
---
_id: '12776'
abstract:
- lang: eng
text: An improved asymptotic formula is established for the number of rational points
of bounded height on the split smooth del Pezzo surface of degree 5. The proof
uses the five conic bundle structures on the surface.
acknowledgement: This work was begun while the author was participating in the programme
on "Diophantine equations" at the Hausdorff Research Institute for Mathematics in
Bonn in 2009. The hospitality and financial support of the institute is gratefully
acknowledged. The idea of using conic bundles to study the split del Pezzo surface
of degree 5 was explained to the author by Professor Salberger. The author is very
grateful to him for his input into this project and also to Shuntaro Yamagishi for
many useful comments on an earlier version of this manuscript. While working on
this paper the author was supported by FWF grant P32428-N35.
article_processing_charge: No
article_type: original
author:
- first_name: Timothy D
full_name: Browning, Timothy D
id: 35827D50-F248-11E8-B48F-1D18A9856A87
last_name: Browning
orcid: 0000-0002-8314-0177
citation:
ama: Browning TD. Revisiting the Manin–Peyre conjecture for the split del Pezzo
surface of degree 5. New York Journal of Mathematics. 2022;28:1193-1229.
apa: Browning, T. D. (2022). Revisiting the Manin–Peyre conjecture for the split
del Pezzo surface of degree 5. New York Journal of Mathematics. State University
of New York.
chicago: Browning, Timothy D. “Revisiting the Manin–Peyre Conjecture for the Split
Del Pezzo Surface of Degree 5.” New York Journal of Mathematics. State
University of New York, 2022.
ieee: T. D. Browning, “Revisiting the Manin–Peyre conjecture for the split del Pezzo
surface of degree 5,” New York Journal of Mathematics, vol. 28. State University
of New York, pp. 1193–1229, 2022.
ista: Browning TD. 2022. Revisiting the Manin–Peyre conjecture for the split del
Pezzo surface of degree 5. New York Journal of Mathematics. 28, 1193–1229.
mla: Browning, Timothy D. “Revisiting the Manin–Peyre Conjecture for the Split Del
Pezzo Surface of Degree 5.” New York Journal of Mathematics, vol. 28, State
University of New York, 2022, pp. 1193–229.
short: T.D. Browning, New York Journal of Mathematics 28 (2022) 1193–1229.
date_created: 2023-03-28T09:21:09Z
date_published: 2022-08-24T00:00:00Z
date_updated: 2023-10-18T07:59:13Z
day: '24'
ddc:
- '510'
department:
- _id: TiBr
file:
- access_level: open_access
checksum: c01e8291794a1bdb7416aa103cb68ef8
content_type: application/pdf
creator: dernst
date_created: 2023-03-30T07:09:35Z
date_updated: 2023-03-30T07:09:35Z
file_id: '12778'
file_name: 2022_NYJM_Browning.pdf
file_size: 897267
relation: main_file
success: 1
file_date_updated: 2023-03-30T07:09:35Z
has_accepted_license: '1'
intvolume: ' 28'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
page: 1193 - 1229
project:
- _id: 26AEDAB2-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P32428
name: New frontiers of the Manin conjecture
publication: New York Journal of Mathematics
publication_identifier:
issn:
- 1076-9803
publication_status: published
publisher: State University of New York
quality_controlled: '1'
status: public
title: Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree
5
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: 28
year: '2022'
...
---
_id: '12780'
abstract:
- lang: eng
text: "The ability to scale out training workloads has been one of the key performance
enablers of deep learning. The main scaling approach is data-parallel GPU-based
training, which has been boosted by hardware and software support for highly efficient
point-to-point communication, and in particular via hardware bandwidth over-provisioning.
Overprovisioning comes at a cost: there is an order of magnitude price difference
between \"cloud-grade\" servers with such support, relative to their popular \"consumer-grade\"
counterparts, although single server-grade and consumer-grade GPUs can have similar
computational envelopes.\r\n\r\nIn this paper, we show that the costly hardware
overprovisioning approach can be supplanted via algorithmic and system design,
and propose a framework called CGX, which provides efficient software support
for compressed communication in ML applications, for both multi-GPU single-node
training, as well as larger-scale multi-node training. CGX is based on two technical
advances: At the system level, it relies on a re-developed communication stack
for ML frameworks, which provides flexible, highly-efficient support for compressed
communication. At the application level, it provides seamless, parameter-free
integration with popular frameworks, so that end-users do not have to modify training
recipes, nor significant training code. This is complemented by a layer-wise adaptive
compression technique which dynamically balances compression gains with accuracy
preservation. CGX integrates with popular ML frameworks, providing up to 3X speedups
for multi-GPU nodes based on commodity hardware, and order-of-magnitude improvements
in the multi-node setting, with negligible impact on accuracy."
acknowledgement: The authors sincerely thank Nikoli Dryden, Tal Ben-Nun, Torsten Hoefler
and Bapi Chatterjee for useful discussions throughout the development of this project.
article_processing_charge: Yes (via OA deal)
arxiv: 1
author:
- first_name: Ilia
full_name: Markov, Ilia
id: D0CF4148-C985-11E9-8066-0BDEE5697425
last_name: Markov
- first_name: Hamidreza
full_name: Ramezanikebrya, Hamidreza
last_name: Ramezanikebrya
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Markov I, Ramezanikebrya H, Alistarh D-A. CGX: Adaptive system support for
communication-efficient deep learning. In: Proceedings of the 23rd ACM/IFIP
International Middleware Conference. Association for Computing Machinery;
2022:241-254. doi:10.1145/3528535.3565248'
apa: 'Markov, I., Ramezanikebrya, H., & Alistarh, D.-A. (2022). CGX: Adaptive
system support for communication-efficient deep learning. In Proceedings of
the 23rd ACM/IFIP International Middleware Conference (pp. 241–254). Quebec,
QC, Canada: Association for Computing Machinery. https://doi.org/10.1145/3528535.3565248'
chicago: 'Markov, Ilia, Hamidreza Ramezanikebrya, and Dan-Adrian Alistarh. “CGX:
Adaptive System Support for Communication-Efficient Deep Learning.” In Proceedings
of the 23rd ACM/IFIP International Middleware Conference, 241–54. Association
for Computing Machinery, 2022. https://doi.org/10.1145/3528535.3565248.'
ieee: 'I. Markov, H. Ramezanikebrya, and D.-A. Alistarh, “CGX: Adaptive system support
for communication-efficient deep learning,” in Proceedings of the 23rd ACM/IFIP
International Middleware Conference, Quebec, QC, Canada, 2022, pp. 241–254.'
ista: 'Markov I, Ramezanikebrya H, Alistarh D-A. 2022. CGX: Adaptive system support
for communication-efficient deep learning. Proceedings of the 23rd ACM/IFIP International
Middleware Conference. Middleware: International Middleware Conference, 241–254.'
mla: 'Markov, Ilia, et al. “CGX: Adaptive System Support for Communication-Efficient
Deep Learning.” Proceedings of the 23rd ACM/IFIP International Middleware Conference,
Association for Computing Machinery, 2022, pp. 241–54, doi:10.1145/3528535.3565248.'
short: I. Markov, H. Ramezanikebrya, D.-A. Alistarh, in:, Proceedings of the 23rd
ACM/IFIP International Middleware Conference, Association for Computing Machinery,
2022, pp. 241–254.
conference:
end_date: 2022-11-11
location: Quebec, QC, Canada
name: 'Middleware: International Middleware Conference'
start_date: 2022-11-07
date_created: 2023-03-31T06:17:00Z
date_published: 2022-11-01T00:00:00Z
date_updated: 2023-04-03T06:21:04Z
day: '01'
ddc:
- '000'
department:
- _id: DaAl
doi: 10.1145/3528535.3565248
external_id:
arxiv:
- '2111.08617'
file:
- access_level: open_access
checksum: 1a397746235f245da5468819247ff663
content_type: application/pdf
creator: dernst
date_created: 2023-04-03T06:17:58Z
date_updated: 2023-04-03T06:17:58Z
file_id: '12795'
file_name: 2022_ACMMiddleware_Markov.pdf
file_size: 1514169
relation: main_file
success: 1
file_date_updated: 2023-04-03T06:17:58Z
has_accepted_license: '1'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: 241-254
publication: Proceedings of the 23rd ACM/IFIP International Middleware Conference
publication_identifier:
isbn:
- '9781450393409'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
status: public
title: 'CGX: Adaptive system support for communication-efficient deep learning'
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
year: '2022'
...
---
_id: '12793'
abstract:
- lang: eng
text: "Let F be a global function field with constant field Fq. Let G be a reductive
group over Fq. We establish a variant of Arthur's truncated kernel for G and for
its Lie algebra which generalizes Arthur's original construction. We establish
a coarse geometric expansion for our variant truncation.\r\nAs applications, we
consider some existence and uniqueness problems of some cuspidal automorphic representations
for the functions field of the projective line P1Fq with two points of ramifications."
acknowledgement: 'I’d like to thank Prof. Chaudouard for introducing me to this area.
