---
_id: '14922'
abstract:
- lang: eng
text: 'We propose a novel approach to concentration for non-independent random variables.
The main idea is to ``pretend'''' that the random variables are independent and
pay a multiplicative price measuring how far they are from actually being independent.
This price is encapsulated in the Hellinger integral between the joint and the
product of the marginals, which is then upper bounded leveraging tensorisation
properties. Our bounds represent a natural generalisation of concentration inequalities
in the presence of dependence: we recover exactly the classical bounds (McDiarmid''s
inequality) when the random variables are independent. Furthermore, in a ``large
deviations'''' regime, we obtain the same decay in the probability as for the
independent case, even when the random variables display non-trivial dependencies.
To show this, we consider a number of applications of interest. First, we provide
a bound for Markov chains with finite state space. Then, we consider the Simple
Symmetric Random Walk, which is a non-contracting Markov chain, and a non-Markovian
setting in which the stochastic process depends on its entire past. To conclude,
we propose an application to Markov Chain Monte Carlo methods, where our approach
leads to an improved lower bound on the minimum burn-in period required to reach
a certain accuracy. In all of these settings, we provide a regime of parameters
in which our bound fares better than what the state of the art can provide.'
acknowledgement: The authors are partially supported by the 2019 Lopez-Loreta Prize.
They would also like to thank Professor Jan Maas for providing valuable suggestions
and comments on an early version of the work.
article_processing_charge: No
arxiv: 1
author:
- first_name: Amedeo Roberto
full_name: Esposito, Amedeo Roberto
id: 9583e921-e1ad-11ec-9862-cef099626dc9
last_name: Esposito
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
citation:
ama: 'Esposito AR, Mondelli M. Concentration without independence via information
measures. In: Proceedings of 2023 IEEE International Symposium on Information
Theory. IEEE; 2023:400-405. doi:10.1109/isit54713.2023.10206899'
apa: 'Esposito, A. R., & Mondelli, M. (2023). Concentration without independence
via information measures. In Proceedings of 2023 IEEE International Symposium
on Information Theory (pp. 400–405). Taipei, Taiwan: IEEE. https://doi.org/10.1109/isit54713.2023.10206899'
chicago: Esposito, Amedeo Roberto, and Marco Mondelli. “Concentration without Independence
via Information Measures.” In Proceedings of 2023 IEEE International Symposium
on Information Theory, 400–405. IEEE, 2023. https://doi.org/10.1109/isit54713.2023.10206899.
ieee: A. R. Esposito and M. Mondelli, “Concentration without independence via information
measures,” in Proceedings of 2023 IEEE International Symposium on Information
Theory, Taipei, Taiwan, 2023, pp. 400–405.
ista: 'Esposito AR, Mondelli M. 2023. Concentration without independence via information
measures. Proceedings of 2023 IEEE International Symposium on Information Theory.
ISIT: International Symposium on Information Theory, 400–405.'
mla: Esposito, Amedeo Roberto, and Marco Mondelli. “Concentration without Independence
via Information Measures.” Proceedings of 2023 IEEE International Symposium
on Information Theory, IEEE, 2023, pp. 400–05, doi:10.1109/isit54713.2023.10206899.
short: A.R. Esposito, M. Mondelli, in:, Proceedings of 2023 IEEE International Symposium
on Information Theory, IEEE, 2023, pp. 400–405.
conference:
end_date: 2023-06-30
location: Taipei, Taiwan
name: 'ISIT: International Symposium on Information Theory'
start_date: 2023-06-25
corr_author: '1'
date_created: 2024-02-02T11:18:40Z
date_published: 2023-06-30T00:00:00Z
date_updated: 2025-09-04T13:06:52Z
day: '30'
department:
- _id: MaMo
doi: 10.1109/isit54713.2023.10206899
external_id:
arxiv:
- '2303.07245'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2303.07245
month: '06'
oa: 1
oa_version: Preprint
page: 400-405
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: Proceedings of 2023 IEEE International Symposium on Information Theory
publication_identifier:
eisbn:
- '9781665475549'
eissn:
- 2157-8117
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
record:
- id: '15172'
relation: later_version
status: public
scopus_import: '1'
status: public
title: Concentration without independence via information measures
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14923'
abstract:
- lang: eng
text: We study the performance of a Bayesian statistician who estimates a rank-one
signal corrupted by non-symmetric rotationally invariant noise with a generic
distribution of singular values. As the signal-to-noise ratio and the noise structure
are unknown, a Gaussian setup is incorrectly assumed. We derive the exact analytic
expression for the error of the mismatched Bayes estimator and also provide the
analysis of an approximate message passing (AMP) algorithm. The first result exploits
the asymptotic behavior of spherical integrals for rectangular matrices and of
low-rank matrix perturbations; the second one relies on the design and analysis
of an auxiliary AMP. The numerical experiments show that there is a performance
gap between the AMP and Bayes estimators, which is due to the incorrect estimation
of the signal norm.
article_processing_charge: No
arxiv: 1
author:
- first_name: Teng
full_name: Fu, Teng
last_name: Fu
- first_name: YuHao
full_name: Liu, YuHao
last_name: Liu
- first_name: Jean
full_name: Barbier, Jean
last_name: Barbier
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
- first_name: ShanSuo
full_name: Liang, ShanSuo
last_name: Liang
- first_name: TianQi
full_name: Hou, TianQi
last_name: Hou
citation:
ama: 'Fu T, Liu Y, Barbier J, Mondelli M, Liang S, Hou T. Mismatched estimation
of non-symmetric rank-one matrices corrupted by structured noise. In: Proceedings
of 2023 IEEE International Symposium on Information Theory. IEEE; 2023:1178-1183.
doi:10.1109/isit54713.2023.10206671'
apa: 'Fu, T., Liu, Y., Barbier, J., Mondelli, M., Liang, S., & Hou, T. (2023).
Mismatched estimation of non-symmetric rank-one matrices corrupted by structured
noise. In Proceedings of 2023 IEEE International Symposium on Information Theory
(pp. 1178–1183). Taipei, Taiwan: IEEE. https://doi.org/10.1109/isit54713.2023.10206671'
chicago: Fu, Teng, YuHao Liu, Jean Barbier, Marco Mondelli, ShanSuo Liang, and TianQi
Hou. “Mismatched Estimation of Non-Symmetric Rank-One Matrices Corrupted by Structured
Noise.” In Proceedings of 2023 IEEE International Symposium on Information
Theory, 1178–83. IEEE, 2023. https://doi.org/10.1109/isit54713.2023.10206671.
ieee: T. Fu, Y. Liu, J. Barbier, M. Mondelli, S. Liang, and T. Hou, “Mismatched
estimation of non-symmetric rank-one matrices corrupted by structured noise,”
in Proceedings of 2023 IEEE International Symposium on Information Theory,
Taipei, Taiwan, 2023, pp. 1178–1183.
ista: 'Fu T, Liu Y, Barbier J, Mondelli M, Liang S, Hou T. 2023. Mismatched estimation
of non-symmetric rank-one matrices corrupted by structured noise. Proceedings
of 2023 IEEE International Symposium on Information Theory. ISIT: International
Symposium on Information Theory, 1178–1183.'
mla: Fu, Teng, et al. “Mismatched Estimation of Non-Symmetric Rank-One Matrices
Corrupted by Structured Noise.” Proceedings of 2023 IEEE International Symposium
on Information Theory, IEEE, 2023, pp. 1178–83, doi:10.1109/isit54713.2023.10206671.
short: T. Fu, Y. Liu, J. Barbier, M. Mondelli, S. Liang, T. Hou, in:, Proceedings
of 2023 IEEE International Symposium on Information Theory, IEEE, 2023, pp. 1178–1183.
conference:
end_date: 2023-06-30
location: Taipei, Taiwan
name: 'ISIT: International Symposium on Information Theory'
start_date: 2023-06-25
corr_author: '1'
date_created: 2024-02-02T11:20:39Z
date_published: 2023-06-30T00:00:00Z
date_updated: 2025-07-10T11:51:04Z
day: '30'
department:
- _id: MaMo
doi: 10.1109/isit54713.2023.10206671
external_id:
arxiv:
- '2302.03306'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2302.03306
month: '06'
oa: 1
oa_version: Preprint
page: 1178-1183
publication: Proceedings of 2023 IEEE International Symposium on Information Theory
publication_identifier:
eissn:
- 2157-8117
isbn:
- '9781665475549'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Mismatched estimation of non-symmetric rank-one matrices corrupted by structured
noise
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14924'
abstract:
- lang: eng
text: "The stochastic heavy ball method (SHB), also known as stochastic gradient
descent (SGD) with Polyak's momentum, is widely used in training neural networks.
However, despite the remarkable success of such algorithm in practice, its theoretical
characterization remains limited. In this paper, we focus on neural networks with
two and three layers and provide a rigorous understanding of the properties of
the solutions found by SHB: \\emph{(i)} stability after dropping out part of the
neurons, \\emph{(ii)} connectivity along a low-loss path, and \\emph{(iii)} convergence
to the global optimum.\r\nTo achieve this goal, we take a mean-field view and
relate the SHB dynamics to a certain partial differential equation in the limit
of large network widths. This mean-field perspective has inspired a recent line
of work focusing on SGD while, in contrast, our paper considers an algorithm with
momentum. More specifically, after proving existence and uniqueness of the limit
differential equations, we show convergence to the global optimum and give a quantitative
bound between the mean-field limit and the SHB dynamics of a finite-width network.
