Nonlinear Filtering in High Dimension - Patrick Rebeschini (2014)

Fast Mixing for Discrete Point Processes. - Patrick Rebeschini; Amin Karbasi (COLT, 2015)

Decay of correlation in network flow problems. - Patrick Rebeschini; Sekhar Tatikonda (CISS, 2016)

Accelerated consensus via Min-Sum Splitting. - Patrick Rebeschini; Sekhar Tatikonda (NIPS, 2017)

Decentralized Cooperative Stochastic Multi-armed Bandits. - David Martínez-Rubio; Varun Kanade; Patrick Rebeschini (CoRR, 2018)

Decentralized Cooperative Stochastic Bandits. - David Martínez-Rubio; Varun Kanade; Patrick Rebeschini (NeurIPS, 2019)

Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up. - Dominic Richards; Patrick Rebeschini (NeurIPS, 2019)

Implicit Regularization for Optimal Sparse Recovery. - Tomas Vaskevicius; Varun Kanade; Patrick Rebeschini (NeurIPS, 2019)

A New Approach to Laplacian Solvers and Flow Problems. - Patrick Rebeschini; Sekhar Tatikonda (J. Mach. Learn. Res., 2019)

Locality in Network Optimization. - Patrick Rebeschini; Sekhar Tatikonda (IEEE Trans. Control. Netw. Syst., 2019)

Decentralised Learning with Random Features and Distributed Gradient Descent. - Dominic Richards; Patrick Rebeschini; Lorenzo Rosasco (ICML, 2020)

The Statistical Complexity of Early-Stopped Mirror Descent. - Tomas Vaskevicius; Varun Kanade; Patrick Rebeschini (NeurIPS, 2020)

A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval. - Fan Wu 010; Patrick Rebeschini (NeurIPS, 2020)

Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent. - Dominic Richards; Patrick Rebeschini (J. Mach. Learn. Res., 2020)

Hadamard Wirtinger Flow for Sparse Phase Retrieval. - Fan Wu 010; Patrick Rebeschini (AISTATS, 2021)

Time-independent Generalization Bounds for SGLD in Non-convex Settings. - Tyler Farghly; Patrick Rebeschini (NeurIPS, 2021)

On Optimal Interpolation in Linear Regression. - Eduard Oravkin; Patrick Rebeschini (NeurIPS, 2021)

Distributed Machine Learning with Sparse Heterogeneous Data. - Dominic Richards; Sahand Negahban; Patrick Rebeschini (NeurIPS, 2021)

Implicit Regularization in Matrix Sensing via Mirror Descent. - Fan Wu 010; Patrick Rebeschini (NeurIPS, 2021)

Nearly Minimax-Optimal Rates for Noisy Sparse Phase Retrieval via Early-Stopped Mirror Descent. - Fan Wu 010; Patrick Rebeschini (CoRR, 2021)

Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models? - Dominic Richards; Edgar Dobriban; Patrick Rebeschini (CoRR, 2021)

Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning. - Carlo Alfano; Patrick Rebeschini (CoRR, 2021)

Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition. - Varun Kanade; Patrick Rebeschini; Tomas Vaskevicius (CoRR, 2022)

Linear Convergence for Natural Policy Gradient with Log-linear Policy Parametrization. - Carlo Alfano; Patrick Rebeschini (CoRR, 2022)

A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence. - Carlo Alfano; Rui Yuan; Patrick Rebeschini (NeurIPS, 2023)

Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes. - Emmeran Johnson; Ciara Pike-Burke; Patrick Rebeschini (NeurIPS, 2023)

A Novel Framework for Policy Mirror Descent with General Parametrization and Linear Convergence. - Carlo Alfano; Rui Yuan; Patrick Rebeschini (CoRR, 2023)

Generalization Bounds for Label Noise Stochastic Gradient Descent. - Jung Eun Huh; Patrick Rebeschini (AISTATS, 2024)

Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity. - Emmeran Johnson; Ciara Pike-Burke; Patrick Rebeschini (ICLR, 2024)

Meta-learning the mirror map in policy mirror descent. - Carlo Alfano; Sebastian Towers; Silvia Sapora; Chris Lu 001; Patrick Rebeschini (CoRR, 2024)

Differentiable Cost-Parameterized Monge Map Estimators. - Samuel Howard; George Deligiannidis; Patrick Rebeschini; James Thornton (CoRR, 2024)

Learning Loss Landscapes in Preference Optimization. - Carlo Alfano; Silvia Sapora; Jakob Nicolaus Foerster; Patrick Rebeschini; Yee Whye Teh (CoRR, 2024)

Robust Gradient Descent for Phase Retrieval. - Alex Buna; Patrick Rebeschini (AISTATS, 2025)

Black-Box Uniform Stability for Non-Euclidean Empirical Risk Minimization. - Simon Vary; David Martínez-Rubio; Patrick Rebeschini (AISTATS, 2025)

Learning mirror maps in policy mirror descent. - Carlo Alfano; Sebastian Rene Towers; Silvia Sapora; Chris Lu 001; Patrick Rebeschini (ICLR, 2025)

Early-Stopped Mirror Descent for Linear Regression over Convex Bodies. - Tobias Wegel; Gil Kur; Patrick Rebeschini (CoRR, 2025)

Non-stationary Bandit Convex Optimization: A Comprehensive Study. - Xiaoqi Liu; Dorian Baudry; Julian Zimmert; Patrick Rebeschini; Arya Akhavan (CoRR, 2025)

On the necessity of adaptive regularisation:Optimal anytime online learning on ℓp-balls. - Emmeran Johnson; David Martínez-Rubio; Ciara Pike-Burke; Patrick Rebeschini (CoRR, 2025)

Implicit Regularisation in Diffusion Models: An Algorithm-Dependent Generalisation Analysis. - Tyler Farghly; Patrick Rebeschini; George Deligiannidis; Arnaud Doucet (CoRR, 2025)