I’d like to thank Prof. Harris for asking me the question that makes Section 10
possible. I’m grateful for the support of Prof. Hausel and IST Austria. The author
was funded by an ISTplus fellowship: This project has received funding from the
European Union’s Horizon 2020 research and innovation programme under the Marie
Skłodowska-Curie Grant Agreement No. 754411.'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Hongjie
full_name: Yu, Hongjie
id: 3D7DD9BE-F248-11E8-B48F-1D18A9856A87
last_name: Yu
orcid: 0000-0001-5128-7126
citation:
ama: Yu H. A coarse geometric expansion of a variant of Arthur’s truncated traces
and some applications. Pacific Journal of Mathematics. 2022;321(1):193-237.
doi:10.2140/pjm.2022.321.193
apa: Yu, H. (2022). A coarse geometric expansion of a variant of Arthur’s truncated
traces and some applications. Pacific Journal of Mathematics. Mathematical
Sciences Publishers. https://doi.org/10.2140/pjm.2022.321.193
chicago: Yu, Hongjie. “ A Coarse Geometric Expansion of a Variant of Arthur’s Truncated
Traces and Some Applications.” Pacific Journal of Mathematics. Mathematical
Sciences Publishers, 2022. https://doi.org/10.2140/pjm.2022.321.193.
ieee: H. Yu, “ A coarse geometric expansion of a variant of Arthur’s truncated traces
and some applications,” Pacific Journal of Mathematics, vol. 321, no. 1.
Mathematical Sciences Publishers, pp. 193–237, 2022.
ista: Yu H. 2022. A coarse geometric expansion of a variant of Arthur’s truncated
traces and some applications. Pacific Journal of Mathematics. 321(1), 193–237.
mla: Yu, Hongjie. “ A Coarse Geometric Expansion of a Variant of Arthur’s Truncated
Traces and Some Applications.” Pacific Journal of Mathematics, vol. 321,
no. 1, Mathematical Sciences Publishers, 2022, pp. 193–237, doi:10.2140/pjm.2022.321.193.
short: H. Yu, Pacific Journal of Mathematics 321 (2022) 193–237.
date_created: 2023-04-02T22:01:11Z
date_published: 2022-08-29T00:00:00Z
date_updated: 2023-08-04T10:42:38Z
day: '29'
department:
- _id: TaHa
doi: 10.2140/pjm.2022.321.193
ec_funded: 1
external_id:
arxiv:
- '2109.10245'
isi:
- '000954466300006'
intvolume: ' 321'
isi: 1
issue: '1'
keyword:
- Arthur–Selberg trace formula
- cuspidal automorphic representations
- global function fields
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2109.10245
month: '08'
oa: 1
oa_version: Preprint
page: 193-237
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Pacific Journal of Mathematics
publication_identifier:
eissn:
- 1945-5844
issn:
- 0030-8730
publication_status: published
publisher: Mathematical Sciences Publishers
quality_controlled: '1'
scopus_import: '1'
status: public
title: ' A coarse geometric expansion of a variant of Arthur''s truncated traces and
some applications'
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 321
year: '2022'
...
---
_id: '12860'
abstract:
- lang: eng
text: 'Memorization of the relation between entities in a dataset can lead to privacy
issues when using a trained model for question answering. We introduce Relational
Memorization (RM) to understand, quantify and control this phenomenon. While bounding
general memorization can have detrimental effects on the performance of a trained
model, bounding RM does not prevent effective learning. The difference is most
pronounced when the data distribution is long-tailed, with many queries having
only few training examples: Impeding general memorization prevents effective learning,
while impeding only relational memorization still allows learning general properties
of the underlying concepts. We formalize the notion of Relational Privacy (RP)
and, inspired by Differential Privacy (DP), we provide a possible definition of
Differential Relational Privacy (DrP). These notions can be used to describe and
compute bounds on the amount of RM in a trained model. We illustrate Relational
Privacy concepts in experiments with large-scale models for Question Answering.'
article_number: '2203.16701'
article_processing_charge: No
arxiv: 1
author:
- first_name: Simone
full_name: Bombari, Simone
id: ca726dda-de17-11ea-bc14-f9da834f63aa
last_name: Bombari
- first_name: Alessandro
full_name: Achille, Alessandro
last_name: Achille
- first_name: Zijian
full_name: Wang, Zijian
last_name: Wang
- first_name: Yu-Xiang
full_name: Wang, Yu-Xiang
last_name: Wang
- first_name: Yusheng
full_name: Xie, Yusheng
last_name: Xie
- first_name: Kunwar Yashraj
full_name: Singh, Kunwar Yashraj
last_name: Singh
- first_name: Srikar
full_name: Appalaraju, Srikar
last_name: Appalaraju
- first_name: Vijay
full_name: Mahadevan, Vijay
last_name: Mahadevan
- first_name: Stefano
full_name: Soatto, Stefano
last_name: Soatto
citation:
ama: Bombari S, Achille A, Wang Z, et al. Towards differential relational privacy
and its use in question answering. arXiv. doi:10.48550/arXiv.2203.16701
apa: Bombari, S., Achille, A., Wang, Z., Wang, Y.-X., Xie, Y., Singh, K. Y., … Soatto,
S. (n.d.). Towards differential relational privacy and its use in question answering.
arXiv. https://doi.org/10.48550/arXiv.2203.16701
chicago: Bombari, Simone, Alessandro Achille, Zijian Wang, Yu-Xiang Wang, Yusheng
Xie, Kunwar Yashraj Singh, Srikar Appalaraju, Vijay Mahadevan, and Stefano Soatto.
“Towards Differential Relational Privacy and Its Use in Question Answering.” ArXiv,
n.d. https://doi.org/10.48550/arXiv.2203.16701.
ieee: S. Bombari et al., “Towards differential relational privacy and its
use in question answering,” arXiv. .
ista: Bombari S, Achille A, Wang Z, Wang Y-X, Xie Y, Singh KY, Appalaraju S, Mahadevan
V, Soatto S. Towards differential relational privacy and its use in question answering.
arXiv, 2203.16701.
mla: Bombari, Simone, et al. “Towards Differential Relational Privacy and Its Use
in Question Answering.” ArXiv, 2203.16701, doi:10.48550/arXiv.2203.16701.
short: S. Bombari, A. Achille, Z. Wang, Y.-X. Wang, Y. Xie, K.Y. Singh, S. Appalaraju,
V. Mahadevan, S. Soatto, ArXiv (n.d.).
date_created: 2023-04-23T16:11:48Z
date_published: 2022-03-30T00:00:00Z
date_updated: 2023-04-25T07:34:49Z
day: '30'
department:
- _id: GradSch
- _id: MaMo
doi: 10.48550/arXiv.2203.16701
external_id:
arxiv:
- '2203.16701'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2203.16701
month: '03'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Towards differential relational privacy and its use in question answering
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '13064'
abstract:
- lang: eng
text: Genetically informed, deep-phenotyped biobanks are an important research resource
and it is imperative that the most powerful, versatile, and efficient analysis
approaches are used. Here, we apply our recently developed Bayesian grouped mixture
of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest
genomic prediction accuracy reported to date across 21 heritable traits. When
compared to other approaches, GMRM accuracy was greater than annotation prediction
models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%),
respectively, and was 18% (SE 3%) greater than a baseline BayesR model without
single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage
disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy
R 2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h SNP
2 . We then extend our GMRM prediction model to provide mixed-linear model association
(MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which
increased the independent loci detected to 16,162 in unrelated UK Biobank individuals,
compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase,
respectively. The average χ2 value of the leading markers increased by 15.24 (SE
0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR
model across the traits. Thus, we show that modeling genetic associations accounting
for MAF and LD differences among SNP markers, and incorporating prior knowledge
of genomic function, is important for both genomic prediction and discovery in
large-scale individual-level studies.
article_processing_charge: No
author:
- first_name: Etienne
full_name: Orliac, Etienne
last_name: Orliac
- first_name: Daniel
full_name: Trejo Banos, Daniel
last_name: Trejo Banos
- first_name: Sven
full_name: Ojavee, Sven
last_name: Ojavee
- first_name: Kristi
full_name: Läll, Kristi
last_name: Läll
- first_name: Reedik
full_name: Mägi, Reedik
last_name: Mägi
- first_name: Peter
full_name: Visscher, Peter
last_name: Visscher
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
citation:
ama: Orliac E, Trejo Banos D, Ojavee S, et al. Improving genome-wide association
discovery and genomic prediction accuracy in biobank data. 2022. doi:10.5061/DRYAD.GTHT76HMZ
apa: Orliac, E., Trejo Banos, D., Ojavee, S., Läll, K., Mägi, R., Visscher, P.,
& Robinson, M. R. (2022). Improving genome-wide association discovery and
genomic prediction accuracy in biobank data. Dryad. https://doi.org/10.5061/DRYAD.GTHT76HMZ
chicago: Orliac, Etienne, Daniel Trejo Banos, Sven Ojavee, Kristi Läll, Reedik Mägi,
Peter Visscher, and Matthew Richard Robinson. “Improving Genome-Wide Association
Discovery and Genomic Prediction Accuracy in Biobank Data.” Dryad, 2022. https://doi.org/10.5061/DRYAD.GTHT76HMZ.
ieee: E. Orliac et al., “Improving genome-wide association discovery and
genomic prediction accuracy in biobank data.” Dryad, 2022.
ista: Orliac E, Trejo Banos D, Ojavee S, Läll K, Mägi R, Visscher P, Robinson MR.