Armed with this last bound, we are able to establish the dropout-stability and
connectivity of SHB solutions."
acknowledgement: D. Wu and M. Mondelli are partially supported by the 2019 Lopez-Loreta
Prize. V. Kungurtsev was supported by the OP VVV project CZ.02.1.01/0.0/0.0/16_019/0000765
"Research Center for Informatics".
alternative_title:
- TMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Diyuan
full_name: Wu, Diyuan
id: 1a5914c2-896a-11ed-bdf8-fb80621a0635
last_name: Wu
- first_name: Vyacheslav
full_name: Kungurtsev, Vyacheslav
last_name: Kungurtsev
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
citation:
ama: 'Wu D, Kungurtsev V, Mondelli M. Mean-field analysis for heavy ball methods:
Dropout-stability, connectivity, and global convergence. In: Transactions on
Machine Learning Research. ML Research Press; 2023.'
apa: 'Wu, D., Kungurtsev, V., & Mondelli, M. (2023). Mean-field analysis for
heavy ball methods: Dropout-stability, connectivity, and global convergence. In
Transactions on Machine Learning Research. ML Research Press.'
chicago: 'Wu, Diyuan, Vyacheslav Kungurtsev, and Marco Mondelli. “Mean-Field Analysis
for Heavy Ball Methods: Dropout-Stability, Connectivity, and Global Convergence.”
In Transactions on Machine Learning Research. ML Research Press, 2023.'
ieee: 'D. Wu, V. Kungurtsev, and M. Mondelli, “Mean-field analysis for heavy ball
methods: Dropout-stability, connectivity, and global convergence,” in Transactions
on Machine Learning Research, 2023.'
ista: 'Wu D, Kungurtsev V, Mondelli M. 2023. Mean-field analysis for heavy ball
methods: Dropout-stability, connectivity, and global convergence. Transactions
on Machine Learning Research. , TMLR, .'
mla: 'Wu, Diyuan, et al. “Mean-Field Analysis for Heavy Ball Methods: Dropout-Stability,
Connectivity, and Global Convergence.” Transactions on Machine Learning Research,
ML Research Press, 2023.'
short: D. Wu, V. Kungurtsev, M. Mondelli, in:, Transactions on Machine Learning
Research, ML Research Press, 2023.
corr_author: '1'
date_created: 2024-02-02T11:21:56Z
date_published: 2023-02-28T00:00:00Z
date_updated: 2025-04-15T07:50:17Z
day: '28'
department:
- _id: MaMo
external_id:
arxiv:
- '2210.06819'
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2210.06819
month: '02'
oa: 1
oa_version: Published Version
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: Transactions on Machine Learning Research
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
status: public
title: 'Mean-field analysis for heavy ball methods: Dropout-stability, connectivity,
and global convergence'
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: '2023'
...
---
_id: '14459'
abstract:
- lang: eng
text: Autoencoders are a popular model in many branches of machine learning and
lossy data compression. However, their fundamental limits, the performance of
gradient methods and the features learnt during optimization remain poorly understood,
even in the two-layer setting. In fact, earlier work has considered either linear
autoencoders or specific training regimes (leading to vanishing or diverging compression
rates). Our paper addresses this gap by focusing on non-linear two-layer autoencoders
trained in the challenging proportional regime in which the input dimension scales
linearly with the size of the representation. Our results characterize the minimizers
of the population risk, and show that such minimizers are achieved by gradient
methods; their structure is also unveiled, thus leading to a concise description
of the features obtained via training. For the special case of a sign activation
function, our analysis establishes the fundamental limits for the lossy compression
of Gaussian sources via (shallow) autoencoders. Finally, while the results are
proved for Gaussian data, numerical simulations on standard datasets display the
universality of the theoretical predictions.
acknowledgement: Aleksandr Shevchenko, Kevin Kogler and Marco Mondelli are supported
by the 2019 Lopez-Loreta Prize. Hamed Hassani acknowledges the support by the NSF
CIF award (1910056) and the NSF Institute for CORE Emerging Methods in Data Science
(EnCORE).
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Aleksandr
full_name: Shevchenko, Aleksandr
id: F2B06EC2-C99E-11E9-89F0-752EE6697425
last_name: Shevchenko
- first_name: Kevin
full_name: Kögler, Kevin
id: 94ec913c-dc85-11ea-9058-e5051ab2428b
last_name: Kögler
- first_name: Hamed
full_name: Hassani, Hamed
last_name: Hassani
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
citation:
ama: 'Shevchenko A, Kögler K, Hassani H, Mondelli M. Fundamental limits of two-layer
autoencoders, and achieving them with gradient methods. In: Proceedings of
the 40th International Conference on Machine Learning. Vol 202. ML Research
Press; 2023:31151-31209.'
apa: 'Shevchenko, A., Kögler, K., Hassani, H., & Mondelli, M. (2023). Fundamental
limits of two-layer autoencoders, and achieving them with gradient methods. In
Proceedings of the 40th International Conference on Machine Learning (Vol.
202, pp. 31151–31209). Honolulu, Hawaii, HI, United States: ML Research Press.'
chicago: Shevchenko, Alexander, Kevin Kögler, Hamed Hassani, and Marco Mondelli.
“Fundamental Limits of Two-Layer Autoencoders, and Achieving Them with Gradient
Methods.” In Proceedings of the 40th International Conference on Machine Learning,
202:31151–209. ML Research Press, 2023.
ieee: A. Shevchenko, K. Kögler, H. Hassani, and M. Mondelli, “Fundamental limits
of two-layer autoencoders, and achieving them with gradient methods,” in Proceedings
of the 40th International Conference on Machine Learning, Honolulu, Hawaii,
HI, United States, 2023, vol. 202, pp. 31151–31209.
ista: 'Shevchenko A, Kögler K, Hassani H, Mondelli M. 2023. Fundamental limits of
two-layer autoencoders, and achieving them with gradient methods. Proceedings
of the 40th International Conference on Machine Learning. ICML: International
Conference on Machine Learning, PMLR, vol. 202, 31151–31209.'
mla: Shevchenko, Alexander, et al. “Fundamental Limits of Two-Layer Autoencoders,
and Achieving Them with Gradient Methods.” Proceedings of the 40th International
Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 31151–209.
short: A. Shevchenko, K. Kögler, H. Hassani, M. Mondelli, in:, Proceedings of the
40th International Conference on Machine Learning, ML Research Press, 2023, pp.
31151–31209.
conference:
end_date: 2023-07-29
location: Honolulu, Hawaii, HI, United States
name: 'ICML: International Conference on Machine Learning'
start_date: 2023-07-23
corr_author: '1'
date_created: 2023-10-29T23:01:17Z
date_published: 2023-07-30T00:00:00Z
date_updated: 2026-05-20T22:30:05Z
day: '30'
department:
- _id: MaMo
- _id: DaAl
external_id:
arxiv:
- '2212.13468'
intvolume: ' 202'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2212.13468
month: '07'
oa: 1
oa_version: Preprint
page: 31151-31209
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: Proceedings of the 40th International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
record:
- id: '17465'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Fundamental limits of two-layer autoencoders, and achieving them with gradient
methods
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 202
year: '2023'
...
---
_id: '11639'
abstract:
- lang: eng
text: We study the list decodability of different ensembles of codes over the real
alphabet under the assumption of an omniscient adversary. It is a well-known result
that when the source and the adversary have power constraints P and N respectively,
the list decoding capacity is equal to 1/2logP/N. Random spherical codes achieve
constant list sizes, and the goal of the present paper is to obtain a better understanding
of the smallest achievable list size as a function of the gap to capacity. We
show a reduction from arbitrary codes to spherical codes, and derive a lower bound
on the list size of typical random spherical codes. We also give an upper bound
on the list size achievable using nested Construction-A lattices and infinite
Construction-A lattices. We then define and study a class of infinite constellations
that generalize Construction-A lattices and prove upper and lower bounds for the
same. Other goodness properties such as packing goodness and AWGN goodness of
infinite constellations are proved along the way. Finally, we consider random
lattices sampled from the Haar distribution and show that if a certain conjecture
that originates in analytic number theory is true, then the list size grows as
a polynomial function of the gap-to-capacity.
acknowledgement: "This work was done when Shashank Vatedka was at the Chinese University
of Hong Kong, where he was supported in part by CUHK Direct Grants 4055039 and 4055077.
He would like to acknowledge funding from a seed grant offered by IIT Hyderabad
and the Start-up Research Grant (SRG/2020/000910) from the Science and Engineering
Board, India. Yihan Zhang has received funding from the European Union’s Horizon
2020 research and innovation programme\r\nunder grant agreement No 682203-ERC-[Inf-Speed-Tradeoff]."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Yihan
full_name: Zhang, Yihan
id: 2ce5da42-b2ea-11eb-bba5-9f264e9d002c
last_name: Zhang
orcid: 0000-0002-6465-6258
- first_name: Shashank
full_name: Vatedka, Shashank
last_name: Vatedka
citation:
ama: Zhang Y, Vatedka S. List decoding random Euclidean codes and Infinite constellations.
IEEE Transactions on Information Theory. 2022;68(12):7753-7786. doi:10.1109/TIT.2022.3189542
apa: Zhang, Y., & Vatedka, S. (2022). List decoding random Euclidean codes and
Infinite constellations. IEEE Transactions on Information Theory. IEEE.
https://doi.org/10.1109/TIT.2022.3189542
chicago: Zhang, Yihan, and Shashank Vatedka. “List Decoding Random Euclidean Codes
and Infinite Constellations.” IEEE Transactions on Information Theory.
