---
_id: '12511'
abstract:
- lang: eng
text: "We consider the problem of formally verifying almost-sure (a.s.) asymptotic
stability in discrete-time nonlinear stochastic control systems. While verifying
stability in deterministic control systems is extensively studied in the literature,
verifying stability in stochastic control systems is an open problem. The few
existing works on this topic either consider only specialized forms of stochasticity
or make restrictive assumptions on the system, rendering them inapplicable to
learning algorithms with neural network policies. \r\n In this work, we present
an approach for general nonlinear stochastic control problems with two novel aspects:
(a) instead of classical stochastic extensions of Lyapunov functions, we use ranking
supermartingales (RSMs) to certify a.s. asymptotic stability, and (b) we present
a method for learning neural network RSMs. \r\n We prove that our approach guarantees
a.s. asymptotic stability of the system and\r\n provides the first method to obtain
bounds on the stabilization time, which stochastic Lyapunov functions do not.\r\n
Finally, we validate our approach experimentally on a set of nonlinear stochastic
reinforcement learning environments with neural network policies."
acknowledgement: "This work was supported in part by the ERC-2020-AdG 101020093, ERC
CoG 863818 (FoRM-SMArt) and the European Union’s Horizon 2020 research and innovation
programme\r\nunder the Marie Skłodowska-Curie Grant Agreement No. 665385."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
citation:
ama: Lechner M, Zikelic D, Chatterjee K, Henzinger TA. Stability verification in
stochastic control systems via neural network supermartingales. Proceedings
of the AAAI Conference on Artificial Intelligence. 2022;36(7):7326-7336. doi:10.1609/aaai.v36i7.20695
apa: Lechner, M., Zikelic, D., Chatterjee, K., & Henzinger, T. A. (2022). Stability
verification in stochastic control systems via neural network supermartingales.
Proceedings of the AAAI Conference on Artificial Intelligence. Association
for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v36i7.20695
chicago: Lechner, Mathias, Dorde Zikelic, Krishnendu Chatterjee, and Thomas A Henzinger.
“Stability Verification in Stochastic Control Systems via Neural Network Supermartingales.”
Proceedings of the AAAI Conference on Artificial Intelligence. Association
for the Advancement of Artificial Intelligence, 2022. https://doi.org/10.1609/aaai.v36i7.20695.
ieee: M. Lechner, D. Zikelic, K. Chatterjee, and T. A. Henzinger, “Stability verification
in stochastic control systems via neural network supermartingales,” Proceedings
of the AAAI Conference on Artificial Intelligence, vol. 36, no. 7. Association
for the Advancement of Artificial Intelligence, pp. 7326–7336, 2022.
ista: Lechner M, Zikelic D, Chatterjee K, Henzinger TA. 2022. Stability verification
in stochastic control systems via neural network supermartingales. Proceedings
of the AAAI Conference on Artificial Intelligence. 36(7), 7326–7336.
mla: Lechner, Mathias, et al. “Stability Verification in Stochastic Control Systems
via Neural Network Supermartingales.” Proceedings of the AAAI Conference on
Artificial Intelligence, vol. 36, no. 7, Association for the Advancement of
Artificial Intelligence, 2022, pp. 7326–36, doi:10.1609/aaai.v36i7.20695.
short: M. Lechner, D. Zikelic, K. Chatterjee, T.A. Henzinger, Proceedings of the
AAAI Conference on Artificial Intelligence 36 (2022) 7326–7336.
date_created: 2023-02-05T17:29:50Z
date_published: 2022-06-28T00:00:00Z
date_updated: 2025-07-14T09:09:58Z
day: '28'
department:
- _id: ToHe
- _id: KrCh
doi: 10.1609/aaai.v36i7.20695
ec_funded: 1
external_id:
arxiv:
- '2112.09495'
intvolume: ' 36'
issue: '7'
keyword:
- General Medicine
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2112.09495
month: '06'
oa: 1
oa_version: Preprint
page: 7326-7336
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: Proceedings of the AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
isbn:
- '9781577358350'
issn:
- 2159-5399
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
related_material:
record:
- id: '14539'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Stability verification in stochastic control systems via neural network supermartingales
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 36
year: '2022'
...
---
_id: '12516'
abstract:
- lang: eng
text: "The homogeneous continuous LWE (hCLWE) problem is to distinguish samples
of a specific high-dimensional Gaussian mixture from standard normal samples.
It was shown to be at least as hard as Learning with Errors, but no reduction
in the other direction is currently known.\r\nWe present four new public-key encryption
schemes based on the hardness of hCLWE, with varying tradeoffs between decryption
and security errors, and different discretization techniques. Our schemes yield
a polynomial-time algorithm for solving hCLWE using a Statistical Zero-Knowledge
oracle."
acknowledgement: "We are grateful to Devika Sharma and Luca Trevisan for their insight
and advice and to an anonymous reviewer for helpful comments.\r\n\r\nThis work was
supported by the European Research Council (ERC) under the European Union’s Horizon
2020 research and innovation programme (Grant agreement No. 101019547). The first
author was additionally supported by RGC GRF CUHK14209920 and the fourth author
was additionally supported by ISF grant No. 1399/17, project PROMETHEUS (Grant 780701),
and Cariplo CRYPTONOMEX grant."
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Andrej
full_name: Bogdanov, Andrej
last_name: Bogdanov
- first_name: Miguel
full_name: Cueto Noval, Miguel
id: ffc563a3-f6e0-11ea-865d-e3cce03d17cc
last_name: Cueto Noval
- first_name: Charlotte
full_name: Hoffmann, Charlotte
id: 0f78d746-dc7d-11ea-9b2f-83f92091afe7
last_name: Hoffmann
- first_name: Alon
full_name: Rosen, Alon
last_name: Rosen
citation:
ama: 'Bogdanov A, Cueto Noval M, Hoffmann C, Rosen A. Public-Key Encryption from Homogeneous
CLWE. In: Theory of Cryptography. Vol 13748. Springer Nature; 2022:565-592.
doi:10.1007/978-3-031-22365-5_20'
apa: 'Bogdanov, A., Cueto Noval, M., Hoffmann, C., & Rosen, A. (2022). Public-Key
Encryption from Homogeneous CLWE. In Theory of Cryptography (Vol. 13748,
pp. 565–592). Chicago, IL, United States: Springer Nature. https://doi.org/10.1007/978-3-031-22365-5_20'
chicago: Bogdanov, Andrej, Miguel Cueto Noval, Charlotte Hoffmann, and Alon Rosen.
