Patrick Rebeschini

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 43

Top Topics

Markov Decision Process

Reinforcement Learning

Top Venues

J. Mach. Learn. Res.

Publications

Samuel Howard, George Deligiannidis, Patrick Rebeschini, James Thornton
Differentiable Cost-Parameterized Monge Map Estimators. CoRR (2024)
Emmeran Johnson, Ciara Pike-Burke, Patrick Rebeschini
Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity. ICLR (2024)
Carlo Alfano, Sebastian Towers, Silvia Sapora, Chris Lu, Patrick Rebeschini
Meta-learning the mirror map in policy mirror descent. CoRR (2024)
Jung Eun Huh, Patrick Rebeschini
Generalization Bounds for Label Noise Stochastic Gradient Descent. AISTATS (2024)
Carlo Alfano, Rui Yuan, Patrick Rebeschini
A Novel Framework for Policy Mirror Descent with General Parametrization and Linear Convergence. CoRR (2023)
Carlo Alfano, Rui Yuan, Patrick Rebeschini
A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence. NeurIPS (2023)
Jung Eun Huh, Patrick Rebeschini
Generalization Bounds for Label Noise Stochastic Gradient Descent. CoRR (2023)
Emmeran Johnson, Ciara Pike-Burke, Patrick Rebeschini
Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes. NeurIPS (2023)
Emmeran Johnson, Ciara Pike-Burke, Patrick Rebeschini
Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity. CoRR (2023)
Emmeran Johnson, Ciara Pike-Burke, Patrick Rebeschini
Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes. CoRR (2023)
Varun Kanade, Patrick Rebeschini, Tomas Vaskevicius
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition. CoRR (2022)
Carlo Alfano, Patrick Rebeschini
Linear Convergence for Natural Policy Gradient with Log-linear Policy Parametrization. CoRR (2022)
Fan Wu, Patrick Rebeschini
Implicit Regularization in Matrix Sensing via Mirror Descent. CoRR (2021)
Eduard Oravkin, Patrick Rebeschini
On Optimal Interpolation In Linear Regression. CoRR (2021)
Carlo Alfano, Patrick Rebeschini
Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning. CoRR (2021)
Fan Wu, Patrick Rebeschini
Hadamard Wirtinger Flow for Sparse Phase Retrieval. AISTATS (2021)
Fan Wu, Patrick Rebeschini
Nearly Minimax-Optimal Rates for Noisy Sparse Phase Retrieval via Early-Stopped Mirror Descent. CoRR (2021)
Fan Wu, Patrick Rebeschini
Implicit Regularization in Matrix Sensing via Mirror Descent. NeurIPS (2021)
Dominic Richards, Edgar Dobriban, Patrick Rebeschini
Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models? CoRR (2021)
Tyler Farghly, Patrick Rebeschini
Time-independent Generalization Bounds for SGLD in Non-convex Settings. CoRR (2021)
Eduard Oravkin, Patrick Rebeschini
On Optimal Interpolation in Linear Regression. NeurIPS (2021)
Dominic Richards, Sahand Negahban, Patrick Rebeschini
Distributed Machine Learning with Sparse Heterogeneous Data. NeurIPS (2021)
Tyler Farghly, Patrick Rebeschini
Time-independent Generalization Bounds for SGLD in Non-convex Settings. NeurIPS (2021)
Dominic Richards, Patrick Rebeschini
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent. J. Mach. Learn. Res. 21 (2020)
Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco
Decentralised Learning with Random Features and Distributed Gradient Descent. ICML (2020)
Fan Wu, Patrick Rebeschini
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval. NeurIPS (2020)
Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco
Decentralised Learning with Random Features and Distributed Gradient Descent. CoRR (2020)
Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini
The Statistical Complexity of Early-Stopped Mirror Descent. NeurIPS (2020)
Fan Wu, Patrick Rebeschini
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval. CoRR (2020)
Fan Wu, Patrick Rebeschini
Hadamard Wirtinger Flow for Sparse Phase Retrieval. CoRR (2020)
Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini
The Statistical Complexity of Early Stopped Mirror Descent. CoRR (2020)
Patrick Rebeschini, Sekhar Tatikonda
Locality in Network Optimization. IEEE Trans. Control. Netw. Syst. 6 (2) (2019)
Dominic Richards, Patrick Rebeschini
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up. CoRR (2019)
Patrick Rebeschini, Sekhar Tatikonda
A New Approach to Laplacian Solvers and Flow Problems. J. Mach. Learn. Res. 20 (2019)
David Martínez-Rubio, Varun Kanade, Patrick Rebeschini
Decentralized Cooperative Stochastic Bandits. NeurIPS (2019)
Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini
Implicit Regularization for Optimal Sparse Recovery. CoRR (2019)
Dominic Richards, Patrick Rebeschini
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up. NeurIPS (2019)
Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini
Implicit Regularization for Optimal Sparse Recovery. NeurIPS (2019)
Dominic Richards, Patrick Rebeschini
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent. CoRR (2018)
David Martínez-Rubio, Varun Kanade, Patrick Rebeschini
Decentralized Cooperative Stochastic Multi-armed Bandits. CoRR (2018)
Patrick Rebeschini, Sekhar C. Tatikonda
Accelerated consensus via Min-Sum Splitting. NIPS (2017)
Patrick Rebeschini, Sekhar Tatikonda
A new approach to Laplacian solvers and flow problems. CoRR (2016)
Patrick Rebeschini, Sekhar Tatikonda
Decay of correlation in network flow problems. CISS (2016)
Patrick Rebeschini, Amin Karbasi
Fast Mixing for Discrete Point Processes. COLT (2015)