IEEE, 2022. https://doi.org/10.1109/TIT.2022.3189542.
ieee: Y. Zhang and S. Vatedka, “List decoding random Euclidean codes and Infinite
constellations,” IEEE Transactions on Information Theory, vol. 68, no.
12. IEEE, pp. 7753–7786, 2022.
ista: Zhang Y, Vatedka S. 2022. List decoding random Euclidean codes and Infinite
constellations. IEEE Transactions on Information Theory. 68(12), 7753–7786.
mla: Zhang, Yihan, and Shashank Vatedka. “List Decoding Random Euclidean Codes and
Infinite Constellations.” IEEE Transactions on Information Theory, vol.
68, no. 12, IEEE, 2022, pp. 7753–86, doi:10.1109/TIT.2022.3189542.
short: Y. Zhang, S. Vatedka, IEEE Transactions on Information Theory 68 (2022) 7753–7786.
corr_author: '1'
date_created: 2022-07-24T22:01:42Z
date_published: 2022-12-01T00:00:00Z
date_updated: 2024-10-09T21:02:55Z
day: '01'
department:
- _id: MaMo
doi: 10.1109/TIT.2022.3189542
external_id:
arxiv:
- '1901.03790'
isi:
- '000891796100007'
intvolume: ' 68'
isi: 1
issue: '12'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1901.03790
month: '12'
oa: 1
oa_version: Preprint
page: 7753-7786
publication: IEEE Transactions on Information Theory
publication_identifier:
eissn:
- 1557-9654
issn:
- 0018-9448
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: List decoding random Euclidean codes and Infinite constellations
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 68
year: '2022'
...
---
_id: '12011'
abstract:
- lang: eng
text: We characterize the capacity for the discrete-time arbitrarily varying channel
with discrete inputs, outputs, and states when (a) the encoder and decoder do
not share common randomness, (b) the input and state are subject to cost constraints,
(c) the transition matrix of the channel is deterministic given the state, and
(d) at each time step the adversary can only observe the current and past channel
inputs when choosing the state at that time. The achievable strategy involves
stochastic encoding together with list decoding and a disambiguation step. The
converse uses a two-phase "babble-and-push" strategy where the adversary chooses
the state randomly in the first phase, list decodes the output, and then chooses
state inputs to symmetrize the channel in the second phase. These results generalize
prior work on specific channels models (additive, erasure) to general discrete
alphabets and models.
acknowledgement: The work of ADS and ML was supported in part by the US National Science
Foundation under awards CCF-1909468 and CCF-1909451.
article_processing_charge: No
arxiv: 1
author:
- first_name: Yihan
full_name: Zhang, Yihan
id: 2ce5da42-b2ea-11eb-bba5-9f264e9d002c
last_name: Zhang
orcid: 0000-0002-6465-6258
- first_name: Sidharth
full_name: Jaggi, Sidharth
last_name: Jaggi
- first_name: Michael
full_name: Langberg, Michael
last_name: Langberg
- first_name: Anand D.
full_name: Sarwate, Anand D.
last_name: Sarwate
citation:
ama: 'Zhang Y, Jaggi S, Langberg M, Sarwate AD. The capacity of causal adversarial
channels. In: 2022 IEEE International Symposium on Information Theory.
Vol 2022. IEEE; 2022:2523-2528. doi:10.1109/ISIT50566.2022.9834709'
apa: 'Zhang, Y., Jaggi, S., Langberg, M., & Sarwate, A. D. (2022). The capacity
of causal adversarial channels. In 2022 IEEE International Symposium on Information
Theory (Vol. 2022, pp. 2523–2528). Espoo, Finland: IEEE. https://doi.org/10.1109/ISIT50566.2022.9834709'
chicago: Zhang, Yihan, Sidharth Jaggi, Michael Langberg, and Anand D. Sarwate. “The
Capacity of Causal Adversarial Channels.” In 2022 IEEE International Symposium
on Information Theory, 2022:2523–28. IEEE, 2022. https://doi.org/10.1109/ISIT50566.2022.9834709.
ieee: Y. Zhang, S. Jaggi, M. Langberg, and A. D. Sarwate, “The capacity of causal
adversarial channels,” in 2022 IEEE International Symposium on Information
Theory, Espoo, Finland, 2022, vol. 2022, pp. 2523–2528.
ista: 'Zhang Y, Jaggi S, Langberg M, Sarwate AD. 2022. The capacity of causal adversarial
channels. 2022 IEEE International Symposium on Information Theory. ISIT: International
Symposium on Information Theory vol. 2022, 2523–2528.'
mla: Zhang, Yihan, et al. “The Capacity of Causal Adversarial Channels.” 2022
IEEE International Symposium on Information Theory, vol. 2022, IEEE, 2022,
pp. 2523–28, doi:10.1109/ISIT50566.2022.9834709.
short: Y. Zhang, S. Jaggi, M. Langberg, A.D. Sarwate, in:, 2022 IEEE International
Symposium on Information Theory, IEEE, 2022, pp. 2523–2528.
conference:
end_date: 2022-07-01
location: Espoo, Finland
name: 'ISIT: International Symposium on Information Theory'
start_date: 2022-06-26
date_created: 2022-09-04T22:02:03Z
date_published: 2022-08-03T00:00:00Z
date_updated: 2025-09-10T09:40:42Z
day: '03'
department:
- _id: MaMo
doi: 10.1109/ISIT50566.2022.9834709
external_id:
arxiv:
- '2205.06708'
isi:
- '001254261902114'
intvolume: ' 2022'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2205.06708'
month: '08'
oa: 1
oa_version: Preprint
page: 2523-2528
publication: 2022 IEEE International Symposium on Information Theory
publication_identifier:
isbn:
- '9781665421591'
issn:
- 2157-8095
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: The capacity of causal adversarial channels
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 2022
year: '2022'
...
---
_id: '12012'
abstract:
- lang: eng
text: This paper is eligible for the Jack Keil Wolf ISIT Student Paper Award. We
generalize a previous framework for designing utility-optimal differentially private
(DP) mechanisms via graphs, where datasets are vertices in the graph and edges
represent dataset neighborhood. The boundary set contains datasets where an individual’s
response changes the binary-valued query compared to its neighbors. Previous work
was limited to the homogeneous case where the privacy parameter ε across all datasets
was the same and the mechanism at boundary datasets was identical. In our work,
the mechanism can take different distributions at the boundary and the privacy
parameter ε is a function of neighboring datasets, which recovers an earlier definition
of personalized DP as special case. The problem is how to extend the mechanism,
which is only defined at the boundary set, to other datasets in the graph in a
computationally efficient and utility optimal manner. Using the concept of strongest
induced DP condition we solve this problem efficiently in polynomial time (in
the size of the graph).
article_processing_charge: No
arxiv: 1
author:
- first_name: Sahel
full_name: Torkamani, Sahel
id: 0503e7f8-2d05-11ed-aa17-db0640c720fc
last_name: Torkamani
- first_name: Javad B.
full_name: Ebrahimi, Javad B.
last_name: Ebrahimi
- first_name: Parastoo
full_name: Sadeghi, Parastoo
last_name: Sadeghi
- first_name: Rafael G.L.
full_name: D'Oliveira, Rafael G.L.
last_name: D'Oliveira
- first_name: Muriel
full_name: Médard, Muriel
last_name: Médard
citation:
ama: 'Torkamani S, Ebrahimi JB, Sadeghi P, D’Oliveira RGL, Médard M. Heterogeneous
differential privacy via graphs. In: 2022 IEEE International Symposium on Information
Theory. Vol 2022. IEEE; 2022:1623-1628. doi:10.1109/ISIT50566.2022.9834711'
apa: 'Torkamani, S., Ebrahimi, J. B., Sadeghi, P., D’Oliveira, R. G. L., & Médard,
M. (2022). Heterogeneous differential privacy via graphs. In 2022 IEEE International
Symposium on Information Theory (Vol. 2022, pp. 1623–1628). Espoo, Finland:
IEEE. https://doi.org/10.1109/ISIT50566.2022.9834711'
chicago: Torkamani, Sahel, Javad B. Ebrahimi, Parastoo Sadeghi, Rafael G.L. D’Oliveira,
and Muriel Médard. “Heterogeneous Differential Privacy via Graphs.” In 2022
IEEE International Symposium on Information Theory, 2022:1623–28. IEEE, 2022.
https://doi.org/10.1109/ISIT50566.2022.9834711.
ieee: S. Torkamani, J. B. Ebrahimi, P. Sadeghi, R. G. L. D’Oliveira, and M. Médard,
“Heterogeneous differential privacy via graphs,” in 2022 IEEE International
Symposium on Information Theory, Espoo, Finland, 2022, vol. 2022, pp. 1623–1628.
ista: 'Torkamani S, Ebrahimi JB, Sadeghi P, D’Oliveira RGL, Médard M. 2022. Heterogeneous
differential privacy via graphs. 2022 IEEE International Symposium on Information
Theory. ISIT: International Symposium on Information Theory vol. 2022, 1623–1628.'
mla: Torkamani, Sahel, et al. “Heterogeneous Differential Privacy via Graphs.” 2022
IEEE International Symposium on Information Theory, vol. 2022, IEEE, 2022,
pp. 1623–28, doi:10.1109/ISIT50566.2022.9834711.
short: S. Torkamani, J.B. Ebrahimi, P. Sadeghi, R.G.L. D’Oliveira, M. Médard, in:,
2022 IEEE International Symposium on Information Theory, IEEE, 2022, pp. 1623–1628.
conference:
end_date: 2022-07-01
location: Espoo, Finland
name: 'ISIT: International Symposium on Information Theory'
start_date: 2022-06-26
date_created: 2022-09-04T22:02:04Z
date_published: 2022-08-03T00:00:00Z
date_updated: 2025-09-10T09:42:41Z
day: '03'
department:
- _id: MaMo
doi: 10.1109/ISIT50566.2022.9834711
external_id:
arxiv:
- '2203.15429'
isi:
- '001254261901131'
intvolume: ' 2022'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2203.15429
month: '08'
oa: 1
oa_version: Preprint
page: 1623-1628
publication: 2022 IEEE International Symposium on Information Theory
publication_identifier:
isbn:
- '9781665421591'
issn:
- 2157-8095
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Heterogeneous differential privacy via graphs
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 2022
year: '2022'
...