2022. Improving genome-wide association discovery and genomic prediction accuracy
in biobank data, Dryad, 10.5061/DRYAD.GTHT76HMZ.
mla: Orliac, Etienne, et al. Improving Genome-Wide Association Discovery and
Genomic Prediction Accuracy in Biobank Data. Dryad, 2022, doi:10.5061/DRYAD.GTHT76HMZ.
short: E. Orliac, D. Trejo Banos, S. Ojavee, K. Läll, R. Mägi, P. Visscher, M.R.
Robinson, (2022).
date_created: 2023-05-23T16:28:13Z
date_published: 2022-09-02T00:00:00Z
date_updated: 2023-08-03T12:40:37Z
day: '02'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.5061/DRYAD.GTHT76HMZ
license: https://creativecommons.org/publicdomain/zero/1.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.5061/dryad.gtht76hmz
month: '09'
oa: 1
oa_version: Published Version
publisher: Dryad
related_material:
record:
- id: '11733'
relation: used_in_publication
status: public
status: public
title: Improving genome-wide association discovery and genomic prediction accuracy
in biobank data
tmp:
image: /images/cc_0.png
legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
name: Creative Commons Public Domain Dedication (CC0 1.0)
short: CC0 (1.0)
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '13066'
abstract:
- lang: eng
text: Chromosomal inversions have been shown to play a major role in local adaptation
by suppressing recombination between alternative arrangements and maintaining
beneficial allele combinations. However, so far, their importance relative to
the remaining genome remains largely unknown. Understanding the genetic architecture
of adaptation requires better estimates of how loci of different effect sizes
contribute to phenotypic variation. Here, we used three Swedish islands where
the marine snail Littorina saxatilis has repeatedly evolved into two distinct
ecotypes along a habitat transition. We estimated the contribution of inversion
polymorphisms to phenotypic divergence while controlling for polygenic effects
in the remaining genome using a quantitative genetics framework. We confirmed
the importance of inversions but showed that contributions of loci outside inversions
are of similar magnitude, with variable proportions dependent on the trait and
the population. Some inversions showed consistent effects across all sites, whereas
others exhibited site-specific effects, indicating that the genomic basis for
replicated phenotypic divergence is only partly shared. The contributions of sexual
dimorphism as well as environmental factors to phenotypic variation were significant
but minor compared to inversions and polygenic background. Overall, this integrated
approach provides insight into the multiple mechanisms contributing to parallel
phenotypic divergence.
article_processing_charge: No
author:
- first_name: Eva
full_name: Koch, Eva
last_name: Koch
- first_name: Mark
full_name: Ravinet, Mark
last_name: Ravinet
- first_name: Anja M
full_name: Westram, Anja M
id: 3C147470-F248-11E8-B48F-1D18A9856A87
last_name: Westram
orcid: 0000-0003-1050-4969
- first_name: Kerstin
full_name: Jonannesson, Kerstin
last_name: Jonannesson
- first_name: Roger
full_name: Butlin, Roger
last_name: Butlin
citation:
ama: 'Koch E, Ravinet M, Westram AM, Jonannesson K, Butlin R. Data from: Genetic
architecture of repeated phenotypic divergence in Littorina saxatilis ecotype
evolution. 2022. doi:10.5061/DRYAD.M905QFV4B'
apa: 'Koch, E., Ravinet, M., Westram, A. M., Jonannesson, K., & Butlin, R. (2022).
Data from: Genetic architecture of repeated phenotypic divergence in Littorina
saxatilis ecotype evolution. Dryad. https://doi.org/10.5061/DRYAD.M905QFV4B'
chicago: 'Koch, Eva, Mark Ravinet, Anja M Westram, Kerstin Jonannesson, and Roger
Butlin. “Data from: Genetic Architecture of Repeated Phenotypic Divergence in
Littorina Saxatilis Ecotype Evolution.” Dryad, 2022. https://doi.org/10.5061/DRYAD.M905QFV4B.'
ieee: 'E. Koch, M. Ravinet, A. M. Westram, K. Jonannesson, and R. Butlin, “Data
from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis
ecotype evolution.” Dryad, 2022.'
ista: 'Koch E, Ravinet M, Westram AM, Jonannesson K, Butlin R. 2022. Data from:
Genetic architecture of repeated phenotypic divergence in Littorina saxatilis
ecotype evolution, Dryad, 10.5061/DRYAD.M905QFV4B.'
mla: 'Koch, Eva, et al. Data from: Genetic Architecture of Repeated Phenotypic
Divergence in Littorina Saxatilis Ecotype Evolution. Dryad, 2022, doi:10.5061/DRYAD.M905QFV4B.'
short: E. Koch, M. Ravinet, A.M. Westram, K. Jonannesson, R. Butlin, (2022).
date_created: 2023-05-23T16:33:12Z
date_published: 2022-07-28T00:00:00Z
date_updated: 2023-08-04T09:42:10Z
day: '28'
ddc:
- '570'
department:
- _id: NiBa
doi: 10.5061/DRYAD.M905QFV4B
main_file_link:
- open_access: '1'
url: https://doi.org/10.5061/dryad.m905qfv4b
month: '07'
oa: 1
oa_version: Published Version
publisher: Dryad
related_material:
record:
- id: '12247'
relation: used_in_publication
status: public
status: public
title: 'Data from: Genetic architecture of repeated phenotypic divergence in Littorina
saxatilis ecotype evolution'
tmp:
image: /images/cc_0.png
legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
name: Creative Commons Public Domain Dedication (CC0 1.0)
short: CC0 (1.0)
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '13076'
abstract:
- lang: eng
text: "The source code for replicating experiments presented in the paper.\r\n\r\nThe
implementation of the designed priority schedulers can be found in Galois-2.2.1/include/Galois/WorkList/:\r\nStealingMultiQueue.h
is the StealingMultiQueue.\r\nMQOptimized/ contains MQ Optimized variants.\r\n\r\nWe
provide images that contain all the dependencies and datasets. Images can be pulled
from npostnikova/mq-based-schedulers repository, or downloaded from Zenodo. See
readme for more detail."
article_processing_charge: No
author:
- first_name: Anastasiia
full_name: Postnikova, Anastasiia
last_name: Postnikova
- first_name: Nikita
full_name: Koval, Nikita
id: 2F4DB10C-F248-11E8-B48F-1D18A9856A87
last_name: Koval
- first_name: Giorgi
full_name: Nadiradze, Giorgi
id: 3279A00C-F248-11E8-B48F-1D18A9856A87
last_name: Nadiradze
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: Postnikova A, Koval N, Nadiradze G, Alistarh D-A. Multi-queues can be state-of-the-art
priority schedulers. 2022. doi:10.5281/ZENODO.5733408
apa: Postnikova, A., Koval, N., Nadiradze, G., & Alistarh, D.-A. (2022). Multi-queues
can be state-of-the-art priority schedulers. Zenodo. https://doi.org/10.5281/ZENODO.5733408
chicago: Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian
Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.” Zenodo,
2022. https://doi.org/10.5281/ZENODO.5733408.
ieee: A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can
be state-of-the-art priority schedulers.” Zenodo, 2022.
ista: Postnikova A, Koval N, Nadiradze G, Alistarh D-A. 2022. Multi-queues can be
state-of-the-art priority schedulers, Zenodo, 10.5281/ZENODO.5733408.
mla: Postnikova, Anastasiia, et al. Multi-Queues Can Be State-of-the-Art Priority
Schedulers. Zenodo, 2022, doi:10.5281/ZENODO.5733408.
short: A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, (2022).
date_created: 2023-05-23T17:05:40Z
date_published: 2022-01-03T00:00:00Z
date_updated: 2023-08-03T06:48:34Z
day: '03'
ddc:
- '510'
department:
- _id: DaAl
doi: 10.5281/ZENODO.5733408
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.5813846
month: '01'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
link:
- relation: software
url: https://github.com/npostnikova/mq-based-schedulers/tree/v1.1
record:
- id: '11180'
relation: used_in_publication
status: public
status: public
title: Multi-queues can be state-of-the-art priority schedulers
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '13239'
abstract:
- lang: eng
text: Brains are thought to engage in predictive learning - learning to predict
upcoming stimuli - to construct an internal model of their environment. This is
especially notable for spatial navigation, as first described by Tolman’s latent
learning tasks. However, predictive learning has also been observed in sensory
cortex, in settings unrelated to spatial navigation. Apart from normative frameworks
such as active inference or efficient coding, what could be the utility of learning
to predict the patterns of occurrence of correlated stimuli? Here we show that
prediction, and thereby the construction of an internal model of sequential stimuli,
can bootstrap the learning process of a working memory task in a recurrent neural
network. We implemented predictive learning alongside working memory match-tasks,
and networks emerged to solve the prediction task first by encoding information
across time to predict upcoming stimuli, and then eavesdropped on this solution
to solve the matching task. Eavesdropping was most beneficial when neural resources
were limited. Hence, predictive learning acts as a general neural mechanism to
learn to store sensory information that can later be essential for working memory
tasks.