“Public-Key Encryption from Homogeneous CLWE.” In Theory of Cryptography,
13748:565–92. Springer Nature, 2022. https://doi.org/10.1007/978-3-031-22365-5_20.
ieee: A. Bogdanov, M. Cueto Noval, C. Hoffmann, and A. Rosen, “Public-Key Encryption
from Homogeneous CLWE,” in Theory of Cryptography, Chicago, IL, United
States, 2022, vol. 13748, pp. 565–592.
ista: 'Bogdanov A, Cueto Noval M, Hoffmann C, Rosen A. 2022. Public-Key Encryption
from Homogeneous CLWE. Theory of Cryptography. TCC: Theory of Cryptography, LNCS,
vol. 13748, 565–592.'
mla: Bogdanov, Andrej, et al. “Public-Key Encryption from Homogeneous CLWE.” Theory
of Cryptography, vol. 13748, Springer Nature, 2022, pp. 565–92, doi:10.1007/978-3-031-22365-5_20.
short: A. Bogdanov, M. Cueto Noval, C. Hoffmann, A. Rosen, in:, Theory of Cryptography,
Springer Nature, 2022, pp. 565–592.
conference:
end_date: 2022-11-10
location: Chicago, IL, United States
name: 'TCC: Theory of Cryptography'
start_date: 2022-11-07
date_created: 2023-02-05T23:01:00Z
date_published: 2022-12-21T00:00:00Z
date_updated: 2023-08-04T10:39:30Z
day: '21'
department:
- _id: KrPi
doi: 10.1007/978-3-031-22365-5_20
external_id:
isi:
- '000921318200020'
intvolume: ' 13748'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://eprint.iacr.org/2022/093
month: '12'
oa: 1
oa_version: Preprint
page: 565-592
publication: Theory of Cryptography
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783031223648'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Public-Key Encryption from Homogeneous CLWE
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 13748
year: '2022'
...
---
_id: '12522'
abstract:
- lang: eng
text: This .zip File contains the transport data, the codes for the data analysis,
the microscopy analysis and the codes for the theoretical simulations for "Majorana-like
Coulomb spectroscopy in the absence of zero bias peaks" by M. Valentini, et. al.
The transport data are saved with hdf5 file format. The files can be open with
the log browser of Labber.
article_processing_charge: No
author:
- first_name: Marco
full_name: Valentini, Marco
id: C0BB2FAC-D767-11E9-B658-BC13E6697425
last_name: Valentini
- first_name: Pablo
full_name: San-Jose, Pablo
last_name: San-Jose
- first_name: Jordi
full_name: Arbiol, Jordi
last_name: Arbiol
- first_name: Sara
full_name: Marti-Sanchez, Sara
last_name: Marti-Sanchez
- first_name: Marc
full_name: Botifoll, Marc
last_name: Botifoll
citation:
ama: Valentini M, San-Jose P, Arbiol J, Marti-Sanchez S, Botifoll M. Data for “Majorana-like
Coulomb spectroscopy in the absence of zero bias peaks.” 2022. doi:10.15479/AT:ISTA:12102
apa: Valentini, M., San-Jose, P., Arbiol, J., Marti-Sanchez, S., & Botifoll,
M. (2022). Data for “Majorana-like Coulomb spectroscopy in the absence of zero
bias peaks.” Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:12102
chicago: Valentini, Marco, Pablo San-Jose, Jordi Arbiol, Sara Marti-Sanchez, and
Marc Botifoll. “Data for ‘Majorana-like Coulomb Spectroscopy in the Absence of
Zero Bias Peaks.’” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/AT:ISTA:12102.
ieee: M. Valentini, P. San-Jose, J. Arbiol, S. Marti-Sanchez, and M. Botifoll, “Data
for ‘Majorana-like Coulomb spectroscopy in the absence of zero bias peaks.’” Institute
of Science and Technology Austria, 2022.
ista: Valentini M, San-Jose P, Arbiol J, Marti-Sanchez S, Botifoll M. 2022. Data
for ‘Majorana-like Coulomb spectroscopy in the absence of zero bias peaks’, Institute
of Science and Technology Austria, 10.15479/AT:ISTA:12102.
mla: Valentini, Marco, et al. Data for “Majorana-like Coulomb Spectroscopy in
the Absence of Zero Bias Peaks.” Institute of Science and Technology Austria,
2022, doi:10.15479/AT:ISTA:12102.
short: M. Valentini, P. San-Jose, J. Arbiol, S. Marti-Sanchez, M. Botifoll, (2022).
contributor:
- contributor_type: contact_person
first_name: Marco
id: C0BB2FAC-D767-11E9-B658-BC13E6697425
last_name: Valentini
date_created: 2023-02-07T08:13:39Z
date_published: 2022-09-25T00:00:00Z
date_updated: 2024-02-21T12:35:34Z
day: '25'
ddc:
- '530'
department:
- _id: GeKa
doi: 10.15479/AT:ISTA:12102
file:
- access_level: open_access
checksum: 0dbd6327bf84c7e81b295c4bc9d12826
content_type: application/x-zip-compressed
creator: dernst
date_created: 2023-02-07T08:18:24Z
date_updated: 2023-02-07T08:18:24Z
file_id: '12523'
file_name: Majorana_like.zip
file_size: 3609122411
relation: main_file
success: 1
file_date_updated: 2023-02-07T08:18:24Z
has_accepted_license: '1'
month: '09'
oa: 1
oa_version: Published Version
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '12118'
relation: used_in_publication
status: public
- id: '13286'
relation: used_in_publication
status: public
status: public
title: Data for "Majorana-like Coulomb spectroscopy in the absence of zero bias peaks"
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: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12536'
abstract:
- lang: eng
text: 'We consider the problem of estimating a rank-1 signal corrupted by structured
rotationally invariant noise, and address the following question: how well do
inference algorithms perform when the noise statistics is unknown and hence Gaussian
noise is assumed? While the matched Bayes-optimal setting with unstructured noise
is well understood, the analysis of this mismatched problem is only at its premises.