---
_id: '12013'
abstract:
- lang: eng
text: We consider the problem of communication over adversarial channels with feedback.
Two parties comprising sender Alice and receiver Bob seek to communicate reliably.
An adversary James observes Alice's channel transmission entirely and chooses,
maliciously, its additive channel input or jamming state thereby corrupting Bob's
observation. Bob can communicate over a one-way reverse link with Alice; we assume
that transmissions over this feedback link cannot be corrupted by James. Our goal
in this work is to study the optimum throughput or capacity over such channels
with feedback. We first present results for the quadratically-constrained additive
channel where communication is known to be impossible when the noise-to-signal
(power) ratio (NSR) is at least 1. We present a novel achievability scheme to
establish that positive rate communication is possible even when the NSR is as
high as 8/9. We also present new converse upper bounds on the capacity of this
channel under potentially stochastic encoders and decoders. We also study feedback
communication over the more widely studied q-ary alphabet channel under additive
noise. For the q -ary channel, where q > 2, it is well known that capacity is
positive under full feedback if and only if the adversary can corrupt strictly
less than half the transmitted symbols. We generalize this result and show that
the same threshold holds for positive rate communication when the noiseless feedback
may only be partial; our scheme employs a stochastic decoder. We extend this characterization,
albeit partially, to fully deterministic schemes under partial noiseless feedback.
We also present new converse upper bounds for q-ary channels under full feedback,
where the encoder and/or decoder may privately randomize. Our converse results
bring to the fore an interesting alternate expression for the well known converse
bound for the q—ary channel under full feedback which, when specialized to the
binary channel, also equals its known capacity.
article_processing_charge: No
author:
- first_name: Pranav
full_name: Joshi, Pranav
last_name: Joshi
- first_name: Amritakshya
full_name: Purkayastha, Amritakshya
last_name: Purkayastha
- first_name: Yihan
full_name: Zhang, Yihan
id: 2ce5da42-b2ea-11eb-bba5-9f264e9d002c
last_name: Zhang
orcid: 0000-0002-6465-6258
- first_name: Amitalok J.
full_name: Budkuley, Amitalok J.
last_name: Budkuley
- first_name: Sidharth
full_name: Jaggi, Sidharth
last_name: Jaggi
citation:
ama: 'Joshi P, Purkayastha A, Zhang Y, Budkuley AJ, Jaggi S. On the capacity of
additive AVCs with feedback. In: 2022 IEEE International Symposium on Information
Theory. Vol 2022. IEEE; 2022:504-509. doi:10.1109/ISIT50566.2022.9834850'
apa: 'Joshi, P., Purkayastha, A., Zhang, Y., Budkuley, A. J., & Jaggi, S. (2022).
On the capacity of additive AVCs with feedback. In 2022 IEEE International
Symposium on Information Theory (Vol. 2022, pp. 504–509). Espoo, Finland:
IEEE. https://doi.org/10.1109/ISIT50566.2022.9834850'
chicago: Joshi, Pranav, Amritakshya Purkayastha, Yihan Zhang, Amitalok J. Budkuley,
and Sidharth Jaggi. “On the Capacity of Additive AVCs with Feedback.” In 2022
IEEE International Symposium on Information Theory, 2022:504–9. IEEE, 2022.
https://doi.org/10.1109/ISIT50566.2022.9834850.
ieee: P. Joshi, A. Purkayastha, Y. Zhang, A. J. Budkuley, and S. Jaggi, “On the
capacity of additive AVCs with feedback,” in 2022 IEEE International Symposium
on Information Theory, Espoo, Finland, 2022, vol. 2022, pp. 504–509.
ista: 'Joshi P, Purkayastha A, Zhang Y, Budkuley AJ, Jaggi S. 2022. On the capacity
of additive AVCs with feedback. 2022 IEEE International Symposium on Information
Theory. ISIT: International Symposium on Information Theory vol. 2022, 504–509.'
mla: Joshi, Pranav, et al. “On the Capacity of Additive AVCs with Feedback.” 2022
IEEE International Symposium on Information Theory, vol. 2022, IEEE, 2022,
pp. 504–09, doi:10.1109/ISIT50566.2022.9834850.
short: P. Joshi, A. Purkayastha, Y. Zhang, A.J. Budkuley, S. Jaggi, in:, 2022 IEEE
International Symposium on Information Theory, IEEE, 2022, pp. 504–509.
conference:
end_date: 2022-07-01
location: Espoo, Finland
name: 'ISIT: International Symposium on Information Theory'
start_date: 2022-06-26
date_created: 2022-09-04T22:02:04Z
date_published: 2022-08-03T00:00:00Z
date_updated: 2025-09-10T09:42:04Z
day: '03'
department:
- _id: MaMo
doi: 10.1109/ISIT50566.2022.9834850
external_id:
isi:
- '001254261900085'
intvolume: ' 2022'
isi: 1
language:
- iso: eng
month: '08'
oa_version: None
page: 504-509
publication: 2022 IEEE International Symposium on Information Theory
publication_identifier:
isbn:
- '9781665421591'
issn:
- 2157-8095
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: On the capacity of additive AVCs with feedback
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 2022
year: '2022'
...
---
_id: '12014'
abstract:
- lang: eng
text: We study the problem of high-dimensional multiple packing in Euclidean space.
Multiple packing is a natural generalization of sphere packing and is defined
as follows. Let N > 0 and L∈Z≥2. A multiple packing is a set C of points in Rn
such that any point in Rn lies in the intersection of at most L – 1 balls of radius
nN−−−√ around points in C. Given a well-known connection with coding theory, multiple
packings can be viewed as the Euclidean analog of list-decodable codes, which
are well-studied for finite fields. In this paper, we exactly pin down the asymptotic
density of (expurgated) Poisson Point Processes under a stronger notion called
average-radius multiple packing. To this end, we apply tools from high-dimensional
geometry and large deviation theory. This gives rise to the best known lower bound
on the largest multiple packing density. Our result corrects a mistake in a previous
paper by Blinovsky [Bli05].
article_processing_charge: No
author:
- first_name: Yihan
full_name: Zhang, Yihan
id: 2ce5da42-b2ea-11eb-bba5-9f264e9d002c
last_name: Zhang
orcid: 0000-0002-6465-6258
- first_name: Shashank
full_name: Vatedka, Shashank
last_name: Vatedka
citation:
ama: 'Zhang Y, Vatedka S. List-decodability of Poisson Point Processes. In: 2022
IEEE International Symposium on Information Theory. Vol 2022. IEEE; 2022:2559-2564.
doi:10.1109/ISIT50566.2022.9834512'
apa: 'Zhang, Y., & Vatedka, S. (2022). List-decodability of Poisson Point Processes.
In 2022 IEEE International Symposium on Information Theory (Vol. 2022,
pp. 2559–2564). Espoo, Finland: IEEE. https://doi.org/10.1109/ISIT50566.2022.9834512'
chicago: Zhang, Yihan, and Shashank Vatedka. “List-Decodability of Poisson Point
Processes.” In 2022 IEEE International Symposium on Information Theory,
2022:2559–64. IEEE, 2022. https://doi.org/10.1109/ISIT50566.2022.9834512.
ieee: Y. Zhang and S. Vatedka, “List-decodability of Poisson Point Processes,” in
2022 IEEE International Symposium on Information Theory, Espoo, Finland,
2022, vol. 2022, pp. 2559–2564.
ista: 'Zhang Y, Vatedka S. 2022. List-decodability of Poisson Point Processes. 2022
IEEE International Symposium on Information Theory. ISIT: International Symposium
on Information Theory vol. 2022, 2559–2564.'
mla: Zhang, Yihan, and Shashank Vatedka. “List-Decodability of Poisson Point Processes.”
2022 IEEE International Symposium on Information Theory, vol. 2022, IEEE,
2022, pp. 2559–64, doi:10.1109/ISIT50566.2022.9834512.
short: Y. Zhang, S. Vatedka, in:, 2022 IEEE International Symposium on Information
Theory, IEEE, 2022, pp. 2559–2564.
conference:
end_date: 2022-07-01
location: Espoo, Finland
name: 'ISIT: International Symposium on Information Theory'
start_date: 2022-06-26
date_created: 2022-09-04T22:02:04Z
date_published: 2022-08-03T00:00:00Z
date_updated: 2025-09-10T09:41:24Z
day: '03'
department:
- _id: MaMo
doi: 10.1109/ISIT50566.2022.9834512
external_id:
isi:
- '001254261902120'
intvolume: ' 2022'
isi: 1
language:
- iso: eng
month: '08'
oa_version: None
page: 2559-2564
publication: 2022 IEEE International Symposium on Information Theory
publication_identifier:
isbn:
- '9781665421591'
issn:
- 2157-8095
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: List-decodability of Poisson Point Processes
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 2022
year: '2022'
...