acknowledgement: "The authors would like to thank members of the Vogels lab and Manohar
lab, as well as Adam Packer, Andrew Saxe, Stefano Sarao Mannelli and Jacob Bakermans
for fruitful discussions and comments on earlier versions of the manuscript.\r\nTLvdP
was supported by funding from the Biotechnology and Biological Sciences Research
Council (BBSRC) [grant number BB/M011224/1]. TPV was supported by an ERC Consolidator
Grant (SYNAPSEEK). SGM was funded by a MRC Clinician Scientist Fellowship MR/P00878X
and Leverhulme Grant RPG-2018-310."
article_processing_charge: No
author:
- first_name: Thijs L.
full_name: Van Der Plas, Thijs L.
last_name: Van Der Plas
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Sanjay G.
full_name: Manohar, Sanjay G.
last_name: Manohar
citation:
ama: 'Van Der Plas TL, Vogels TP, Manohar SG. Predictive learning enables neural
networks to learn complex working memory tasks. In: Proceedings of Machine
Learning Research. Vol 199. ML Research Press; 2022:518-531.'
apa: Van Der Plas, T. L., Vogels, T. P., & Manohar, S. G. (2022). Predictive
learning enables neural networks to learn complex working memory tasks. In Proceedings
of Machine Learning Research (Vol. 199, pp. 518–531). ML Research Press.
chicago: Van Der Plas, Thijs L., Tim P Vogels, and Sanjay G. Manohar. “Predictive
Learning Enables Neural Networks to Learn Complex Working Memory Tasks.” In Proceedings
of Machine Learning Research, 199:518–31. ML Research Press, 2022.
ieee: T. L. Van Der Plas, T. P. Vogels, and S. G. Manohar, “Predictive learning
enables neural networks to learn complex working memory tasks,” in Proceedings
of Machine Learning Research, 2022, vol. 199, pp. 518–531.
ista: Van Der Plas TL, Vogels TP, Manohar SG. 2022. Predictive learning enables
neural networks to learn complex working memory tasks. Proceedings of Machine
Learning Research. vol. 199, 518–531.
mla: Van Der Plas, Thijs L., et al. “Predictive Learning Enables Neural Networks
to Learn Complex Working Memory Tasks.” Proceedings of Machine Learning Research,
vol. 199, ML Research Press, 2022, pp. 518–31.
short: T.L. Van Der Plas, T.P. Vogels, S.G. Manohar, in:, Proceedings of Machine
Learning Research, ML Research Press, 2022, pp. 518–531.
date_created: 2023-07-16T22:01:12Z
date_published: 2022-12-01T00:00:00Z
date_updated: 2023-07-18T06:36:28Z
day: '01'
ddc:
- '000'
department:
- _id: TiVo
ec_funded: 1
file:
- access_level: open_access
checksum: 7530a93ef42e10b4db1e5e4b69796e93
content_type: application/pdf
creator: dernst
date_created: 2023-07-18T06:32:38Z
date_updated: 2023-07-18T06:32:38Z
file_id: '13243'
file_name: 2022_PMLR_vanderPlas.pdf
file_size: 585135
relation: main_file
success: 1
file_date_updated: 2023-07-18T06:32:38Z
has_accepted_license: '1'
intvolume: ' 199'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: 518-531
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
publication: Proceedings of Machine Learning Research
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Predictive learning enables neural networks to learn complex working memory
tasks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 199
year: '2022'
...
---
_id: '13240'
abstract:
- lang: eng
text: Ustilago maydis is a biotrophic phytopathogenic fungus that causes corn smut
disease. As a well-established model system, U. maydis is genetically fully accessible
with large omics datasets available and subject to various biological questions
ranging from DNA-repair, RNA-transport, and protein secretion to disease biology.
For many genetic approaches, tight control of transgene regulation is important.
Here we established an optimised version of the Tetracycline-ON (TetON) system
for U. maydis. We demonstrate the Tetracycline concentration-dependent expression
of fluorescent protein transgenes and the system’s suitability for the induced
expression of the toxic protein BCL2 Associated X-1 (Bax1). The Golden Gate compatible
vector system contains a native minimal promoter from the mating factor a-1 encoding
gene, mfa with ten copies of the tet-regulated operator (tetO) and a codon optimised
Tet-repressor (tetR*) which is translationally fused to the native transcriptional
corepressor Mql1 (UMAG_05501). The metabolism-independent transcriptional regulator
system is functional both, in liquid culture as well as on solid media in the
presence of the inducer and can become a useful tool for toxin-antitoxin studies,
identification of antifungal proteins, and to study functions of toxic gene products
in Ustilago maydis.
acknowledgement: "The research leading to these results received funding from the
European Research Council under the European Union’s Seventh Framework Programme
ERC-2013-STG (grant agreement: 335691), the Austrian Science Fund (I 3033-B22),
the Austrian Academy of Sciences, and the Deutsche Forschungsgemeinschaft (DFG,
German Research Foundation) under Germany's Excellence Strategy EXC-2070-390732324
(PhenoRob) and DFG grant (DJ 64/5-1).\r\nWe would like to thank the GMI/IMBA/IMP
core facilities for their excellent technical support. We would like to acknowledge
Dr. Sinéad A. O’Sullivan from DZNE, University of Bonn for providing anti-GFP antibodies.
The authors are thankful to the Excellence University of Bonn for providing infrastructure
and instrumentation facilities at the INRES-Plant Pathology department."
article_number: '1029114'
article_processing_charge: Yes
article_type: original
author:
- first_name: Kishor D.
full_name: Ingole, Kishor D.
last_name: Ingole
- first_name: Nithya
full_name: Nagarajan, Nithya
last_name: Nagarajan
- first_name: Simon
full_name: Uhse, Simon
last_name: Uhse
- first_name: Caterina
full_name: Giannini, Caterina
id: e3fdddd5-f6e0-11ea-865d-ca99ee6367f4
last_name: Giannini
- first_name: Armin
full_name: Djamei, Armin
last_name: Djamei
citation:
ama: Ingole KD, Nagarajan N, Uhse S, Giannini C, Djamei A. Tetracycline-controlled
(TetON) gene expression system for the smut fungus Ustilago maydis. Frontiers
in Fungal Biology. 2022;3. doi:10.3389/ffunb.2022.1029114
apa: Ingole, K. D., Nagarajan, N., Uhse, S., Giannini, C., & Djamei, A. (2022).
Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago
maydis. Frontiers in Fungal Biology. Frontiers Media. https://doi.org/10.3389/ffunb.2022.1029114
chicago: Ingole, Kishor D., Nithya Nagarajan, Simon Uhse, Caterina Giannini, and
Armin Djamei. “Tetracycline-Controlled (TetON) Gene Expression System for the
Smut Fungus Ustilago Maydis.” Frontiers in Fungal Biology. Frontiers Media,
2022. https://doi.org/10.3389/ffunb.2022.1029114.
ieee: K. D. Ingole, N. Nagarajan, S. Uhse, C. Giannini, and A. Djamei, “Tetracycline-controlled
(TetON) gene expression system for the smut fungus Ustilago maydis,” Frontiers
in Fungal Biology, vol. 3. Frontiers Media, 2022.
ista: Ingole KD, Nagarajan N, Uhse S, Giannini C, Djamei A. 2022. Tetracycline-controlled
(TetON) gene expression system for the smut fungus Ustilago maydis. Frontiers
in Fungal Biology. 3, 1029114.
mla: Ingole, Kishor D., et al. “Tetracycline-Controlled (TetON) Gene Expression
System for the Smut Fungus Ustilago Maydis.” Frontiers in Fungal Biology,
vol. 3, 1029114, Frontiers Media, 2022, doi:10.3389/ffunb.2022.1029114.
short: K.D. Ingole, N. Nagarajan, S. Uhse, C. Giannini, A. Djamei, Frontiers in
Fungal Biology 3 (2022).
date_created: 2023-07-16T22:01:12Z
date_published: 2022-10-19T00:00:00Z
date_updated: 2024-03-06T14:01:57Z
day: '19'
ddc:
- '579'
department:
- _id: JiFr
doi: 10.3389/ffunb.2022.1029114
file:
- access_level: open_access
checksum: 2254e0119c0749d6f7237084fefcece6
content_type: application/pdf
creator: dernst
date_created: 2023-07-17T11:46:34Z
date_updated: 2023-07-17T11:46:34Z
file_id: '13242'
file_name: 2023_FrontiersFungalBio_Ingole.pdf
file_size: 27966699
relation: main_file
success: 1
file_date_updated: 2023-07-17T11:46:34Z
has_accepted_license: '1'
intvolume: ' 3'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
publication: Frontiers in Fungal Biology
publication_identifier:
eissn:
- 2673-6128
publication_status: published
publisher: Frontiers Media
quality_controlled: '1'
scopus_import: '1'
status: public
title: Tetracycline-controlled (TetON) gene expression system for the smut fungus
Ustilago maydis
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: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 3
year: '2022'
...