In this paper, we make a step towards understanding the effect of the strong source
of mismatch which is the noise statistics. Our main technical contribution is
the rigorous analysis of a Bayes estimator and of an approximate message passing
(AMP) algorithm, both of which incorrectly assume a Gaussian setup. The first
result exploits the theory of spherical integrals and of low-rank matrix perturbations;
the idea behind the second one is to design and analyze an artificial AMP which,
by taking advantage of the flexibility in the denoisers, is able to "correct"
the mismatch. Armed with these sharp asymptotic characterizations, we unveil a
rich and often unexpected phenomenology. For example, despite AMP is in principle
designed to efficiently compute the Bayes estimator, the former is outperformed
by the latter in terms of mean-square error. We show that this performance gap
is due to an incorrect estimation of the signal norm. In fact, when the SNR is
large enough, the overlaps of the AMP and the Bayes estimator coincide, and they
even match those of optimal estimators taking into account the structure of the
noise.'
article_number: '2205.10009'
article_processing_charge: No
arxiv: 1
author:
- first_name: Jean
full_name: Barbier, Jean
last_name: Barbier
- first_name: TianQi
full_name: Hou, TianQi
last_name: Hou
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
- first_name: Manuel
full_name: Saenz, Manuel
last_name: Saenz
citation:
ama: 'Barbier J, Hou T, Mondelli M, Saenz M. The price of ignorance: How much does
it cost to forget noise structure in low-rank matrix estimation? arXiv.
doi:10.48550/arXiv.2205.10009'
apa: 'Barbier, J., Hou, T., Mondelli, M., & Saenz, M. (n.d.). The price of ignorance:
How much does it cost to forget noise structure in low-rank matrix estimation?
arXiv. https://doi.org/10.48550/arXiv.2205.10009'
chicago: 'Barbier, Jean, TianQi Hou, Marco Mondelli, and Manuel Saenz. “The Price
of Ignorance: How Much Does It Cost to Forget Noise Structure in Low-Rank Matrix
Estimation?” ArXiv, n.d. https://doi.org/10.48550/arXiv.2205.10009.'
ieee: 'J. Barbier, T. Hou, M. Mondelli, and M. Saenz, “The price of ignorance: How
much does it cost to forget noise structure in low-rank matrix estimation?,” arXiv.
.'
ista: 'Barbier J, Hou T, Mondelli M, Saenz M. The price of ignorance: How much does
it cost to forget noise structure in low-rank matrix estimation? arXiv, 2205.10009.'
mla: 'Barbier, Jean, et al. “The Price of Ignorance: How Much Does It Cost to Forget
Noise Structure in Low-Rank Matrix Estimation?” ArXiv, 2205.10009, doi:10.48550/arXiv.2205.10009.'
short: J. Barbier, T. Hou, M. Mondelli, M. Saenz, ArXiv (n.d.).
date_created: 2023-02-10T13:45:41Z
date_published: 2022-05-20T00:00:00Z
date_updated: 2023-02-16T09:41:25Z
day: '20'
department:
- _id: MaMo
doi: 10.48550/arXiv.2205.10009
external_id:
arxiv:
- '2205.10009'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2205.10009
month: '05'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: accepted
status: public
title: 'The price of ignorance: How much does it cost to forget noise structure in
low-rank matrix estimation?'
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12537'
abstract:
- lang: eng
text: 'The Neural Tangent Kernel (NTK) has emerged as a powerful tool to provide
memorization, optimization and generalization guarantees in deep neural networks.
A line of work has studied the NTK spectrum for two-layer and deep networks with
at least a layer with Ω(N) neurons, N being the number of training samples. Furthermore,
there is increasing evidence suggesting that deep networks with sub-linear layer
widths are powerful memorizers and optimizers, as long as the number of parameters
exceeds the number of samples. Thus, a natural open question is whether the NTK
is well conditioned in such a challenging sub-linear setup. In this paper, we
answer this question in the affirmative. Our key technical contribution is a lower
bound on the smallest NTK eigenvalue for deep networks with the minimum possible
over-parameterization: the number of parameters is roughly Ω(N) and, hence, the
number of neurons is as little as Ω(N−−√). To showcase the applicability of our
NTK bounds, we provide two results concerning memorization capacity and optimization
guarantees for gradient descent training.'
acknowledgement: "The authors were partially supported by the 2019 Lopez-Loreta prize,
and they would like to thank\r\nQuynh Nguyen, Mahdi Soltanolkotabi and Adel Javanmard
for helpful discussions.\r\n"
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: Mohammad Hossein
full_name: Amani, Mohammad Hossein
last_name: Amani
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
citation:
ama: 'Bombari S, Amani MH, Mondelli M. Memorization and optimization in deep neural
networks with minimum over-parameterization. In: 36th Conference on Neural
Information Processing Systems. Vol 35. Curran Associates; 2022:7628-7640.'
apa: Bombari, S., Amani, M. H., & Mondelli, M. (2022). Memorization and optimization
in deep neural networks with minimum over-parameterization. In 36th Conference
on Neural Information Processing Systems (Vol. 35, pp. 7628–7640). Curran
Associates.
chicago: Bombari, Simone, Mohammad Hossein Amani, and Marco Mondelli. “Memorization
and Optimization in Deep Neural Networks with Minimum Over-Parameterization.”
In 36th Conference on Neural Information Processing Systems, 35:7628–40.
Curran Associates, 2022.
ieee: S. Bombari, M. H. Amani, and M. Mondelli, “Memorization and optimization in
deep neural networks with minimum over-parameterization,” in 36th Conference
on Neural Information Processing Systems, 2022, vol. 35, pp. 7628–7640.
ista: Bombari S, Amani MH, Mondelli M. 2022. Memorization and optimization in deep
neural networks with minimum over-parameterization. 36th Conference on Neural
Information Processing Systems. vol. 35, 7628–7640.
mla: Bombari, Simone, et al. “Memorization and Optimization in Deep Neural Networks
with Minimum Over-Parameterization.” 36th Conference on Neural Information
Processing Systems, vol. 35, Curran Associates, 2022, pp. 7628–40.
short: S. Bombari, M.H. Amani, M. Mondelli, in:, 36th Conference on Neural Information
Processing Systems, Curran Associates, 2022, pp. 7628–7640.
date_created: 2023-02-10T13:46:37Z
date_published: 2022-07-24T00:00:00Z
date_updated: 2024-09-10T13:03:19Z
day: '24'
department:
- _id: MaMo
external_id:
arxiv:
- '2205.10217'
intvolume: ' 35'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2205.10217'
month: '07'
oa: 1
oa_version: Preprint
page: 7628-7640
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: 36th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713871088'
publication_status: published
publisher: Curran Associates
quality_controlled: '1'
status: public
title: Memorization and optimization in deep neural networks with minimum over-parameterization
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2022'
...
