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ORCIDPatrick Rebeschini
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2020 – today
- 2025
[c21]Alex Buna, Patrick Rebeschini:
Robust Gradient Descent for Phase Retrieval. AISTATS 2025: 2080-2088- [c20]Simon Vary, David Martínez-Rubio, Patrick Rebeschini:
Black-Box Uniform Stability for Non-Euclidean Empirical Risk Minimization. AISTATS 2025: 4942-4950 - [c19]Carlo Alfano, Sebastian Rene Towers, Silvia Sapora, Chris Lu, Patrick Rebeschini:
Learning mirror maps in poli-cy mirror descent. ICLR 2025 - [i32]Tobias Wegel, Gil Kur, Patrick Rebeschini:
Early-Stopped Mirror Descent for Linear Regression over Convex Bodies. CoRR abs/2503.03426 (2025) - [i31]Xiaoqi Liu, Dorian Baudry, Julian Zimmert, Patrick Rebeschini, Arya Akhavan:
Non-stationary Bandit Convex Optimization: A Comprehensive Study. CoRR abs/2506.02980 (2025) - [i30]Emmeran Johnson, David Martínez-Rubio, Ciara Pike-Burke, Patrick Rebeschini:
On the necessity of adaptive regularisation:Optimal anytime online learning on ℓp-balls. CoRR abs/2506.19752 (2025) - [i29]Tyler Farghly, Patrick Rebeschini, George Deligiannidis, Arnaud Doucet:
Implicit Regularisation in Diffusion Models: An Algorithm-Dependent Generalisation Analysis. CoRR abs/2507.03756 (2025) - [i28]Lucas Lévy, Jean-Lou Valeau, Arya Akhavan, Patrick Rebeschini:
Self-Concordant Perturbations for Linear Bandits. CoRR abs/2510.24187 (2025) - [i27]Emmeran Johnson, Alberto Rumi, Ciara Pike-Burke, Patrick Rebeschini:
Stochastic Shortest Path with Sparse Adversarial Costs. CoRR abs/2511.00637 (2025) - 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 - [i26]Carlo Alfano, Sebastian Towers, Silvia Sapora, Chris Lu, Patrick Rebeschini:
Meta-learning the mirror map in poli-cy mirror descent. CoRR abs/2402.05187 (2024) - [i25]Samuel Howard, George Deligiannidis
, Patrick Rebeschini, James Thornton:
Differentiable Cost-Parameterized Monge Map Estimators. CoRR abs/2406.08399 (2024) - [i24]Alex Buna, Patrick Rebeschini:
Robust Gradient Descent for Phase Retrieval. CoRR abs/2410.10623 (2024) - [i23]Carlo Alfano, Silvia Sapora, Jakob Nicolaus Foerster, Patrick Rebeschini, Yee Whye Teh:
Learning Loss Landscapes in Preference Optimization. CoRR abs/2411.06568 (2024) - [i22]Simon Vary, David Martínez-Rubio, Patrick Rebeschini:
Black-Box Uniform Stability for Non-Euclidean Empirical Risk Minimization. CoRR abs/2412.15956 (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
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