---
_id: '13241'
abstract:
- lang: eng
text: Addressing fairness concerns about machine learning models is a crucial step
towards their long-term adoption in real-world automated systems. Many approaches
for training fair models from data have been developed and an implicit assumption
about such algorithms is that they are able to recover a fair model, despite potential
historical biases in the data. In this work we show a number of impossibility
results that indicate that there is no learning algorithm that can recover a fair
model when a proportion of the dataset is subject to arbitrary manipulations.
Specifically, we prove that there are situations in which an adversary can force
any learner to return a biased classifier, with or without degrading accuracy,
and that the strength of this bias increases for learning problems with underrepresented
protected groups in the data. Our results emphasize on the importance of studying
further data corruption models of various strength and of establishing stricter
data collection practices for fairness-aware learning.
acknowledgement: "This paper is a shortened, workshop version of Konstantinov and
Lampert (2021),\r\nhttps://arxiv.org/abs/2102.06004. For further results, including
an analysis of algorithms achieving the lower bounds from this paper, we refer to
the full version."
article_processing_charge: No
arxiv: 1
author:
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Konstantinov NH, Lampert C. On the impossibility of fairness-aware learning
from corrupted data. In: Proceedings of Machine Learning Research. Vol
171. ML Research Press; 2022:59-83.'
apa: Konstantinov, N. H., & Lampert, C. (2022). On the impossibility of fairness-aware
learning from corrupted data. In Proceedings of Machine Learning Research
(Vol. 171, pp. 59–83). ML Research Press.
chicago: Konstantinov, Nikola H, and Christoph Lampert. “On the Impossibility of
Fairness-Aware Learning from Corrupted Data.” In Proceedings of Machine Learning
Research, 171:59–83. ML Research Press, 2022.
ieee: N. H. Konstantinov and C. Lampert, “On the impossibility of fairness-aware
learning from corrupted data,” in Proceedings of Machine Learning Research,
2022, vol. 171, pp. 59–83.
ista: Konstantinov NH, Lampert C. 2022. On the impossibility of fairness-aware learning
from corrupted data. Proceedings of Machine Learning Research. vol. 171, 59–83.
mla: Konstantinov, Nikola H., and Christoph Lampert. “On the Impossibility of Fairness-Aware
Learning from Corrupted Data.” Proceedings of Machine Learning Research,
vol. 171, ML Research Press, 2022, pp. 59–83.
short: N.H. Konstantinov, C. Lampert, in:, Proceedings of Machine Learning Research,
ML Research Press, 2022, pp. 59–83.
date_created: 2023-07-16T22:01:13Z
date_published: 2022-12-01T00:00:00Z
date_updated: 2023-09-26T10:44:37Z
day: '01'
department:
- _id: ChLa
external_id:
arxiv:
- '2102.06004'
intvolume: ' 171'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2102.06004
month: '12'
oa: 1
oa_version: Preprint
page: 59-83
publication: Proceedings of Machine Learning Research
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
record:
- id: '10802'
relation: extended_version
status: public
scopus_import: '1'
status: public
title: On the impossibility of fairness-aware learning from corrupted data
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 171
year: '2022'
...
---
_id: '14093'
abstract:
- lang: eng
text: ' We propose a stochastic conditional gradient method (CGM) for minimizing
convex finite-sum objectives formed as a sum of smooth and non-smooth terms. Existing
CGM variants for this template either suffer from slow convergence rates, or require
carefully increasing the batch size over the course of the algorithm’s execution,
which leads to computing full gradients. In contrast, the proposed method, equipped
with a stochastic average gradient (SAG) estimator, requires only one sample per
iteration. Nevertheless, it guarantees fast convergence rates on par with more
sophisticated variance reduction techniques. In applications we put special emphasis
on problems with a large number of separable constraints. Such problems are prevalent
among semidefinite programming (SDP) formulations arising in machine learning
and theoretical computer science. We provide numerical experiments on matrix completion,
unsupervised clustering, and sparsest-cut SDPs. '
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Gideon
full_name: Dresdner, Gideon
last_name: Dresdner
- first_name: Maria-Luiza
full_name: Vladarean, Maria-Luiza
last_name: Vladarean
- first_name: Gunnar
full_name: Rätsch, Gunnar
last_name: Rätsch
- 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
- first_name: Alp
full_name: Yurtsever, Alp
last_name: Yurtsever
citation:
ama: 'Dresdner G, Vladarean M-L, Rätsch G, Locatello F, Cevher V, Yurtsever A. Faster
one-sample stochastic conditional gradient method for composite convex minimization.
In: Proceedings of the 25th International Conference on Artificial Intelligence
and Statistics. Vol 151. ML Research Press; 2022:8439-8457.'
apa: 'Dresdner, G., Vladarean, M.-L., Rätsch, G., Locatello, F., Cevher, V., &
Yurtsever, A. (2022). Faster one-sample stochastic conditional gradient method
for composite convex minimization. In Proceedings of the 25th International
Conference on Artificial Intelligence and Statistics (Vol. 151, pp. 8439–8457).
Virtual: ML Research Press.'
chicago: Dresdner, Gideon, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello,
Volkan Cevher, and Alp Yurtsever. “ Faster One-Sample Stochastic Conditional Gradient
Method for Composite Convex Minimization.” In Proceedings of the 25th International
Conference on Artificial Intelligence and Statistics, 151:8439–57. ML Research
Press, 2022.
ieee: G. Dresdner, M.-L. Vladarean, G. Rätsch, F. Locatello, V. Cevher, and A. Yurtsever,
“ Faster one-sample stochastic conditional gradient method for composite convex
minimization,” in Proceedings of the 25th International Conference on Artificial
Intelligence and Statistics, Virtual, 2022, vol. 151, pp. 8439–8457.
ista: 'Dresdner G, Vladarean M-L, Rätsch G, Locatello F, Cevher V, Yurtsever A.
2022. Faster one-sample stochastic conditional gradient method for composite
convex minimization. Proceedings of the 25th International Conference on Artificial
Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and
Statistics, PMLR, vol. 151, 8439–8457.'
mla: Dresdner, Gideon, et al. “ Faster One-Sample Stochastic Conditional Gradient
Method for Composite Convex Minimization.” Proceedings of the 25th International
Conference on Artificial Intelligence and Statistics, vol. 151, ML Research
Press, 2022, pp. 8439–57.
short: G. Dresdner, M.-L. Vladarean, G. Rätsch, F. Locatello, V. Cevher, A. Yurtsever,
in:, Proceedings of the 25th International Conference on Artificial Intelligence
and Statistics, ML Research Press, 2022, pp. 8439–8457.
conference:
end_date: 2022-03-30
location: Virtual
name: 'AISTATS: Conference on Artificial Intelligence and Statistics'
start_date: 2022-03-28
date_created: 2023-08-21T09:27:43Z
date_published: 2022-04-01T00:00:00Z
date_updated: 2023-09-06T10:28:17Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2202.13212'
intvolume: ' 151'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2202.13212
month: '04'
oa: 1
oa_version: Preprint
page: 8439-8457
publication: Proceedings of the 25th International Conference on Artificial Intelligence
and Statistics
publication_identifier:
issn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: ' Faster one-sample stochastic conditional gradient method for composite convex
minimization'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 151
year: '2022'
...
---
_id: '14106'
abstract:
- lang: eng
text: "We show that deep networks trained to satisfy demographic parity often do
so\r\nthrough a form of race or gender awareness, and that the more we force a
network\r\nto be fair, the more accurately we can recover race or gender from
the internal state\r\nof the network. Based on this observation, we investigate
an alternative fairness\r\napproach: we add a second classification head to the
network to explicitly predict\r\nthe protected attribute (such as race or gender)
alongside the original task. After\r\ntraining the two-headed network, we enforce
demographic parity by merging the\r\ntwo heads, creating a network with the same
architecture as the original network.\r\nWe establish a close relationship between
existing approaches and our approach\r\nby showing (1) that the decisions of a
fair classifier are well-approximated by our\r\napproach, and (2) that an unfair
and optimally accurate classifier can be recovered\r\nfrom a fair classifier and
our second head predicting the protected attribute. We use\r\nour explicit formulation
to argue that the existing fairness approaches, just as ours,\r\ndemonstrate disparate
treatment and that they are likely to be unlawful in a wide\r\nrange of scenarios
under US law."