---
_id: '12538'
abstract:
- lang: eng
text: In this paper, we study the compression of a target two-layer neural network
with N nodes into a compressed network with MIEEE Information Theory Workshop.
2022:588-593. doi:10.1109/ITW54588.2022.9965870
apa: 'Amani, M. H., Bombari, S., Mondelli, M., Pukdee, R., & Rini, S. (2022).
Sharp asymptotics on the compression of two-layer neural networks. IEEE Information
Theory Workshop. Mumbai, India: IEEE. https://doi.org/10.1109/ITW54588.2022.9965870'
chicago: Amani, Mohammad Hossein, Simone Bombari, Marco Mondelli, Rattana Pukdee,
and Stefano Rini. “Sharp Asymptotics on the Compression of Two-Layer Neural Networks.”
IEEE Information Theory Workshop. IEEE, 2022. https://doi.org/10.1109/ITW54588.2022.9965870.
ieee: M. H. Amani, S. Bombari, M. Mondelli, R. Pukdee, and S. Rini, “Sharp asymptotics
on the compression of two-layer neural networks,” IEEE Information Theory Workshop.
IEEE, pp. 588–593, 2022.
ista: Amani MH, Bombari S, Mondelli M, Pukdee R, Rini S. 2022. Sharp asymptotics
on the compression of two-layer neural networks. IEEE Information Theory Workshop.,
588–593.
mla: Amani, Mohammad Hossein, et al. “Sharp Asymptotics on the Compression of Two-Layer
Neural Networks.” IEEE Information Theory Workshop, IEEE, 2022, pp. 588–93,
doi:10.1109/ITW54588.2022.9965870.
short: M.H. Amani, S. Bombari, M. Mondelli, R. Pukdee, S. Rini, IEEE Information
Theory Workshop (2022) 588–593.
conference:
end_date: 2022-11-09
location: Mumbai, India
name: 'ITW: Information Theory Workshop'
start_date: 2022-11-01
date_created: 2023-02-10T13:47:56Z
date_published: 2022-11-16T00:00:00Z
date_updated: 2023-12-18T11:31:47Z
day: '16'
department:
- _id: MaMo
doi: 10.1109/ITW54588.2022.9965870
external_id:
arxiv:
- '2205.08199'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2205.08199'
month: '11'
oa: 1
oa_version: Preprint
page: 588-593
publication: IEEE Information Theory Workshop
publication_identifier:
isbn:
- '9781665483414'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Sharp asymptotics on the compression of two-layer neural networks
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12540'
abstract:
- lang: eng
text: We consider the problem of signal estimation in generalized linear models
defined via rotationally invariant design matrices. Since these matrices can have
an arbitrary spectral distribution, this model is well suited for capturing complex
correlation structures which often arise in applications. We propose a novel family
of approximate message passing (AMP) algorithms for signal estimation, and rigorously
characterize their performance in the high-dimensional limit via a state evolution
recursion. Our rotationally invariant AMP has complexity of the same order as
the existing AMP derived under the restrictive assumption of a Gaussian design;
our algorithm also recovers this existing AMP as a special case. Numerical results
showcase a performance close to Vector AMP (which is conjectured to be Bayes-optimal
in some settings), but obtained with a much lower complexity, as the proposed
algorithm does not require a computationally expensive singular value decomposition.
acknowledgement: The authors would like to thank the anonymous reviewers for their
helpful comments. KK and MM were partially supported by the 2019 Lopez-Loreta Prize.
article_number: '22'
article_processing_charge: No
author:
- first_name: Ramji
full_name: Venkataramanan, Ramji
last_name: Venkataramanan
- first_name: Kevin
full_name: Kögler, Kevin
id: 94ec913c-dc85-11ea-9058-e5051ab2428b
last_name: Kögler
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
citation:
ama: 'Venkataramanan R, Kögler K, Mondelli M. Estimation in rotationally invariant
generalized linear models via approximate message passing. In: Proceedings
of the 39th International Conference on Machine Learning. Vol 162. ML Research
Press; 2022.'
apa: 'Venkataramanan, R., Kögler, K., & Mondelli, M. (2022). Estimation in rotationally
invariant generalized linear models via approximate message passing. In Proceedings
of the 39th International Conference on Machine Learning (Vol. 162). Baltimore,
MD, United States: ML Research Press.'
chicago: Venkataramanan, Ramji, Kevin Kögler, and Marco Mondelli. “Estimation in
Rotationally Invariant Generalized Linear Models via Approximate Message Passing.”
In Proceedings of the 39th International Conference on Machine Learning,
Vol. 162. ML Research Press, 2022.
ieee: R. Venkataramanan, K. Kögler, and M. Mondelli, “Estimation in rotationally
invariant generalized linear models via approximate message passing,” in Proceedings
of the 39th International Conference on Machine Learning, Baltimore, MD, United
States, 2022, vol. 162.
ista: 'Venkataramanan R, Kögler K, Mondelli M. 2022. Estimation in rotationally
invariant generalized linear models via approximate message passing. Proceedings
of the 39th International Conference on Machine Learning. ICML: International
Conference on Machine Learning vol. 162, 22.'
mla: Venkataramanan, Ramji, et al. “Estimation in Rotationally Invariant Generalized
Linear Models via Approximate Message Passing.” Proceedings of the 39th International
Conference on Machine Learning, vol. 162, 22, ML Research Press, 2022.
short: R. Venkataramanan, K. Kögler, M. Mondelli, in:, Proceedings of the 39th International
Conference on Machine Learning, ML Research Press, 2022.
conference:
end_date: 2022-07-23
location: Baltimore, MD, United States
name: 'ICML: International Conference on Machine Learning'
start_date: 2022-07-17
date_created: 2023-02-10T13:49:04Z
date_published: 2022-01-01T00:00:00Z
date_updated: 2024-09-10T13:03:17Z
ddc:
- '000'
department:
- _id: MaMo
file:
- access_level: open_access
checksum: 67436eb0a660789514cdf9db79e84683
content_type: application/pdf
creator: dernst
date_created: 2023-02-13T10:53:11Z
date_updated: 2023-02-13T10:53:11Z
file_id: '12547'
file_name: 2022_PMLR_Venkataramanan.pdf
file_size: 2341343
relation: main_file
success: 1
file_date_updated: 2023-02-13T10:53:11Z
has_accepted_license: '1'
intvolume: ' 162'
language:
- iso: eng
oa: 1
oa_version: Published Version
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: Proceedings of the 39th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
status: public
title: Estimation in rotationally invariant generalized linear models via approximate
message passing
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 162
year: '2022'
...
