← Authors

Patrick Rebeschini

23 papers · 2015–2025 · 6 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (10) 🐝 Cross-Pollinator (12) 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (32) 🧭 Keyword Pioneer 🌍 Conference Polyglot (6) πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (93) πŸ’Ž Century Club (23) ⚑ Prolific Year (5)

Conferences

NIPS (12) AISTATS (4) JMLR (3) ICLR (2) COLT (1) ICML (1)

Top co-authors

Dominic Richards (4) Varun Kanade (4) Fan Wu (3) Tomas Vaskevicius (3) David MartΓ­nez-Rubio (2) Carlo Alfano (2) Emmeran Johnson (2) Ciara Pike-Burke (2) Sekhar C Tatikonda (1) sekhar tatikonda (1)

Keywords

implicit regularization (5) gradient descent (5) distributed learning (4) mirror descent (4) non-convex optimization (3) empirical risk minimization (3) algorithmic stability (3) stochastic gradient descent (2) generalization bound (2) sparse phase retrieval (2) signal reconstruction (2) distributed optimization (2) sample complexity (1) convergence analysis (1) model selection (1) ridge regression (1) policy gradient (1) sparse recovery (1) statistical learning theory (1) markov decision process (1)

Papers

Learning mirror maps in policy mirror descent ICLR 2025
Black-Box Uniform Stability for Non-Euclidean Empirical Risk Minimization AISTATS 2025
Robust Gradient Descent for Phase Retrieval AISTATS 2025
Generalization Bounds for Label Noise Stochastic Gradient Descent AISTATS 2024
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition JMLR 2024
Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity ICLR 2024
Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes NIPS 2023
A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence NIPS 2023
Implicit Regularization in Matrix Sensing via Mirror Descent NIPS 2021
On Optimal Interpolation in Linear Regression NIPS 2021
Hadamard Wirtinger Flow for Sparse Phase Retrieval AISTATS 2021
Distributed Machine Learning with Sparse Heterogeneous Data NIPS 2021
Time-independent Generalization Bounds for SGLD in Non-convex Settings NIPS 2021
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval NIPS 2020
The Statistical Complexity of Early-Stopped Mirror Descent NIPS 2020
Decentralised Learning with Random Features and Distributed Gradient Descent ICML 2020
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent JMLR 2020
A New Approach to Laplacian Solvers and Flow Problems JMLR 2019
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up NIPS 2019
Decentralized Cooperative Stochastic Bandits NIPS 2019
Implicit Regularization for Optimal Sparse Recovery NIPS 2019
Accelerated consensus via Min-Sum Splitting NIPS 2017
Fast Mixing for Discrete Point Processes COLT 2015