---
_id: '12015'
abstract:
- lang: eng
text: We study the problem of high-dimensional multiple packing in Euclidean space.
Multiple packing is a natural generalization of sphere packing and is defined
as follows. Let P, N > 0 and L∈Z≥2. A multiple packing is a set C of points in
Bn(0–,nP−−−√) such that any point in ℝ n lies in the intersection of at most L
– 1 balls of radius nN−−−√ around points in C. 1 In this paper, we derive two
lower bounds on the largest possible density of a multiple packing. These bounds
are obtained through a stronger notion called average-radius multiple packing.
Specifically, we exactly pin down the asymptotics of (expurgated) Gaussian codes
and (expurgated) spherical codes under average-radius multiple packing. To this
end, we apply tools from high-dimensional geometry and large deviation theory.
The bound for spherical codes matches the previous best known bound which was
obtained for the standard (weaker) notion of multiple packing through a curious
connection with error exponents [Bli99], [ZV21]. The bound for Gaussian codes
suggests that they are strictly inferior to spherical codes.
article_processing_charge: No
author:
- first_name: Yihan
full_name: Zhang, Yihan
id: 2ce5da42-b2ea-11eb-bba5-9f264e9d002c
last_name: Zhang
orcid: 0000-0002-6465-6258
- first_name: Shashank
full_name: Vatedka, Shashank
last_name: Vatedka
citation:
ama: 'Zhang Y, Vatedka S. Lower bounds for multiple packing. In: 2022 IEEE International
Symposium on Information Theory. Vol 2022. IEEE; 2022:3085-3090. doi:10.1109/ISIT50566.2022.9834443'
apa: 'Zhang, Y., & Vatedka, S. (2022). Lower bounds for multiple packing. In
2022 IEEE International Symposium on Information Theory (Vol. 2022, pp.
3085–3090). Espoo, Finland: IEEE. https://doi.org/10.1109/ISIT50566.2022.9834443'
chicago: Zhang, Yihan, and Shashank Vatedka. “Lower Bounds for Multiple Packing.”
In 2022 IEEE International Symposium on Information Theory, 2022:3085–90.
IEEE, 2022. https://doi.org/10.1109/ISIT50566.2022.9834443.
ieee: Y. Zhang and S. Vatedka, “Lower bounds for multiple packing,” in 2022 IEEE
International Symposium on Information Theory, Espoo, Finland, 2022, vol.
2022, pp. 3085–3090.
ista: 'Zhang Y, Vatedka S. 2022. Lower bounds for multiple packing. 2022 IEEE International
Symposium on Information Theory. ISIT: International Symposium on Information
Theory vol. 2022, 3085–3090.'
mla: Zhang, Yihan, and Shashank Vatedka. “Lower Bounds for Multiple Packing.” 2022
IEEE International Symposium on Information Theory, vol. 2022, IEEE, 2022,
pp. 3085–90, doi:10.1109/ISIT50566.2022.9834443.
short: Y. Zhang, S. Vatedka, in:, 2022 IEEE International Symposium on Information
Theory, IEEE, 2022, pp. 3085–3090.
conference:
end_date: 2022-07-01
location: Espoo, Finland
name: 'ISIT: International Symposium on Information Theory'
start_date: 2022-06-26
date_created: 2022-09-04T22:02:05Z
date_published: 2022-08-03T00:00:00Z
date_updated: 2025-09-10T09:44:24Z
day: '03'
department:
- _id: MaMo
doi: 10.1109/ISIT50566.2022.9834443
external_id:
isi:
- '001254261903042'
intvolume: ' 2022'
isi: 1
language:
- iso: eng
month: '08'
oa_version: None
page: 3085-3090
publication: 2022 IEEE International Symposium on Information Theory
publication_identifier:
isbn:
- '9781665421591'
issn:
- 2157-8095
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Lower bounds for multiple packing
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 2022
year: '2022'
...
---
_id: '12016'
abstract:
- lang: eng
text: We consider the problem of coded distributed computing using polar codes.
The average execution time of a coded computing system is related to the error
probability for transmission over the binary erasure channel in recent work by
Soleymani, Jamali and Mahdavifar, where the performance of binary linear codes
is investigated. In this paper, we focus on polar codes and unveil a connection
between the average execution time and the scaling exponent μ of the family of
codes. In the finite-length characterization of polar codes, the scaling exponent
is a key object capturing the speed of convergence to capacity. In particular,
we show that (i) the gap between the normalized average execution time of polar
codes and that of optimal MDS codes is O(n –1/μ ), and (ii) this upper bound can
be improved to roughly O(n –1/2 ) by considering polar codes with large kernels.
We conjecture that these bounds could be improved to O(n –2/μ ) and O(n –1 ),
respectively, and provide a heuristic argument as well as numerical evidence supporting
this view.
acknowledgement: D. Fathollahi and M. Mondelli were partially supported by the 2019
Lopez-Loreta Prize. The authors thank Hamed Hassani and Hessam Mahdavifar for helpful
discussions.
article_processing_charge: No
arxiv: 1
author:
- first_name: Dorsa
full_name: Fathollahi, Dorsa
last_name: Fathollahi
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
citation:
ama: 'Fathollahi D, Mondelli M. Polar coded computing: The role of the scaling exponent.
In: 2022 IEEE International Symposium on Information Theory. Vol 2022.
IEEE; 2022:2154-2159. doi:10.1109/ISIT50566.2022.9834712'
apa: 'Fathollahi, D., & Mondelli, M. (2022). Polar coded computing: The role
of the scaling exponent. In 2022 IEEE International Symposium on Information
Theory (Vol. 2022, pp. 2154–2159). Espoo, Finland: IEEE. https://doi.org/10.1109/ISIT50566.2022.9834712'
chicago: 'Fathollahi, Dorsa, and Marco Mondelli. “Polar Coded Computing: The Role
of the Scaling Exponent.” In 2022 IEEE International Symposium on Information
Theory, 2022:2154–59. IEEE, 2022. https://doi.org/10.1109/ISIT50566.2022.9834712.'
ieee: 'D. Fathollahi and M. Mondelli, “Polar coded computing: The role of the scaling
exponent,” in 2022 IEEE International Symposium on Information Theory,
Espoo, Finland, 2022, vol. 2022, pp. 2154–2159.'
ista: 'Fathollahi D, Mondelli M. 2022. Polar coded computing: The role of the scaling
exponent. 2022 IEEE International Symposium on Information Theory. ISIT: International
Symposium on Information Theory vol. 2022, 2154–2159.'
mla: 'Fathollahi, Dorsa, and Marco Mondelli. “Polar Coded Computing: The Role of
the Scaling Exponent.” 2022 IEEE International Symposium on Information Theory,
vol. 2022, IEEE, 2022, pp. 2154–59, doi:10.1109/ISIT50566.2022.9834712.'
short: D. Fathollahi, M. Mondelli, in:, 2022 IEEE International Symposium on Information
Theory, IEEE, 2022, pp. 2154–2159.
conference:
end_date: 2022-07-01
location: Espoo, Finland
name: 'ISIT: International Symposium on Information Theory'
start_date: 2022-06-26
date_created: 2022-09-04T22:02:05Z
date_published: 2022-08-03T00:00:00Z
date_updated: 2025-09-10T09:43:32Z
day: '03'
department:
- _id: MaMo
doi: 10.1109/ISIT50566.2022.9834712
external_id:
arxiv:
- '2201.10082'
isi:
- '001254261902052'
intvolume: ' 2022'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2201.10082
month: '08'
oa: 1
oa_version: Preprint
page: 2154-2159
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: 2022 IEEE International Symposium on Information Theory
publication_identifier:
isbn:
- '9781665421591'
issn:
- 2157-8095
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Polar coded computing: The role of the scaling exponent'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 2022
year: '2022'
...