---
_id: '12568'
abstract:
- lang: eng
text: We treat the problem of risk-aware control for stochastic shortest path (SSP)
on Markov decision processes (MDP). Typically, expectation is considered for SSP,
which however is oblivious to the incurred risk. We present an alternative view,
instead optimizing conditional value-at-risk (CVaR), an established risk measure.
We treat both Markov chains as well as MDP and introduce, through novel insights,
two algorithms, based on linear programming and value iteration, respectively.
Both algorithms offer precise and provably correct solutions. Evaluation of our
prototype implementation shows that risk-aware control is feasible on several
moderately sized models.
article_processing_charge: No
arxiv: 1
author:
- first_name: Tobias
full_name: Meggendorfer, Tobias
id: b21b0c15-30a2-11eb-80dc-f13ca25802e1
last_name: Meggendorfer
orcid: 0000-0002-1712-2165
citation:
ama: 'Meggendorfer T. Risk-aware stochastic shortest path. In: Proceedings of
the 36th AAAI Conference on Artificial Intelligence, AAAI 2022. Vol 36. Association
for the Advancement of Artificial Intelligence; 2022:9858-9867. doi:10.1609/aaai.v36i9.21222'
apa: 'Meggendorfer, T. (2022). Risk-aware stochastic shortest path. In Proceedings
of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022 (Vol. 36,
pp. 9858–9867). Virtual: Association for the Advancement of Artificial Intelligence.
https://doi.org/10.1609/aaai.v36i9.21222'
chicago: Meggendorfer, Tobias. “Risk-Aware Stochastic Shortest Path.” In Proceedings
of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, 36:9858–67.
Association for the Advancement of Artificial Intelligence, 2022. https://doi.org/10.1609/aaai.v36i9.21222.
ieee: T. Meggendorfer, “Risk-aware stochastic shortest path,” in Proceedings
of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, Virtual,
2022, vol. 36, no. 9, pp. 9858–9867.
ista: Meggendorfer T. 2022. Risk-aware stochastic shortest path. Proceedings of
the 36th AAAI Conference on Artificial Intelligence, AAAI 2022. Conference on
Artificial Intelligence vol. 36, 9858–9867.
mla: Meggendorfer, Tobias. “Risk-Aware Stochastic Shortest Path.” Proceedings
of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, vol. 36,
no. 9, Association for the Advancement of Artificial Intelligence, 2022, pp. 9858–67,
doi:10.1609/aaai.v36i9.21222.
short: T. Meggendorfer, in:, Proceedings of the 36th AAAI Conference on Artificial
Intelligence, AAAI 2022, Association for the Advancement of Artificial Intelligence,
2022, pp. 9858–9867.
conference:
end_date: 2022-03-01
location: Virtual
name: Conference on Artificial Intelligence
start_date: 2022-02-22
date_created: 2023-02-19T23:00:56Z
date_published: 2022-06-28T00:00:00Z
date_updated: 2023-02-20T07:19:12Z
day: '28'
department:
- _id: KrCh
doi: 10.1609/aaai.v36i9.21222
external_id:
arxiv:
- '2203.01640'
intvolume: ' 36'
issue: '9'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2203.01640'
month: '06'
oa: 1
oa_version: Preprint
page: 9858-9867
publication: Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI
2022
publication_identifier:
eissn:
- 2374-3468
isbn:
- '1577358767'
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: Risk-aware stochastic shortest path
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 36
year: '2022'
...
---
_id: '12660'
abstract:
- lang: eng
text: 'We present Cross-Client Label Propagation(XCLP), a new method for transductive
federated learning. XCLP estimates a data graph jointly from the data of multiple
clients and computes labels for the unlabeled data by propagating label information
across the graph. To avoid clients having to share their data with anyone, XCLP
employs two cryptographically secure protocols: secure Hamming distance computation
and secure summation. We demonstrate two distinct applications of XCLP within
federated learning. In the first, we use it in a one-shot way to predict labels
for unseen test points. In the second, we use it to repeatedly pseudo-label unlabeled
training data in a federated semi-supervised setting. Experiments on both real
federated and standard benchmark datasets show that in both applications XCLP
achieves higher classification accuracy than alternative approaches.'
article_number: '2210.06434'
article_processing_charge: No
arxiv: 1
author:
- first_name: Jonathan A
full_name: Scott, Jonathan A
id: e499926b-f6e0-11ea-865d-9c63db0031e8
last_name: Scott
- first_name: Michelle X
full_name: Yeo, Michelle X
id: 2D82B818-F248-11E8-B48F-1D18A9856A87
last_name: Yeo
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: Scott JA, Yeo MX, Lampert C. Cross-client Label Propagation for transductive
federated learning. arXiv. doi:10.48550/arXiv.2210.06434
apa: Scott, J. A., Yeo, M. X., & Lampert, C. (n.d.). Cross-client Label Propagation
for transductive federated learning. arXiv. https://doi.org/10.48550/arXiv.2210.06434
chicago: Scott, Jonathan A, Michelle X Yeo, and Christoph Lampert. “Cross-Client
Label Propagation for Transductive Federated Learning.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2210.06434.
ieee: J. A. Scott, M. X. Yeo, and C. Lampert, “Cross-client Label Propagation for
transductive federated learning,” arXiv. .
ista: Scott JA, Yeo MX, Lampert C. Cross-client Label Propagation for transductive
federated learning. arXiv, 2210.06434.
mla: Scott, Jonathan A., et al. “Cross-Client Label Propagation for Transductive
Federated Learning.” ArXiv, 2210.06434, doi:10.48550/arXiv.2210.06434.
