default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDPatrick Rebeschini
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
[c18]Jung Eun Huh, Patrick Rebeschini:
Generalization Bounds for Label Noise Stochastic Gradient Descent. AISTATS 2024: 1360-1368- [c17]Emmeran Johnson, Ciara Pike-Burke, Patrick Rebeschini:
Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity. ICLR 2024 - [i23]Carlo Alfano, Sebastian Towers, Silvia Sapora, Chris Lu, Patrick Rebeschini:
Meta-learning the mirror map in policy mirror descent. CoRR abs/2402.05187 (2024) - [i22]Samuel Howard, George Deligiannidis, Patrick Rebeschini, James Thornton:
Differentiable Cost-Parameterized Monge Map Estimators. CoRR abs/2406.08399 (2024) - 2023
- [c16]Carlo Alfano, Rui Yuan, Patrick Rebeschini:
A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence. NeurIPS 2023 - [c15]Emmeran Johnson, Ciara Pike-Burke, Patrick Rebeschini:
Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes. NeurIPS 2023 - [i21]Carlo Alfano, Rui Yuan, Patrick Rebeschini:
A Novel Framework for Policy Mirror Descent with General Parametrization and Linear Convergence. CoRR abs/2301.13139 (2023) - [i20]Emmeran Johnson, Ciara Pike-Burke, Patrick Rebeschini:
Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes. CoRR abs/2302.11381 (2023) - [i19]Emmeran Johnson, Ciara Pike-Burke, Patrick Rebeschini:
Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity. CoRR abs/2310.01616 (2023) - [i18]Jung Eun Huh, Patrick Rebeschini:
Generalization Bounds for Label Noise Stochastic Gradient Descent. CoRR abs/2311.00274 (2023) - 2022
- [i17]Varun Kanade, Patrick Rebeschini, Tomas Vaskevicius:
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition. CoRR abs/2202.11461 (2022) - [i16]Carlo Alfano, Patrick Rebeschini:
Linear Convergence for Natural Policy Gradient with Log-linear Policy Parametrization. CoRR abs/2209.15382 (2022) - 2021
- [c14]Fan Wu
, Patrick Rebeschini:
Hadamard Wirtinger Flow for Sparse Phase Retrieval. AISTATS 2021: 982-990 - [c13]Dominic Richards, Sahand Negahban, Patrick Rebeschini:
Distributed Machine Learning with Sparse Heterogeneous Data. NeurIPS 2021: 18008-18020 - [c12]Tyler Farghly, Patrick Rebeschini:
Time-independent Generalization Bounds for SGLD in Non-convex Settings. NeurIPS 2021: 19836-19846 - [c11]Fan Wu, Patrick Rebeschini:
Implicit Regularization in Matrix Sensing via Mirror Descent. NeurIPS 2021: 20558-20570 - [c10]Eduard Oravkin, Patrick Rebeschini:
On Optimal Interpolation in Linear Regression. NeurIPS 2021: 29116-29128 - [i15]Fan Wu, Patrick Rebeschini:
Nearly Minimax-Optimal Rates for Noisy Sparse Phase Retrieval via Early-Stopped Mirror Descent. CoRR abs/2105.03678 (2021) - [i14]Fan Wu, Patrick Rebeschini:
Implicit Regularization in Matrix Sensing via Mirror Descent. CoRR abs/2105.13831 (2021) - [i13]Dominic Richards, Edgar Dobriban, Patrick Rebeschini:
Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models? CoRR abs/2108.11872 (2021) - [i12]Carlo Alfano, Patrick Rebeschini:
Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning. CoRR abs/2109.11692 (2021) - [i11]Eduard Oravkin, Patrick Rebeschini:
On Optimal Interpolation In Linear Regression. CoRR abs/2110.11258 (2021) - [i10]Tyler Farghly, Patrick Rebeschini:
Time-independent Generalization Bounds for SGLD in Non-convex Settings. CoRR abs/2111.12876 (2021) - 2020
- [j3]Dominic Richards, Patrick Rebeschini:
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent. J. Mach. Learn. Res. 21: 34:1-34:44 (2020) - [c9]Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco:
Decentralised Learning with Random Features and Distributed Gradient Descent. ICML 2020: 8105-8115 - [c8]Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini:
The Statistical Complexity of Early-Stopped Mirror Descent. NeurIPS 2020 - [c7]Fan Wu, Patrick Rebeschini:
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval. NeurIPS 2020 - [i9]Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini:
The Statistical Complexity of Early Stopped Mirror Descent. CoRR abs/2002.00189 (2020) - [i8]Fan Wu, Patrick Rebeschini:
Hadamard Wirtinger Flow for Sparse Phase Retrieval. CoRR abs/2006.01065 (2020) - [i7]Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco:
Decentralised Learning with Random Features and Distributed Gradient Descent. CoRR abs/2007.00360 (2020) - [i6]Fan Wu, Patrick Rebeschini:
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval. CoRR abs/2010.10168 (2020)
2010 – 2019
- 2019
- [j2]Patrick Rebeschini, Sekhar Tatikonda:
A New Approach to Laplacian Solvers and Flow Problems. J. Mach. Learn. Res. 20: 36:1-36:37 (2019) - [j1]Patrick Rebeschini, Sekhar Tatikonda:
Locality in Network Optimization. IEEE Trans. Control. Netw. Syst. 6(2): 487-500 (2019) - [c6]Dominic Richards, Patrick Rebeschini:
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up. NeurIPS 2019: 1214-1225 - [c5]Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini:
Implicit Regularization for Optimal Sparse Recovery. NeurIPS 2019: 2968-2979 - [c4]David Martínez-Rubio, Varun Kanade, Patrick Rebeschini:
Decentralized Cooperative Stochastic Bandits. NeurIPS 2019: 4531-4542 - [i5]Dominic Richards, Patrick Rebeschini:
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up. CoRR abs/1905.03135 (2019) - [i4]Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini:
Implicit Regularization for Optimal Sparse Recovery. CoRR abs/1909.05122 (2019) - 2018
- [i3]Dominic Richards, Patrick Rebeschini:
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent. CoRR abs/1809.06958 (2018) - [i2]David Martínez-Rubio, Varun Kanade, Patrick Rebeschini:
Decentralized Cooperative Stochastic Multi-armed Bandits. CoRR abs/1810.04468 (2018) - 2017
- [c3]Patrick Rebeschini, Sekhar Tatikonda:
Accelerated consensus via Min-Sum Splitting. NIPS 2017: 1374-1384 - 2016
- [c2]Patrick Rebeschini, Sekhar Tatikonda:
Decay of correlation in network flow problems. CISS 2016: 169-174 - [i1]Patrick Rebeschini, Sekhar Tatikonda:
A new approach to Laplacian solvers and flow problems. CoRR abs/1611.07138 (2016) - 2015
- [c1]Patrick Rebeschini, Amin Karbasi:
Fast Mixing for Discrete Point Processes. COLT 2015: 1480-1500 - 2014
- [b1]Patrick Rebeschini:
Nonlinear Filtering in High Dimension. Princeton University, USA, 2014
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-09-13 00:41 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