---
_id: '12017'
abstract:
- lang: eng
text: 'In the classic adversarial communication problem, two parties communicate
over a noisy channel in the presence of a malicious jamming adversary. The arbitrarily
varying channels (AVCs) offer an elegant framework to study a wide range of interesting
adversary models. The optimal throughput or capacity over such AVCs is intimately
tied to the underlying adversary model; in some cases, capacity is unknown and
the problem is known to be notoriously hard. The omniscient adversary, one which
knows the sender’s entire channel transmission a priori, is one of such classic
models of interest; the capacity under such an adversary remains an exciting open
problem. The myopic adversary is a generalization of that model where the adversary’s
observation may be corrupted over a noisy discrete memoryless channel. Through
the adversary’s myopicity, one can unify the slew of different adversary models,
ranging from the omniscient adversary to one that is completely blind to the transmission
(the latter is the well known oblivious model where the capacity is fully characterized).In
this work, we present new results on the capacity under both the omniscient and
myopic adversary models. We completely characterize the positive capacity threshold
over general AVCs with omniscient adversaries. The characterization is in terms
of two key combinatorial objects: the set of completely positive distributions
and the CP-confusability set. For omniscient AVCs with positive capacity, we present
non-trivial lower and upper bounds on the capacity; unlike some of the previous
bounds, our bounds hold under fairly general input and jamming constraints. Our
lower bound improves upon the generalized Gilbert-Varshamov bound for general
AVCs while the upper bound generalizes the well known Elias-Bassalygo bound (known
for binary and q-ary alphabets). For the myopic AVCs, we build on prior results
known for the so-called sufficiently myopic model, and present new results on
the positive rate communication threshold over the so-called insufficiently myopic
regime (a completely insufficient myopic adversary specializes to an omniscient
adversary). We present interesting examples for the widely studied models of adversarial
bit-flip and bit-erasure channels. In fact, for the bit-flip AVC with additive
adversarial noise as well as random noise, we completely characterize the omniscient
model capacity when the random noise is sufficiently large vis-a-vis the adversary’s
budget.'
article_processing_charge: No
author:
- first_name: Anuj Kumar
full_name: Yadav, Anuj Kumar
last_name: Yadav
- first_name: Mohammadreza
full_name: Alimohammadi, Mohammadreza
last_name: Alimohammadi
- first_name: Yihan
full_name: Zhang, Yihan
id: 2ce5da42-b2ea-11eb-bba5-9f264e9d002c
last_name: Zhang
orcid: 0000-0002-6465-6258
- first_name: Amitalok J.
full_name: Budkuley, Amitalok J.
last_name: Budkuley
- first_name: Sidharth
full_name: Jaggi, Sidharth
last_name: Jaggi
citation:
ama: 'Yadav AK, Alimohammadi M, Zhang Y, Budkuley AJ, Jaggi S. New results on AVCs
with omniscient and myopic adversaries. In: 2022 IEEE International Symposium
on Information Theory. Vol 2022. Institute of Electrical and Electronics Engineers;
2022:2535-2540. doi:10.1109/ISIT50566.2022.9834632'
apa: 'Yadav, A. K., Alimohammadi, M., Zhang, Y., Budkuley, A. J., & Jaggi, S.
(2022). New results on AVCs with omniscient and myopic adversaries. In 2022
IEEE International Symposium on Information Theory (Vol. 2022, pp. 2535–2540).
Espoo, Finland: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISIT50566.2022.9834632'
chicago: Yadav, Anuj Kumar, Mohammadreza Alimohammadi, Yihan Zhang, Amitalok J.
Budkuley, and Sidharth Jaggi. “New Results on AVCs with Omniscient and Myopic
Adversaries.” In 2022 IEEE International Symposium on Information Theory,
2022:2535–40. Institute of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/ISIT50566.2022.9834632.
ieee: A. K. Yadav, M. Alimohammadi, Y. Zhang, A. J. Budkuley, and S. Jaggi, “New
results on AVCs with omniscient and myopic adversaries,” in 2022 IEEE International
Symposium on Information Theory, Espoo, Finland, 2022, vol. 2022, pp. 2535–2540.
ista: 'Yadav AK, Alimohammadi M, Zhang Y, Budkuley AJ, Jaggi S. 2022. New results
on AVCs with omniscient and myopic adversaries. 2022 IEEE International Symposium
on Information Theory. ISIT: Internation Symposium on Information Theory vol.
2022, 2535–2540.'
mla: Yadav, Anuj Kumar, et al. “New Results on AVCs with Omniscient and Myopic Adversaries.”
2022 IEEE International Symposium on Information Theory, vol. 2022, Institute
of Electrical and Electronics Engineers, 2022, pp. 2535–40, doi:10.1109/ISIT50566.2022.9834632.
short: A.K. Yadav, M. Alimohammadi, Y. Zhang, A.J. Budkuley, S. Jaggi, in:, 2022
IEEE International Symposium on Information Theory, Institute of Electrical and
Electronics Engineers, 2022, pp. 2535–2540.
conference:
end_date: 2022-07-01
location: Espoo, Finland
name: 'ISIT: Internation Symposium on Information Theory'
start_date: 2022-06-26
date_created: 2022-09-04T22:02:06Z
date_published: 2022-08-03T00:00:00Z
date_updated: 2025-09-10T09:45:03Z
day: '03'
department:
- _id: MaMo
doi: 10.1109/ISIT50566.2022.9834632
external_id:
isi:
- '001254261902116'
intvolume: ' 2022'
isi: 1
language:
- iso: eng
month: '08'
oa_version: None
page: 2535-2540
publication: 2022 IEEE International Symposium on Information Theory
publication_identifier:
isbn:
- '9781665421591'
issn:
- 2157-8095
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: New results on AVCs with omniscient and myopic adversaries
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 2022
year: '2022'
...
---
_id: '12018'
abstract:
- lang: eng
text: We study the problem of characterizing the maximal rates of list decoding
in Euclidean spaces for finite list sizes. For any positive integer L ≥ 2 and
real N > 0, we say that a subset C⊂Rn is an (N,L – 1)-multiple packing or an (N,L–
1)-list decodable code if every Euclidean ball of radius nN−−−√ in ℝ n contains
no more than L − 1 points of C. We study this problem with and without ℓ 2 norm
constraints on C, and derive the best-known lower bounds on the maximal rate for
(N,L−1) multiple packing. Our bounds are obtained via error exponents for list
decoding over Additive White Gaussian Noise (AWGN) channels. We establish a curious
inequality which relates the error exponent, a quantity of average-case nature,
to the list-decoding radius, a quantity of worst-case nature. We derive various
bounds on the error exponent for list decoding in both bounded and unbounded settings
which could be of independent interest beyond multiple packing.
article_processing_charge: No
author:
- first_name: Yihan
full_name: Zhang, Yihan
id: 2ce5da42-b2ea-11eb-bba5-9f264e9d002c
last_name: Zhang
orcid: 0000-0002-6465-6258
- first_name: Shashank
full_name: Vatedka, Shashank
last_name: Vatedka
citation:
ama: 'Zhang Y, Vatedka S. Lower bounds on list decoding capacity using error exponents.
In: 2022 IEEE International Symposium on Information Theory. Vol 2022.
Institute of Electrical and Electronics Engineers; 2022:1324-1329. doi:10.1109/ISIT50566.2022.9834815'
apa: 'Zhang, Y., & Vatedka, S. (2022). Lower bounds on list decoding capacity
using error exponents. In 2022 IEEE International Symposium on Information
Theory (Vol. 2022, pp. 1324–1329). Espoo, Finland: Institute of Electrical
and Electronics Engineers. https://doi.org/10.1109/ISIT50566.2022.9834815'
chicago: Zhang, Yihan, and Shashank Vatedka. “Lower Bounds on List Decoding Capacity
Using Error Exponents.” In 2022 IEEE International Symposium on Information
Theory, 2022:1324–29. Institute of Electrical and Electronics Engineers, 2022.
https://doi.org/10.1109/ISIT50566.2022.9834815.
ieee: Y. Zhang and S. Vatedka, “Lower bounds on list decoding capacity using error
exponents,” in 2022 IEEE International Symposium on Information Theory,
Espoo, Finland, 2022, vol. 2022, pp. 1324–1329.
ista: 'Zhang Y, Vatedka S. 2022. Lower bounds on list decoding capacity using error
exponents. 2022 IEEE International Symposium on Information Theory. ISIT: International
Symposium on Information Theory vol. 2022, 1324–1329.'
mla: Zhang, Yihan, and Shashank Vatedka. “Lower Bounds on List Decoding Capacity
Using Error Exponents.” 2022 IEEE International Symposium on Information Theory,
vol. 2022, Institute of Electrical and Electronics Engineers, 2022, pp. 1324–29,
doi:10.1109/ISIT50566.2022.9834815.
short: Y. Zhang, S. Vatedka, in:, 2022 IEEE International Symposium on Information
Theory, Institute of Electrical and Electronics Engineers, 2022, pp. 1324–1329.
conference:
end_date: 2022-07-01
location: Espoo, Finland
name: 'ISIT: International Symposium on Information Theory'
start_date: 2022-06-26
date_created: 2022-09-04T22:02:06Z
date_published: 2022-08-03T00:00:00Z
date_updated: 2025-09-10T09:45:40Z
day: '03'
department:
- _id: MaMo
doi: 10.1109/ISIT50566.2022.9834815
external_id:
isi:
- '001254261901080'
intvolume: ' 2022'
isi: 1
language:
- iso: eng
month: '08'
oa_version: None
page: 1324-1329
publication: 2022 IEEE International Symposium on Information Theory
publication_identifier:
isbn:
- '9781665421591'
issn:
- 2157-8095
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Lower bounds on list decoding capacity using error exponents
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 2022
year: '2022'
...