short: J.A. Scott, M.X. Yeo, C. Lampert, ArXiv (n.d.).
date_created: 2023-02-20T08:21:50Z
date_published: 2022-10-12T00:00:00Z
date_updated: 2023-02-21T08:20:18Z
day: '12'
ddc:
- '004'
department:
- _id: ChLa
doi: 10.48550/arXiv.2210.06434
external_id:
arxiv:
- '2210.06434'
file:
- access_level: open_access
checksum: 7ab20543fd4393f14fb857ce2e4f03c6
content_type: application/pdf
creator: chl
date_created: 2023-02-20T08:21:35Z
date_updated: 2023-02-20T08:21:35Z
file_id: '12661'
file_name: 2210.06434.pdf
file_size: 291893
relation: main_file
success: 1
file_date_updated: 2023-02-20T08:21:35Z
has_accepted_license: '1'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Cross-client Label Propagation for transductive federated 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: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12662'
abstract:
- lang: eng
text: 'Modern machine learning tasks often require considering not just one but
multiple objectives. For example, besides the prediction quality, this could be
the efficiency, robustness or fairness of the learned models, or any of their
combinations. Multi-objective learning offers a natural framework for handling
such problems without having to commit to early trade-offs. Surprisingly, statistical
learning theory so far offers almost no insight into the generalization properties
of multi-objective learning. In this work, we make first steps to fill this gap:
we establish foundational generalization bounds for the multi-objective setting
as well as generalization and excess bounds for learning with scalarizations.
We also provide the first theoretical analysis of the relation between the Pareto-optimal
sets of the true objectives and the Pareto-optimal sets of their empirical approximations
from training data. In particular, we show a surprising asymmetry: all Pareto-optimal
solutions can be approximated by empirically Pareto-optimal ones, but not vice
versa.'
article_number: '2208.13499'
article_processing_charge: No
arxiv: 1
author:
- first_name: Peter
full_name: Súkeník, Peter
id: d64d6a8d-eb8e-11eb-b029-96fd216dec3c
last_name: Súkeník
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: Súkeník P, Lampert C. Generalization in Multi-objective machine learning. arXiv.
doi:10.48550/arXiv.2208.13499
apa: Súkeník, P., & Lampert, C. (n.d.). Generalization in Multi-objective machine
learning. arXiv. https://doi.org/10.48550/arXiv.2208.13499
chicago: Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective
Machine Learning.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2208.13499.
ieee: P. Súkeník and C. Lampert, “Generalization in Multi-objective machine learning,”
arXiv. .
ista: Súkeník P, Lampert C. Generalization in Multi-objective machine learning.
arXiv, 2208.13499.
mla: Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine
Learning.” ArXiv, 2208.13499, doi:10.48550/arXiv.2208.13499.
short: P. Súkeník, C. Lampert, ArXiv (n.d.).
date_created: 2023-02-20T08:23:06Z
date_published: 2022-08-29T00:00:00Z
date_updated: 2023-02-21T08:24:55Z
day: '29'
ddc:
- '004'
department:
- _id: ChLa
doi: 10.48550/arXiv.2208.13499
external_id:
arxiv:
- '2208.13499'
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2208.13499'
month: '08'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Generalization in Multi-objective machine learning
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12670'
abstract:
- lang: eng
text: DNA methylation plays essential homeostatic functions in eukaryotic genomes.
In animals, DNA methylation is also developmentally regulated and, in turn, regulates
development. In the past two decades, huge research effort has endorsed the understanding
that DNA methylation plays a similar role in plant development, especially during
sexual reproduction. The power of whole-genome sequencing and cell isolation techniques,
as well as bioinformatics tools, have enabled recent studies to reveal dynamic
changes in DNA methylation during germline development. Furthermore, the combination
of these technological advances with genetics, developmental biology and cell
biology tools has revealed functional methylation reprogramming events that control
gene and transposon activities in flowering plant germlines. In this review, we
discuss the major advances in our knowledge of DNA methylation dynamics during
male and female germline development in flowering plants.
article_processing_charge: No
article_type: review
author:
- first_name: Shengbo
full_name: He, Shengbo
last_name: He
- first_name: Xiaoqi
full_name: Feng, Xiaoqi
id: e0164712-22ee-11ed-b12a-d80fcdf35958
last_name: Feng
orcid: 0000-0002-4008-1234
citation:
ama: He S, Feng X. DNA methylation dynamics during germline development. Journal
of Integrative Plant Biology. 2022;64(12):2240-2251. doi:10.1111/jipb.13422
apa: He, S., & Feng, X. (2022). DNA methylation dynamics during germline development.
Journal of Integrative Plant Biology. Wiley. https://doi.org/10.1111/jipb.13422
chicago: He, Shengbo, and Xiaoqi Feng. “DNA Methylation Dynamics during Germline
Development.” Journal of Integrative Plant Biology. Wiley, 2022. https://doi.org/10.1111/jipb.13422.
ieee: S. He and X. Feng, “DNA methylation dynamics during germline development,”
Journal of Integrative Plant Biology, vol. 64, no. 12. Wiley, pp. 2240–2251,
2022.
ista: He S, Feng X. 2022. DNA methylation dynamics during germline development.
Journal of Integrative Plant Biology. 64(12), 2240–2251.
mla: He, Shengbo, and Xiaoqi Feng. “DNA Methylation Dynamics during Germline Development.”
Journal of Integrative Plant Biology, vol. 64, no. 12, Wiley, 2022, pp.
2240–51, doi:10.1111/jipb.13422.
short: S. He, X. Feng, Journal of Integrative Plant Biology 64 (2022) 2240–2251.
date_created: 2023-02-23T09:15:57Z
date_published: 2022-12-07T00:00:00Z
date_updated: 2023-05-08T10:59:00Z
day: '07'
department:
- _id: XiFe
doi: 10.1111/jipb.13422
extern: '1'
external_id:
pmid:
- '36478632'
intvolume: ' 64'
issue: '12'
keyword:
- Plant Science
- General Biochemistry
- Genetics and Molecular Biology
- Biochemistry
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1111/jipb.13422
month: '12'
oa: 1
oa_version: Published Version
page: 2240-2251
pmid: 1
publication: Journal of Integrative Plant Biology
publication_identifier:
eissn:
- 1744-7909
issn:
- 1672-9072
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: DNA methylation dynamics during germline development
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 64
year: '2022'
...