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Michael
full_name: Lohaus, Michael
last_name: Lohaus
- first_name: Matthäus
full_name: Kleindessner, Matthäus
last_name: Kleindessner
- first_name: Krishnaram
full_name: Kenthapadi, Krishnaram
last_name: Kenthapadi
- 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: 'Lohaus M, Kleindessner M, Kenthapadi K, Locatello F, Russell C. Are two heads
the same as one? Identifying disparate treatment in fair neural networks. In:
36th Conference on Neural Information Processing Systems. Vol 35. Neural
Information Processing Systems Foundation; 2022:16548-16562.'
apa: 'Lohaus, M., Kleindessner, M., Kenthapadi, K., Locatello, F., & Russell,
C. (2022). Are two heads the same as one? Identifying disparate treatment in fair
neural networks. In 36th Conference on Neural Information Processing Systems
(Vol. 35, pp. 16548–16562). New Orleans, LA, United States: Neural Information
Processing Systems Foundation.'
chicago: Lohaus, Michael, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco
Locatello, and Chris Russell. “Are Two Heads the Same as One? Identifying Disparate
Treatment in Fair Neural Networks.” In 36th Conference on Neural Information
Processing Systems, 35:16548–62. Neural Information Processing Systems Foundation,
2022.
ieee: M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, and C. Russell, “Are
two heads the same as one? Identifying disparate treatment in fair neural networks,”
in 36th Conference on Neural Information Processing Systems, New Orleans,
LA, United States, 2022, vol. 35, pp. 16548–16562.
ista: 'Lohaus M, Kleindessner M, Kenthapadi K, Locatello F, Russell C. 2022. Are
two heads the same as one? Identifying disparate treatment in fair neural networks.
36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information
Processing Systems, Advances in Neural Information Processing Systems, vol. 35,
16548–16562.'
mla: Lohaus, Michael, et al. “Are Two Heads the Same as One? Identifying Disparate
Treatment in Fair Neural Networks.” 36th Conference on Neural Information Processing
Systems, vol. 35, Neural Information Processing Systems Foundation, 2022,
pp. 16548–62.
short: M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, C. Russell, in:,
36th Conference on Neural Information Processing Systems, Neural Information Processing
Systems Foundation, 2022, pp. 16548–16562.
conference:
end_date: 2022-12-09
location: New Orleans, LA, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2022-11-28
date_created: 2023-08-21T12:12:42Z
date_published: 2022-12-15T00:00:00Z
date_updated: 2023-09-06T10:29:42Z
day: '15'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2204.04440'
intvolume: ' 35'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2204.04440
month: '12'
oa: 1
oa_version: Preprint
page: 16548-16562
publication: 36th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713871088'
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Are two heads the same as one? Identifying disparate treatment in fair neural
networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2022'
...
---
_id: '14107'
abstract:
- lang: eng
text: "Amodal perception requires inferring the full shape of an object that is
partially occluded. This task is particularly challenging on two levels: (1) it
requires more information than what is contained in the instant retina or imaging
sensor, (2) it is difficult to obtain enough well-annotated amodal labels for
supervision. To this end, this paper develops a new framework of\r\nSelf-supervised
amodal Video object segmentation (SaVos). Our method efficiently leverages the
visual information of video temporal sequences to infer the amodal mask of objects.
The key intuition is that the occluded part of an object can be explained away
if that part is visible in other frames, possibly deformed as long as the deformation
can be reasonably learned.\r\nAccordingly, we derive a novel self-supervised learning
paradigm that efficiently utilizes the visible object parts as the supervision
to guide the training on videos. In addition to learning type prior to complete
masks for known types, SaVos also learns the spatiotemporal prior, which is also
useful for the amodal task and could generalize to unseen types. The proposed\r\nframework
achieves the state-of-the-art performance on the synthetic amodal segmentation
benchmark FISHBOWL and the real world benchmark KINS-Video-Car. Further, it lends
itself well to being transferred to novel distributions using test-time adaptation,
outperforming existing models even after the transfer to a new distribution."
article_processing_charge: No
arxiv: 1
author:
- first_name: Jian
full_name: Yao, Jian
last_name: Yao
- first_name: Yuxin
full_name: Hong, Yuxin
last_name: Hong
- first_name: Chiyu
full_name: Wang, Chiyu
last_name: Wang
- first_name: Tianjun
full_name: Xiao, Tianjun
last_name: Xiao
- first_name: Tong
full_name: He, Tong
last_name: He
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: David
full_name: Wipf, David
last_name: Wipf
- first_name: Yanwei
full_name: Fu, Yanwei
last_name: Fu
- first_name: Zheng
full_name: Zhang, Zheng
last_name: Zhang
citation:
ama: 'Yao J, Hong Y, Wang C, et al. Self-supervised amodal video object segmentation.
In: 36th Conference on Neural Information Processing Systems. ; 2022. doi:10.48550/arXiv.2210.12733'
apa: Yao, J., Hong, Y., Wang, C., Xiao, T., He, T., Locatello, F., … Zhang, Z. (2022).
Self-supervised amodal video object segmentation. In 36th Conference on Neural
Information Processing Systems. New Orleans, LA, United States. https://doi.org/10.48550/arXiv.2210.12733
chicago: Yao, Jian, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello,
David Wipf, Yanwei Fu, and Zheng Zhang. “Self-Supervised Amodal Video Object Segmentation.”
In 36th Conference on Neural Information Processing Systems, 2022. https://doi.org/10.48550/arXiv.2210.12733.
ieee: J. Yao et al., “Self-supervised amodal video object segmentation,”
in 36th Conference on Neural Information Processing Systems, New Orleans,
LA, United States, 2022.
ista: 'Yao J, Hong Y, Wang C, Xiao T, He T, Locatello F, Wipf D, Fu Y, Zhang Z.
2022. Self-supervised amodal video object segmentation. 36th Conference on Neural
Information Processing Systems. NeurIPS: Neural Information Processing Systems.'
mla: Yao, Jian, et al. “Self-Supervised Amodal Video Object Segmentation.” 36th
Conference on Neural Information Processing Systems, 2022, doi:10.48550/arXiv.2210.12733.
short: J. Yao, Y. Hong, C. Wang, T. Xiao, T. He, F. Locatello, D. Wipf, Y. Fu, Z.
Zhang, in:, 36th Conference on Neural Information Processing Systems, 2022.
conference:
end_date: 2022-12-01
location: New Orleans, LA, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2022-11-28
date_created: 2023-08-21T12:13:25Z
date_published: 2022-10-23T00:00:00Z
date_updated: 2023-09-11T09:34:17Z
day: '23'
department:
- _id: FrLo
doi: 10.48550/arXiv.2210.12733
extern: '1'
external_id:
arxiv:
- '2210.12733'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2210.12733
month: '10'
oa: 1
oa_version: Preprint
publication: 36th Conference on Neural Information Processing Systems
publication_status: published
status: public
title: Self-supervised amodal video object segmentation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '14114'
abstract:
- lang: eng
text: Algorithmic fairness is frequently motivated in terms of a trade-off in which
overall performance is decreased so as to improve performance on disadvantaged
groups where the algorithm would otherwise be less accurate. Contrary to this,
we find that applying existing fairness approaches to computer vision improve
fairness by degrading the performance of classifiers across all groups (with increased
degradation on the best performing groups). Extending the bias-variance decomposition
for classification to fairness, we theoretically explain why the majority of fairness
methods designed for low capacity models should not be used in settings involving
high-capacity models, a scenario common to computer vision. We corroborate this
analysis with extensive experimental support that shows that many of the fairness
heuristics used in computer vision also degrade performance on the most disadvantaged
groups. Building on these insights, we propose an adaptive augmentation strategy
that, uniquely, of all methods tested, improves performance for the disadvantaged
groups.
article_processing_charge: No
arxiv: 1
author:
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Michael
full_name: Lohaus, Michael
last_name: Lohaus
- first_name: Guha
full_name: Balakrishnan, Guha
last_name: Balakrishnan
- first_name: Matthaus
full_name: Kleindessner, Matthaus
last_name: Kleindessner
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bernhard
full_name: Scholkopf, Bernhard
last_name: Scholkopf
- first_name: Chris
full_name: Russell, Chris
last_name: Russell
citation:
ama: 'Zietlow D, Lohaus M, Balakrishnan G, et al. Leveling down in computer vision:
Pareto inefficiencies in fair deep classifiers. In: 2022 IEEE/CVF Conference
on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics
Engineers; 2022:10400-10411. doi:10.1109/cvpr52688.2022.01016'
apa: 'Zietlow, D., Lohaus, M., Balakrishnan, G., Kleindessner, M., Locatello, F.,
Scholkopf, B., & Russell, C. (2022). Leveling down in computer vision: Pareto
inefficiencies in fair deep classifiers. In 2022 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (pp. 10400–10411). New Orleans, LA, United
States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01016'
chicago: 'Zietlow, Dominik, Michael Lohaus, Guha Balakrishnan, Matthaus Kleindessner,
Francesco Locatello, Bernhard Scholkopf, and Chris Russell. “Leveling down in
Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.” In 2022 IEEE/CVF
Conference on Computer Vision and Pattern Recognition, 10400–411. Institute
of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/cvpr52688.2022.01016.'