---
_id: '12019'
abstract:
- lang: eng
text: This paper studies combinatorial properties of codes for the Z-channel. A
Z-channel with error fraction τ takes as input a length-n binary codeword and
injects in an adversarial manner up to nτ asymmetric errors, i.e., errors that
only zero out bits but do not flip 0’s to 1’s. It is known that the largest (L
− 1)-list-decodable code for the Z-channel with error fraction τ has exponential
(in n) size if τ is less than a critical value that we call the Plotkin point
and has constant size if τ is larger than the threshold. The (L−1)-list-decoding
Plotkin point is known to be L−1L−1−L−LL−1. In this paper, we show that the largest
(L−1)-list-decodable code ε-above the Plotkin point has size Θ L (ε −3/2 ) for
any L − 1 ≥ 1.
article_processing_charge: No
author:
- first_name: Nikita
full_name: Polyanskii, Nikita
last_name: Polyanskii
- first_name: Yihan
full_name: Zhang, Yihan
id: 2ce5da42-b2ea-11eb-bba5-9f264e9d002c
last_name: Zhang
orcid: 0000-0002-6465-6258
citation:
ama: 'Polyanskii N, Zhang Y. List-decodable zero-rate codes for the Z-channel. In:
2022 IEEE International Symposium on Information Theory. Vol 2022. Institute
of Electrical and Electronics Engineers; 2022:2553-2558. doi:10.1109/ISIT50566.2022.9834829'
apa: 'Polyanskii, N., & Zhang, Y. (2022). List-decodable zero-rate codes for
the Z-channel. In 2022 IEEE International Symposium on Information Theory
(Vol. 2022, pp. 2553–2558). Espoo, Finland: Institute of Electrical and Electronics
Engineers. https://doi.org/10.1109/ISIT50566.2022.9834829'
chicago: Polyanskii, Nikita, and Yihan Zhang. “List-Decodable Zero-Rate Codes for
the Z-Channel.” In 2022 IEEE International Symposium on Information Theory,
2022:2553–58. Institute of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/ISIT50566.2022.9834829.
ieee: N. Polyanskii and Y. Zhang, “List-decodable zero-rate codes for the Z-channel,”
in 2022 IEEE International Symposium on Information Theory, Espoo, Finland,
2022, vol. 2022, pp. 2553–2558.
ista: 'Polyanskii N, Zhang Y. 2022. List-decodable zero-rate codes for the Z-channel.
2022 IEEE International Symposium on Information Theory. ISIT: International Symposium
on Information Theory vol. 2022, 2553–2558.'
mla: Polyanskii, Nikita, and Yihan Zhang. “List-Decodable Zero-Rate Codes for the
Z-Channel.” 2022 IEEE International Symposium on Information Theory, vol.
2022, Institute of Electrical and Electronics Engineers, 2022, pp. 2553–58, doi:10.1109/ISIT50566.2022.9834829.
short: N. Polyanskii, Y. Zhang, in:, 2022 IEEE International Symposium on Information
Theory, Institute of Electrical and Electronics Engineers, 2022, pp. 2553–2558.
conference:
end_date: 2022-07-01
location: Espoo, Finland
name: 'ISIT: International Symposium on Information Theory'
start_date: 2022-06-26
date_created: 2022-09-04T22:02:07Z
date_published: 2022-08-03T00:00:00Z
date_updated: 2025-09-10T09:46:15Z
day: '03'
department:
- _id: MaMo
doi: 10.1109/ISIT50566.2022.9834829
external_id:
isi:
- '001254261902119'
intvolume: ' 2022'
isi: 1
language:
- iso: eng
month: '08'
oa_version: None
page: 2553-2558
publication: 2022 IEEE International Symposium on Information Theory
publication_identifier:
isbn:
- '9781665421591'
issn:
- 2157-8095
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: List-decodable zero-rate codes for the Z-channel
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 2022
year: '2022'
...
---
_id: '12233'
abstract:
- lang: eng
text: A novel recursive list decoding (RLD) algorithm for Reed-Muller (RM) codes
based on successive permutations (SP) of the codeword is presented. A low-complexity
SP scheme applied to a subset of the symmetry group of RM codes is first proposed
to carefully select a good codeword permutation on the fly. Then, the proposed
SP technique is integrated into an improved RLD algorithm that initializes different
decoding paths with random codeword permutations, which are sampled from the full
symmetry group of RM codes. Finally, efficient latency and complexity reduction
schemes are introduced that virtually preserve the error-correction performance
of the proposed decoder. Simulation results demonstrate that at the target frame
error rate of 10−3 for the RM code of length 256 with 163 information bits, the
proposed decoder reduces 6% of the computational complexity and 22% of the decoding
latency of the state-of-the-art semi-parallel simplified successive-cancellation
decoder with fast Hadamard transform (SSC-FHT) that uses 96 permutations from
the full symmetry group of RM codes, while relatively maintaining the error-correction
performance and memory consumption of the semi-parallel permuted SSC-FHT decoder.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Nghia
full_name: Doan, Nghia
last_name: Doan
- first_name: Seyyed Ali
full_name: Hashemi, Seyyed Ali
last_name: Hashemi
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
- first_name: Warren J.
full_name: Gross, Warren J.
last_name: Gross
citation:
ama: Doan N, Hashemi SA, Mondelli M, Gross WJ. Decoding Reed-Muller codes with successive
codeword permutations. IEEE Transactions on Communications. 2022;70(11):7134-7145.
doi:10.1109/tcomm.2022.3211101
apa: Doan, N., Hashemi, S. A., Mondelli, M., & Gross, W. J. (2022). Decoding
Reed-Muller codes with successive codeword permutations. IEEE Transactions
on Communications. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/tcomm.2022.3211101
chicago: Doan, Nghia, Seyyed Ali Hashemi, Marco Mondelli, and Warren J. Gross. “Decoding
Reed-Muller Codes with Successive Codeword Permutations.” IEEE Transactions
on Communications. Institute of Electrical and Electronics Engineers, 2022.
https://doi.org/10.1109/tcomm.2022.3211101.
ieee: N. Doan, S. A. Hashemi, M. Mondelli, and W. J. Gross, “Decoding Reed-Muller
codes with successive codeword permutations,” IEEE Transactions on Communications,
vol. 70, no. 11. Institute of Electrical and Electronics Engineers, pp. 7134–7145,
2022.
ista: Doan N, Hashemi SA, Mondelli M, Gross WJ. 2022. Decoding Reed-Muller codes
with successive codeword permutations. IEEE Transactions on Communications. 70(11),
7134–7145.
mla: Doan, Nghia, et al. “Decoding Reed-Muller Codes with Successive Codeword Permutations.”
IEEE Transactions on Communications, vol. 70, no. 11, Institute of Electrical
and Electronics Engineers, 2022, pp. 7134–45, doi:10.1109/tcomm.2022.3211101.
short: N. Doan, S.A. Hashemi, M. Mondelli, W.J. Gross, IEEE Transactions on Communications
70 (2022) 7134–7145.
date_created: 2023-01-16T09:50:38Z
date_published: 2022-11-01T00:00:00Z
date_updated: 2023-08-04T09:34:43Z
day: '01'
department:
- _id: MaMo
doi: 10.1109/tcomm.2022.3211101
external_id:
arxiv:
- '2109.02122'
isi:
- '000937284600006'
intvolume: ' 70'
isi: 1
issue: '11'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2109.02122'
month: '11'
oa: 1
oa_version: Preprint
page: 7134-7145
publication: IEEE Transactions on Communications
publication_identifier:
eissn:
- 1558-0857
issn:
- 0090-6778
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Decoding Reed-Muller codes with successive codeword permutations
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 70
year: '2022'
...
---
_id: '12273'
abstract:
- lang: eng
text: We study communication in the presence of a jamming adversary where quadratic
power constraints are imposed on the transmitter and the jammer. The jamming signal
is allowed to be a function of the codebook, and a noncausal but noisy observation
of the transmitted codeword. For a certain range of the noise-to-signal ratios
(NSRs) of the transmitter and the jammer, we are able to characterize the capacity
of this channel under deterministic encoding or stochastic encoding, i.e., with
no common randomness between the encoder/decoder pair. For the remaining NSR regimes,
we determine the capacity under the assumption of a small amount of common randomness
(at most 2log(n) bits in one sub-regime, and at most Ω(n) bits in the other sub-regime)
available to the encoder-decoder pair. Our proof techniques involve a novel myopic
list-decoding result for achievability, and a Plotkin-type push attack for the
converse in a subregion of the NSRs, both of which may be of independent interest.
We also give bounds on the strong secrecy capacity of this channel assuming that
the jammer is simultaneously eavesdropping.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Yihan
full_name: Zhang, Yihan
id: 2ce5da42-b2ea-11eb-bba5-9f264e9d002c
last_name: Zhang
orcid: 0000-0002-6465-6258
- first_name: Shashank
full_name: Vatedka, Shashank
last_name: Vatedka
- first_name: Sidharth
full_name: Jaggi, Sidharth
last_name: Jaggi
- first_name: Anand D.
full_name: Sarwate, Anand D.
last_name: Sarwate
citation:
ama: Zhang Y, Vatedka S, Jaggi S, Sarwate AD. Quadratically constrained myopic adversarial
channels. IEEE Transactions on Information Theory. 2022;68(8):4901-4948.
doi:10.1109/tit.2022.3167554
apa: Zhang, Y., Vatedka, S., Jaggi, S., & Sarwate, A. D. (2022). Quadratically
constrained myopic adversarial channels. IEEE Transactions on Information Theory.
Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/tit.2022.3167554
chicago: Zhang, Yihan, Shashank Vatedka, Sidharth Jaggi, and Anand D. Sarwate. “Quadratically
Constrained Myopic Adversarial Channels.” IEEE Transactions on Information
Theory. Institute of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/tit.2022.3167554.
ieee: Y. Zhang, S. Vatedka, S. Jaggi, and A. D. Sarwate, “Quadratically constrained
myopic adversarial channels,” IEEE Transactions on Information Theory,
vol. 68, no. 8. Institute of Electrical and Electronics Engineers, pp. 4901–4948,
2022.
ista: Zhang Y, Vatedka S, Jaggi S, Sarwate AD. 2022. Quadratically constrained myopic
adversarial channels. IEEE Transactions on Information Theory. 68(8), 4901–4948.
mla: Zhang, Yihan, et al. “Quadratically Constrained Myopic Adversarial Channels.”