---
_id: '12671'
abstract:
- lang: eng
text: Sperm chromatin is typically transformed by protamines into a compact and
transcriptionally inactive state1,2. Sperm cells of flowering plants lack protamines,
yet they have small, transcriptionally active nuclei with chromatin condensed
through an unknown mechanism3,4. Here we show that a histone variant, H2B.8, mediates
sperm chromatin and nuclear condensation in Arabidopsis thaliana. Loss of H2B.8
causes enlarged sperm nuclei with dispersed chromatin, whereas ectopic expression
in somatic cells produces smaller nuclei with aggregated chromatin. This result
demonstrates that H2B.8 is sufficient for chromatin condensation. H2B.8 aggregates
transcriptionally inactive AT-rich chromatin into phase-separated condensates,
which facilitates nuclear compaction without reducing transcription. Reciprocal
crosses show that mutation of h2b.8 reduces male transmission, which suggests
that H2B.8-mediated sperm compaction is important for fertility. Altogether, our
results reveal a new mechanism of nuclear compaction through global aggregation
of unexpressed chromatin. We propose that H2B.8 is an evolutionary innovation
of flowering plants that achieves nuclear condensation compatible with active
transcription.
article_processing_charge: No
article_type: original
author:
- first_name: Toby
full_name: Buttress, Toby
last_name: Buttress
- first_name: Shengbo
full_name: He, Shengbo
last_name: He
- first_name: Liang
full_name: Wang, Liang
last_name: Wang
- first_name: Shaoli
full_name: Zhou, Shaoli
last_name: Zhou
- first_name: Gerhard
full_name: Saalbach, Gerhard
last_name: Saalbach
- first_name: Martin
full_name: Vickers, Martin
last_name: Vickers
- first_name: Guohong
full_name: Li, Guohong
last_name: Li
- first_name: Pilong
full_name: Li, Pilong
last_name: Li
- first_name: Xiaoqi
full_name: Feng, Xiaoqi
id: e0164712-22ee-11ed-b12a-d80fcdf35958
last_name: Feng
orcid: 0000-0002-4008-1234
citation:
ama: Buttress T, He S, Wang L, et al. Histone H2B.8 compacts flowering plant sperm
through chromatin phase separation. Nature. 2022;611(7936):614-622. doi:10.1038/s41586-022-05386-6
apa: Buttress, T., He, S., Wang, L., Zhou, S., Saalbach, G., Vickers, M., … Feng,
X. (2022). Histone H2B.8 compacts flowering plant sperm through chromatin phase
separation. Nature. Springer Nature. https://doi.org/10.1038/s41586-022-05386-6
chicago: Buttress, Toby, Shengbo He, Liang Wang, Shaoli Zhou, Gerhard Saalbach,
Martin Vickers, Guohong Li, Pilong Li, and Xiaoqi Feng. “Histone H2B.8 Compacts
Flowering Plant Sperm through Chromatin Phase Separation.” Nature. Springer
Nature, 2022. https://doi.org/10.1038/s41586-022-05386-6.
ieee: T. Buttress et al., “Histone H2B.8 compacts flowering plant sperm through
chromatin phase separation,” Nature, vol. 611, no. 7936. Springer Nature,
pp. 614–622, 2022.
ista: Buttress T, He S, Wang L, Zhou S, Saalbach G, Vickers M, Li G, Li P, Feng
X. 2022. Histone H2B.8 compacts flowering plant sperm through chromatin phase
separation. Nature. 611(7936), 614–622.
mla: Buttress, Toby, et al. “Histone H2B.8 Compacts Flowering Plant Sperm through
Chromatin Phase Separation.” Nature, vol. 611, no. 7936, Springer Nature,
2022, pp. 614–22, doi:10.1038/s41586-022-05386-6.
short: T. Buttress, S. He, L. Wang, S. Zhou, G. Saalbach, M. Vickers, G. Li, P.
Li, X. Feng, Nature 611 (2022) 614–622.
date_created: 2023-02-23T09:17:05Z
date_published: 2022-11-17T00:00:00Z
date_updated: 2023-05-08T10:59:22Z
day: '17'
department:
- _id: XiFe
doi: 10.1038/s41586-022-05386-6
extern: '1'
external_id:
pmid:
- '36323776'
intvolume: ' 611'
issue: '7936'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1038/s41586-022-05386-6
month: '11'
oa: 1
oa_version: Published Version
page: 614-622
pmid: 1
publication: Nature
publication_identifier:
eissn:
- 1476-4687
issn:
- 0028-0836
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Histone H2B.8 compacts flowering plant sperm through chromatin phase separation
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 611
year: '2022'
...
---
_id: '12677'
abstract:
- lang: eng
text: "In modern sample-driven Prophet Inequality, an adversary chooses a sequence
of n items with values v1,v2,…,vn to be presented to a decision maker (DM). The
process follows in two phases. In the first phase (sampling phase), some items,
possibly selected at random, are revealed to the DM, but she can never accept
them. In the second phase, the DM is presented with the other items in a random
order and online fashion. For each item, she must make an irrevocable decision
to either accept the item and stop the process or reject the item forever and
proceed to the next item. The goal of the DM is to maximize the expected value
as compared to a Prophet (or offline algorithm) that has access to all information.
In this setting, the sampling phase has no cost and is not part of the optimization
process. However, in many scenarios, the samples are obtained as part of the decision-making
process.\r\nWe model this aspect as a two-phase Prophet Inequality where an adversary
chooses a sequence of 2n items with values v1,v2,…,v2n and the items are randomly
ordered. Finally, there are two phases of the Prophet Inequality problem with
the first n-items and the rest of the items, respectively. We show that some basic
algorithms achieve a ratio of at most 0.450. We present an algorithm that achieves
a ratio of at least 0.495. Finally, we show that for every algorithm the ratio
it can achieve is at most 0.502. Hence our algorithm is near-optimal."
acknowledgement: This research was partially supported by the ERC CoG 863818 (ForM-SMArt)
grant.
article_number: '2209.14368'
article_processing_charge: No
arxiv: 1
author:
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Mona
full_name: Mohammadi, Mona
id: 4363614d-b686-11ed-a7d5-ac9e4a24bc2e
last_name: Mohammadi
- first_name: Raimundo J
full_name: Saona Urmeneta, Raimundo J
id: BD1DF4C4-D767-11E9-B658-BC13E6697425
last_name: Saona Urmeneta
orcid: 0000-0001-5103-038X
citation:
ama: Chatterjee K, Mohammadi M, Saona Urmeneta RJ. Repeated prophet inequality with
near-optimal bounds. arXiv. doi:10.48550/ARXIV.2209.14368
apa: Chatterjee, K., Mohammadi, M., & Saona Urmeneta, R. J. (n.d.). Repeated
prophet inequality with near-optimal bounds. arXiv. https://doi.org/10.48550/ARXIV.2209.14368
chicago: Chatterjee, Krishnendu, Mona Mohammadi, and Raimundo J Saona Urmeneta.