ieee: 'D. Zietlow et al., “Leveling down in computer vision: Pareto inefficiencies
in fair deep classifiers,” in 2022 IEEE/CVF Conference on Computer Vision and
Pattern Recognition, New Orleans, LA, United States, 2022, pp. 10400–10411.'
ista: 'Zietlow D, Lohaus M, Balakrishnan G, Kleindessner M, Locatello F, Scholkopf
B, Russell C. 2022. Leveling down in computer vision: Pareto inefficiencies in
fair deep classifiers. 2022 IEEE/CVF Conference on Computer Vision and Pattern
Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 10400–10411.'
mla: 'Zietlow, Dominik, et al. “Leveling down in Computer Vision: Pareto Inefficiencies
in Fair Deep Classifiers.” 2022 IEEE/CVF Conference on Computer Vision and
Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022,
pp. 10400–11, doi:10.1109/cvpr52688.2022.01016.'
short: D. Zietlow, M. Lohaus, G. Balakrishnan, M. Kleindessner, F. Locatello, B.
Scholkopf, C. Russell, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern
Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–10411.
conference:
end_date: 2022-06-24
location: New Orleans, LA, United States
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2022-06-18
date_created: 2023-08-21T12:18:00Z
date_published: 2022-07-01T00:00:00Z
date_updated: 2023-09-11T09:19:14Z
day: '01'
department:
- _id: FrLo
doi: 10.1109/cvpr52688.2022.01016
extern: '1'
external_id:
arxiv:
- '2203.04913'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2203.04913
month: '07'
oa: 1
oa_version: Preprint
page: 10400-10411
publication: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
eissn:
- 2575-7075
isbn:
- '9781665469470'
issn:
- 1063-6919
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '14168'
abstract:
- lang: eng
text: "Recent work has seen the development of general purpose neural architectures\r\nthat
can be trained to perform tasks across diverse data modalities. General\r\npurpose
models typically make few assumptions about the underlying\r\ndata-structure and
are known to perform well in the large-data regime. At the\r\nsame time, there
has been growing interest in modular neural architectures that\r\nrepresent the
data using sparsely interacting modules. These models can be more\r\nrobust out-of-distribution,
computationally efficient, and capable of\r\nsample-efficient adaptation to new
data. However, they tend to make\r\ndomain-specific assumptions about the data,
and present challenges in how\r\nmodule behavior (i.e., parameterization) and
connectivity (i.e., their layout)\r\ncan be jointly learned. In this work, we
introduce a general purpose, yet\r\nmodular neural architecture called Neural
Attentive Circuits (NACs) that\r\njointly learns the parameterization and a sparse
connectivity of neural modules\r\nwithout using domain knowledge. NACs are best
understood as the combination of\r\ntwo systems that are jointly trained end-to-end:
one that determines the module\r\nconfiguration and the other that executes it
on an input. We demonstrate\r\nqualitatively that NACs learn diverse and meaningful
module configurations on\r\nthe NLVR2 dataset without additional supervision.
Quantitatively, we show that\r\nby incorporating modularity in this way, NACs
improve upon a strong non-modular\r\nbaseline in terms of low-shot adaptation
on CIFAR and CUBs dataset by about\r\n10%, and OOD robustness on Tiny ImageNet-R
by about 2.5%. Further, we find that\r\nNACs can achieve an 8x speedup at inference
time while losing less than 3%\r\nperformance. Finally, we find NACs to yield
competitive results on diverse data\r\nmodalities spanning point-cloud classification,
symbolic processing and\r\ntext-classification from ASCII bytes, thereby confirming
its general purpose\r\nnature."
alternative_title:
- ' Advances in Neural Information Processing Systems'
article_processing_charge: No
arxiv: 1
author:
- first_name: Nasim
full_name: Rahaman, Nasim
last_name: Rahaman
- first_name: Martin
full_name: Weiss, Martin
last_name: Weiss
- 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: Pal, Chris
last_name: Pal
- first_name: Yoshua
full_name: Bengio, Yoshua
last_name: Bengio
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Li Erran
full_name: Li, Li Erran
last_name: Li
- first_name: Nicolas
full_name: Ballas, Nicolas
last_name: Ballas
citation:
ama: 'Rahaman N, Weiss M, Locatello F, et al. Neural attentive circuits. In: 36th
Conference on Neural Information Processing Systems. Vol 35. ; 2022.'
apa: Rahaman, N., Weiss, M., Locatello, F., Pal, C., Bengio, Y., Schölkopf, B.,
… Ballas, N. (2022). Neural attentive circuits. In 36th Conference on Neural
Information Processing Systems (Vol. 35). New Orleans, United States.
chicago: Rahaman, Nasim, Martin Weiss, Francesco Locatello, Chris Pal, Yoshua Bengio,
Bernhard Schölkopf, Li Erran Li, and Nicolas Ballas. “Neural Attentive Circuits.”
In 36th Conference on Neural Information Processing Systems, Vol. 35, 2022.
ieee: N. Rahaman et al., “Neural attentive circuits,” in 36th Conference
on Neural Information Processing Systems, New Orleans, United States, 2022,
vol. 35.
ista: 'Rahaman N, Weiss M, Locatello F, Pal C, Bengio Y, Schölkopf B, Li LE, Ballas
N. 2022. Neural attentive circuits. 36th Conference on Neural Information Processing
Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information
Processing Systems, vol. 35.'
mla: Rahaman, Nasim, et al. “Neural Attentive Circuits.” 36th Conference on Neural
Information Processing Systems, vol. 35, 2022.
short: N. Rahaman, M. Weiss, F. Locatello, C. Pal, Y. Bengio, B. Schölkopf, L.E.
Li, N. Ballas, in:, 36th Conference on Neural Information Processing Systems,
2022.
conference:
end_date: 2022-12-01
location: New Orleans, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2022-11-29
date_created: 2023-08-22T13:57:27Z
date_published: 2022-10-14T00:00:00Z
date_updated: 2023-09-11T09:29:09Z
day: '14'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2210.08031'
intvolume: ' 35'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2210.08031
month: '10'
oa: 1
oa_version: Preprint
publication: 36th Conference on Neural Information Processing Systems
publication_status: published
status: public
title: Neural attentive circuits
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2022'
...
---
_id: '14170'
abstract:
- lang: eng
text: "The idea behind object-centric representation learning is that natural scenes
can better be modeled as compositions of objects and their relations as opposed
to distributed representations. This inductive bias can be injected into neural
networks to potentially improve systematic generalization and performance of downstream
tasks in scenes with multiple objects. In this paper, we train state-of-the-art
unsupervised models on five common multi-object datasets and evaluate segmentation
metrics and downstream object property prediction. In addition, we study generalization
and robustness by investigating the settings where either a single object is out
of distribution -- e.g., having an unseen color, texture, or shape -- or global
properties of the scene are altered -- e.g., by occlusions, cropping, or increasing
the number of objects. From our experimental study, we find object-centric representations
to be useful for\r\ndownstream tasks and generally robust to most distribution
shifts affecting objects. However, when the distribution shift affects the input
in a less structured manner, robustness in terms of segmentation and downstream
task performance may vary significantly across models and distribution shifts. "
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Andrea
full_name: Dittadi, Andrea
last_name: Dittadi
- first_name: Samuele
full_name: Papa, Samuele
last_name: Papa
- first_name: Michele De
full_name: Vita, Michele De
last_name: Vita
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Ole
full_name: Winther, Ole
last_name: Winther
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Dittadi A, Papa S, Vita MD, Schölkopf B, Winther O, Locatello F. Generalization
and robustness implications in object-centric learning. In: Proceedings of
the 39th International Conference on Machine Learning. Vol 2022. ML Research
Press; :5221-5285.'
apa: 'Dittadi, A., Papa, S., Vita, M. D., Schölkopf, B., Winther, O., & Locatello,
F. (n.d.). Generalization and robustness implications in object-centric learning.
In Proceedings of the 39th International Conference on Machine Learning
(Vol. 2022, pp. 5221–5285). Baltimore, MD, United States: ML Research Press.'
chicago: Dittadi, Andrea, Samuele Papa, Michele De Vita, Bernhard Schölkopf, Ole
Winther, and Francesco Locatello. “Generalization and Robustness Implications
in Object-Centric Learning.” In Proceedings of the 39th International Conference
on Machine Learning, 2022:5221–85. ML Research Press, n.d.
ieee: A. Dittadi, S. Papa, M. D. Vita, B. Schölkopf, O. Winther, and F. Locatello,
“Generalization and robustness implications in object-centric learning,” in Proceedings
of the 39th International Conference on Machine Learning, Baltimore, MD, United
States, vol. 2022, pp. 5221–5285.