IEEE Transactions on Information Theory, vol. 68, no. 8, Institute of Electrical
and Electronics Engineers, 2022, pp. 4901–48, doi:10.1109/tit.2022.3167554.
short: Y. Zhang, S. Vatedka, S. Jaggi, A.D. Sarwate, IEEE Transactions on Information
Theory 68 (2022) 4901–4948.
corr_author: '1'
date_created: 2023-01-16T10:01:19Z
date_published: 2022-08-01T00:00:00Z
date_updated: 2024-10-09T21:03:54Z
day: '01'
department:
- _id: MaMo
doi: 10.1109/tit.2022.3167554
external_id:
arxiv:
- '1801.05951'
isi:
- '000838527100004'
intvolume: ' 68'
isi: 1
issue: '8'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1801.05951
month: '08'
oa: 1
oa_version: Preprint
page: 4901-4948
publication: IEEE Transactions on Information Theory
publication_identifier:
eissn:
- 1557-9654
issn:
- 0018-9448
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quadratically constrained myopic adversarial channels
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 68
year: '2022'
...
---
_id: '12480'
abstract:
- lang: eng
text: 'We consider the problem of estimating a signal from measurements obtained
via a generalized linear model. We focus on estimators based on approximate message
passing (AMP), a family of iterative algorithms with many appealing features:
the performance of AMP in the high-dimensional limit can be succinctly characterized
under suitable model assumptions; AMP can also be tailored to the empirical distribution
of the signal entries, and for a wide class of estimation problems, AMP is conjectured
to be optimal among all polynomial-time algorithms. However, a major issue of
AMP is that in many models (such as phase retrieval), it requires an initialization
correlated with the ground-truth signal and independent from the measurement matrix.
Assuming that such an initialization is available is typically not realistic.
In this paper, we solve this problem by proposing an AMP algorithm initialized
with a spectral estimator. With such an initialization, the standard AMP analysis
fails since the spectral estimator depends in a complicated way on the design
matrix. Our main contribution is a rigorous characterization of the performance
of AMP with spectral initialization in the high-dimensional limit. The key technical
idea is to define and analyze a two-phase artificial AMP algorithm that first
produces the spectral estimator, and then closely approximates the iterates of
the true AMP. We also provide numerical results that demonstrate the validity
of the proposed approach.'
acknowledgement: "The authors would like to thank Andrea Montanari for helpful discussions.\r\nM
Mondelli was partially supported by the 2019 Lopez-Loreta Prize. R Venkataramanan
was partially supported by the Alan Turing Institute under the EPSRC Grant\r\nEP/N510129/1."
article_number: '114003'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
- first_name: Ramji
full_name: Venkataramanan, Ramji
last_name: Venkataramanan
citation:
ama: 'Mondelli M, Venkataramanan R. Approximate message passing with spectral initialization
for generalized linear models. Journal of Statistical Mechanics: Theory and
Experiment. 2022;2022(11). doi:10.1088/1742-5468/ac9828'
apa: 'Mondelli, M., & Venkataramanan, R. (2022). Approximate message passing
with spectral initialization for generalized linear models. Journal of Statistical
Mechanics: Theory and Experiment. IOP Publishing. https://doi.org/10.1088/1742-5468/ac9828'
chicago: 'Mondelli, Marco, and Ramji Venkataramanan. “Approximate Message Passing
with Spectral Initialization for Generalized Linear Models.” Journal of Statistical
Mechanics: Theory and Experiment. IOP Publishing, 2022. https://doi.org/10.1088/1742-5468/ac9828.'
ieee: 'M. Mondelli and R. Venkataramanan, “Approximate message passing with spectral
initialization for generalized linear models,” Journal of Statistical Mechanics:
Theory and Experiment, vol. 2022, no. 11. IOP Publishing, 2022.'
ista: 'Mondelli M, Venkataramanan R. 2022. Approximate message passing with spectral
initialization for generalized linear models. Journal of Statistical Mechanics:
Theory and Experiment. 2022(11), 114003.'
mla: 'Mondelli, Marco, and Ramji Venkataramanan. “Approximate Message Passing with
Spectral Initialization for Generalized Linear Models.” Journal of Statistical
Mechanics: Theory and Experiment, vol. 2022, no. 11, 114003, IOP Publishing,
2022, doi:10.1088/1742-5468/ac9828.'
short: 'M. Mondelli, R. Venkataramanan, Journal of Statistical Mechanics: Theory
and Experiment 2022 (2022).'
corr_author: '1'
date_created: 2023-02-02T08:31:57Z
date_published: 2022-11-24T00:00:00Z
date_updated: 2025-04-15T07:50:16Z
day: '24'
ddc:
- '510'
- '530'
department:
- _id: MaMo
doi: 10.1088/1742-5468/ac9828
external_id:
isi:
- '000889589900001'
file:
- access_level: open_access
checksum: 01411ffa76d3e380a0446baeb89b1ef7
content_type: application/pdf
creator: dernst
date_created: 2023-02-02T08:35:52Z
date_updated: 2023-02-02T08:35:52Z
file_id: '12481'
file_name: 2022_JourStatisticalMechanics_Mondelli.pdf
file_size: 1729997
relation: main_file
success: 1
file_date_updated: 2023-02-02T08:35:52Z
has_accepted_license: '1'
intvolume: ' 2022'
isi: 1
issue: '11'
keyword:
- Statistics
- Probability and Uncertainty
- Statistics and Probability
- Statistical and Nonlinear Physics
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: 'Journal of Statistical Mechanics: Theory and Experiment'
publication_identifier:
issn:
- 1742-5468
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
related_material:
record:
- id: '10598'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: Approximate message passing with spectral initialization for generalized linear
models
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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 2022
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.'
acknowledgement: "M. Mondelli was partially supported by the 2019 Lopez-Loreta Prize.
The authors acknowledge\r\ndiscussions with A. Krajenbrink, M. Robinson, A. Depope,
N. Macris and F. Pourkamali.\r\n"
alternative_title:
- NeurIPS
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? In: 36th Annual
Conference on Neural Information Processing Systems. Vol 35. ; 2022.'
apa: 'Barbier, J., Hou, T., Mondelli, M., & Saenz, M. (2022). The price of ignorance:
How much does it cost to forget noise structure in low-rank matrix estimation?
In 36th Annual Conference on Neural Information Processing Systems (Vol.
35). New Orleans, LA, United States.'
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?” In 36th Annual Conference on Neural Information Processing Systems,
Vol. 35, 2022.'
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?,” in
36th Annual Conference on Neural Information Processing Systems, New Orleans,
LA, United States, 2022, vol. 35.'
ista: 'Barbier J, Hou T, Mondelli M, Saenz M. 2022. The price of ignorance: How
much does it cost to forget noise structure in low-rank matrix estimation? 36th
Annual Conference on Neural Information Processing Systems. NeurIPS: Neural Information
Processing Systems, NeurIPS, vol. 35.'
mla: 'Barbier, Jean, et al. “The Price of Ignorance: How Much Does It Cost to Forget
Noise Structure in Low-Rank Matrix Estimation?” 36th Annual Conference on Neural
Information Processing Systems, vol. 35, 2022.'
short: J. Barbier, T. Hou, M. Mondelli, M. Saenz, in:, 36th Annual Conference on
Neural Information Processing Systems, 2022.
conference:
end_date: 2022-12-09
location: New Orleans, LA, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2022-11-28
corr_author: '1'
date_created: 2023-02-10T13:45:41Z
date_published: 2022-11-20T00:00:00Z
date_updated: 2024-10-09T21:04:25Z
day: '20'
department:
- _id: MaMo
external_id:
arxiv:
- '2205.10009'
intvolume: ' 35'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2205.10009
month: '11'
oa: 1
oa_version: Preprint
publication: 36th Annual Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713871088'
publication_status: published
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'The price of ignorance: How much does it cost to forget noise structure in
low-rank matrix estimation?'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2022'
...
---
OA_place: repository
OA_type: green
_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"
alternative_title:
- Advances in Neural Information Processing Systems
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. Neural Information Processing Systems
Foundation; 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). New Orleans,
LA, United States: Neural Information Processing Systems Foundation.'
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.
Neural Information Processing Systems Foundation, 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, New Orleans, LA, United States,
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. NeurIPS: Neural Information Processing Systems,
Advances in 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, Neural Information Processing Systems Foundation,
2022, pp. 7628–40.
short: S. Bombari, M.H. Amani, M. Mondelli, in:, 36th Conference on Neural Information
Processing Systems, Neural Information Processing Systems Foundation, 2022, pp.
7628–7640.
conference:
end_date: 2022-12-09
location: New Orleans, LA, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2022-11-28
corr_author: '1'
date_created: 2023-02-10T13:46:37Z
date_published: 2022-07-24T00:00:00Z
date_updated: 2025-05-14T11:28:22Z
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:
eissn:
- 1049-5258
isbn:
- '9781713871088'
publication_status: published
publisher: Neural Information Processing Systems Foundation
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: 2025-09-10T09:53:31Z
day: '16'
department:
- _id: MaMo
doi: 10.1109/ITW54588.2022.9965870
external_id:
arxiv:
- '2205.08199'
isi:
- '000904341100099'
isi: 1
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2022'
...