“Repeated Prophet Inequality with Near-Optimal Bounds.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2209.14368.
ieee: K. Chatterjee, M. Mohammadi, and R. J. Saona Urmeneta, “Repeated prophet inequality
with near-optimal bounds,” arXiv. .
ista: Chatterjee K, Mohammadi M, Saona Urmeneta RJ. Repeated prophet inequality
with near-optimal bounds. arXiv, 2209.14368.
mla: Chatterjee, Krishnendu, et al. “Repeated Prophet Inequality with Near-Optimal
Bounds.” ArXiv, 2209.14368, doi:10.48550/ARXIV.2209.14368.
short: K. Chatterjee, M. Mohammadi, R.J. Saona Urmeneta, ArXiv (n.d.).
date_created: 2023-02-24T12:21:40Z
date_published: 2022-09-28T00:00:00Z
date_updated: 2025-07-14T09:09:51Z
day: '28'
department:
- _id: GradSch
- _id: KrCh
doi: 10.48550/ARXIV.2209.14368
ec_funded: 1
external_id:
arxiv:
- '2209.14368'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2209.14368'
month: '09'
oa: 1
oa_version: Preprint
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: arXiv
publication_status: submitted
status: public
title: Repeated prophet inequality with near-optimal bounds
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12684'
abstract:
- lang: eng
text: Given a place ω of a global function field K over a finite field, with
associated affine function ring Rω and completion Kω , the aim of this paper
is to give an effective joint equidistribution result for renormalized primitive
lattice points (a,b)∈Rω2 in the plane Kω2 , and for renormalized solutions
to the gcd equation ax+by=1 . The main tools are techniques of Goronik and Nevo
for counting lattice points in well-rounded families of subsets. This gives a
sharper analog in positive characteristic of a result of Nevo and the first author
for the equidistribution of the primitive lattice points in \ZZ2 .
acknowledgement: "The authors warmly thank Amos Nevo for having presented the authors
to each other during\r\na beautiful conference in Goa in February 2016, where the
idea of this paper was born. The\r\nfirst author thanks the IHES for two post-doctoral
years when most of this paper was discussed,\r\nand the Topology team in Orsay for
financial support at the final stage. The first author was\r\nsupported by the EPRSC
EP/P026710/1 grant. Finally, we warmly thank the referee for many\r\nvery helpful
comments that have improved the readability of this paper."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Tal
full_name: Horesh, Tal
id: C8B7BF48-8D81-11E9-BCA9-F536E6697425
last_name: Horesh
- first_name: Frédéric
full_name: Paulin, Frédéric
last_name: Paulin
citation:
ama: Horesh T, Paulin F. Effective equidistribution of lattice points in positive
characteristic. Journal de Theorie des Nombres de Bordeaux. 2022;34(3):679-703.
doi:10.5802/JTNB.1222
apa: Horesh, T., & Paulin, F. (2022). Effective equidistribution of lattice
points in positive characteristic. Journal de Theorie Des Nombres de Bordeaux.
Centre Mersenne. https://doi.org/10.5802/JTNB.1222
chicago: Horesh, Tal, and Frédéric Paulin. “Effective Equidistribution of Lattice
Points in Positive Characteristic.” Journal de Theorie Des Nombres de Bordeaux.
Centre Mersenne, 2022. https://doi.org/10.5802/JTNB.1222.
ieee: T. Horesh and F. Paulin, “Effective equidistribution of lattice points in
positive characteristic,” Journal de Theorie des Nombres de Bordeaux, vol.
34, no. 3. Centre Mersenne, pp. 679–703, 2022.
ista: Horesh T, Paulin F. 2022. Effective equidistribution of lattice points in
positive characteristic. Journal de Theorie des Nombres de Bordeaux. 34(3), 679–703.
mla: Horesh, Tal, and Frédéric Paulin. “Effective Equidistribution of Lattice Points
in Positive Characteristic.” Journal de Theorie Des Nombres de Bordeaux,
vol. 34, no. 3, Centre Mersenne, 2022, pp. 679–703, doi:10.5802/JTNB.1222.
short: T. Horesh, F. Paulin, Journal de Theorie Des Nombres de Bordeaux 34 (2022)
679–703.
date_created: 2023-02-26T23:01:02Z
date_published: 2022-01-27T00:00:00Z
date_updated: 2023-08-04T10:41:40Z
day: '27'
ddc:
- '510'
department:
- _id: TiBr
doi: 10.5802/JTNB.1222
external_id:
arxiv:
- '2001.01534'
isi:
- '000926504300003'
file:
- access_level: open_access
checksum: 08f28fded270251f568f610cf5166d69
content_type: application/pdf
creator: dernst
date_created: 2023-02-27T09:10:13Z
date_updated: 2023-02-27T09:10:13Z
file_id: '12689'
file_name: 2023_JourTheorieNombreBordeaux_Horesh.pdf
file_size: 870468
relation: main_file
success: 1
file_date_updated: 2023-02-27T09:10:13Z
has_accepted_license: '1'
intvolume: ' 34'
isi: 1
issue: '3'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
page: 679-703
publication: Journal de Theorie des Nombres de Bordeaux
publication_identifier:
eissn:
- 2118-8572
issn:
- 1246-7405
publication_status: published
publisher: Centre Mersenne
quality_controlled: '1'
scopus_import: '1'
status: public
title: Effective equidistribution of lattice points in positive characteristic
tmp:
image: /image/cc_by_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nd/4.0/legalcode
name: Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)
short: CC BY-ND (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 34
year: '2022'
...
---
_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
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
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'
...