ista: Dittadi A, Papa S, Vita MD, Schölkopf B, Winther O, Locatello F. Generalization
and robustness implications in object-centric learning. Proceedings of the 39th
International Conference on Machine Learning. International Conference on Machine
Learning, PMLR, vol. 2022, 5221–5285.
mla: Dittadi, Andrea, et al. “Generalization and Robustness Implications in Object-Centric
Learning.” Proceedings of the 39th International Conference on Machine Learning,
vol. 2022, ML Research Press, pp. 5221–85.
short: A. Dittadi, S. Papa, M.D. Vita, B. Schölkopf, O. Winther, F. Locatello, in:,
Proceedings of the 39th International Conference on Machine Learning, ML Research
Press, n.d., pp. 5221–5285.
conference:
end_date: 2022-07-23
location: Baltimore, MD, United States
name: International Conference on Machine Learning
start_date: 2022-07-17
date_created: 2023-08-22T13:59:55Z
date_published: 2022-07-22T00:00:00Z
date_updated: 2023-09-11T10:08:14Z
day: '22'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2107.00637'
intvolume: ' 2022'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2107.00637
month: '07'
oa: 1
oa_version: Preprint
page: 5221-5285
publication: Proceedings of the 39th International Conference on Machine Learning
publication_status: submitted
publisher: ML Research Press
quality_controlled: '1'
status: public
title: Generalization and robustness implications in object-centric learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2022
year: '2022'
...
---
_id: '14171'
abstract:
- lang: eng
text: "This paper demonstrates how to recover causal graphs from the score of the\r\ndata
distribution in non-linear additive (Gaussian) noise models. Using score\r\nmatching
algorithms as a building block, we show how to design a new generation\r\nof scalable
causal discovery methods. To showcase our approach, we also propose\r\na new efficient
method for approximating the score's Jacobian, enabling to\r\nrecover the causal
graph. Empirically, we find that the new algorithm, called\r\nSCORE, is competitive
with state-of-the-art causal discovery methods while\r\nbeing significantly faster."
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Paul
full_name: Rolland, Paul
last_name: Rolland
- first_name: Volkan
full_name: Cevher, Volkan
last_name: Cevher
- first_name: Matthäus
full_name: Kleindessner, Matthäus
last_name: Kleindessner
- first_name: Chris
full_name: Russel, Chris
last_name: Russel
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Dominik
full_name: Janzing, Dominik
last_name: Janzing
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Rolland P, Cevher V, Kleindessner M, et al. Score matching enables causal
discovery of nonlinear additive noise models. In: Proceedings of the 39th
International Conference on Machine Learning. Vol 162. ML Research Press;
2022:18741-18753.'
apa: 'Rolland, P., Cevher, V., Kleindessner, M., Russel, C., Schölkopf, B., Janzing,
D., & Locatello, F. (2022). Score matching enables causal discovery of nonlinear
additive noise models. In Proceedings of the 39th International Conference
on Machine Learning (Vol. 162, pp. 18741–18753). Baltimore, MD, United States:
ML Research Press.'
chicago: Rolland, Paul, Volkan Cevher, Matthäus Kleindessner, Chris Russel, Bernhard
Schölkopf, Dominik Janzing, and Francesco Locatello. “Score Matching Enables Causal
Discovery of Nonlinear Additive Noise Models.” In Proceedings of the 39th
International Conference on Machine Learning, 162:18741–53. ML Research Press,
2022.
ieee: P. Rolland et al., “Score matching enables causal discovery of nonlinear
additive noise models,” in Proceedings of the 39th International Conference
on Machine Learning, Baltimore, MD, United States, 2022, vol. 162, pp. 18741–18753.
ista: Rolland P, Cevher V, Kleindessner M, Russel C, Schölkopf B, Janzing D, Locatello
F. 2022. Score matching enables causal discovery of nonlinear additive noise
models. Proceedings of the 39th International Conference on Machine Learning.
International Conference on Machine Learning, PMLR, vol. 162, 18741–18753.
mla: Rolland, Paul, et al. “Score Matching Enables Causal Discovery of Nonlinear
Additive Noise Models.” Proceedings of the 39th International Conference on
Machine Learning, vol. 162, ML Research Press, 2022, pp. 18741–53.
short: P. Rolland, V. Cevher, M. Kleindessner, C. Russel, B. Schölkopf, D. Janzing,
F. Locatello, in:, Proceedings of the 39th International Conference on Machine
Learning, ML Research Press, 2022, pp. 18741–18753.
conference:
end_date: 2022-07-23
location: Baltimore, MD, United States
name: International Conference on Machine Learning
start_date: 2022-07-17
date_created: 2023-08-22T14:00:18Z
date_published: 2022-07-22T00:00:00Z
date_updated: 2023-09-11T10:14:20Z
day: '22'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2203.04413'
intvolume: ' 162'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2203.04413
month: '07'
oa: 1
oa_version: Preprint
page: 18741-18753
publication: Proceedings of the 39th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
status: public
title: Score matching enables causal discovery of nonlinear additive noise models
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 162
year: '2022'
...
---
_id: '14172'
abstract:
- lang: eng
text: "An important component for generalization in machine learning is to uncover
underlying latent factors of variation as well as the mechanism through which
each factor acts in the world. In this paper, we test whether 17 unsupervised,
weakly supervised, and fully supervised representation learning approaches correctly
infer the generative factors of variation in simple datasets (dSprites, Shapes3D,
MPI3D) from controlled environments, and on our contributed CelebGlow dataset.
In contrast to prior robustness work that introduces novel factors of variation
during test time, such as blur or other (un)structured noise, we here recompose,
interpolate, or extrapolate only existing factors of variation from the training
data set (e.g., small and medium-sized objects during training and large objects
during testing). Models\r\nthat learn the correct mechanism should be able to
generalize to this benchmark. In total, we train and test 2000+ models and observe
that all of them struggle to learn the underlying mechanism regardless of supervision
signal and architectural bias. Moreover, the generalization capabilities of all
tested models drop significantly as we move from artificial datasets towards\r\nmore
realistic real-world datasets. Despite their inability to identify the correct
mechanism, the models are quite modular as their ability to infer other in-distribution
factors remains fairly stable, providing only a single factoris out-of-distribution.
These results point to an important yet understudied problem of learning mechanistic
models of observations that can facilitate\r\ngeneralization."
article_processing_charge: No
arxiv: 1
author:
- first_name: Lukas
full_name: Schott, Lukas
last_name: Schott
- first_name: Julius von
full_name: Kügelgen, Julius von
last_name: Kügelgen
- first_name: Frederik
full_name: Träuble, Frederik
last_name: Träuble
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
- first_name: Chris
full_name: Russell, Chris
last_name: Russell
- first_name: Matthias
full_name: Bethge, Matthias
last_name: Bethge
- 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
- first_name: Wieland
full_name: Brendel, Wieland
last_name: Brendel
citation:
ama: 'Schott L, Kügelgen J von, Träuble F, et al. Visual representation learning
does not generalize strongly within the same domain. In: 10th International
Conference on Learning Representations. ; 2022.'
apa: Schott, L., Kügelgen, J. von, Träuble, F., Gehler, P., Russell, C., Bethge,
M., … Brendel, W. (2022). Visual representation learning does not generalize strongly
within the same domain. In 10th International Conference on Learning Representations.
Virtual.
chicago: Schott, Lukas, Julius von Kügelgen, Frederik Träuble, Peter Gehler, Chris
Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, and Wieland
Brendel. “Visual Representation Learning Does Not Generalize Strongly within the
Same Domain.” In 10th International Conference on Learning Representations,
2022.
ieee: L. Schott et al., “Visual representation learning does not generalize
strongly within the same domain,” in 10th International Conference on Learning
Representations, Virtual, 2022.
ista: 'Schott L, Kügelgen J von, Träuble F, Gehler P, Russell C, Bethge M, Schölkopf
B, Locatello F, Brendel W. 2022. Visual representation learning does not generalize
strongly within the same domain. 10th International Conference on Learning Representations.
ICLR: International Conference on Learning Representations.'
mla: Schott, Lukas, et al. “Visual Representation Learning Does Not Generalize Strongly
within the Same Domain.” 10th International Conference on Learning Representations,
2022.
short: L. Schott, J. von Kügelgen, F. Träuble, P. Gehler, C. Russell, M. Bethge,
B. Schölkopf, F. Locatello, W. Brendel, in:, 10th International Conference on
Learning Representations, 2022.
conference:
end_date: 2022-04-29
location: Virtual
name: 'ICLR: International Conference on Learning Representations'
start_date: 2022-04-25
date_created: 2023-08-22T14:00:50Z
date_published: 2022-04-25T00:00:00Z
date_updated: 2023-09-11T09:40:52Z
day: '25'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2107.08221'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2107.08221
month: '04'
oa: 1
oa_version: Preprint
publication: 10th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: Visual representation learning does not generalize strongly within the same
domain
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...