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Gergely Neu

46 papers · 2010–2025 · 7 conferences · across top CS/AI conferences

Achievements

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+10 more ↓ 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer πŸ—ΊοΈ Taxonomy Completionist (14) 🌍 Conference Polyglot (7)
πŸ—ΊοΈ Taxonomy Completionist (14) 🧭 Keyword Pioneer πŸ† Keyword Champion (4) πŸ”¬ Deep Specialist (14) πŸ—ƒοΈ Keyword Collector (136) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (46) πŸ”₯ Unstoppable (14) ⚑ Prolific Year (6)

Conferences

NIPS (15) COLT (12) AISTATS (7) ALT (7) ICML (3) JMLR (1) L4DC (1)

Research topics

Papers

Offline RL via Feature-Occupancy Gradient Ascent AISTATS 2025 Online-to-PAC generalization bounds under graph-mixing dependencies AISTATS 2025 Generalization bounds for mixing processes via delayed online-to-PAC conversions ALT 2025 Optimistically Optimistic Exploration for Provably Efficient Infinite-Horizon Reinforcement and Imitation Learning COLT 2025 Bisimulation Metrics are Optimal Transport Distances, and Can be Computed Efficiently NIPS 2024 Offline Primal-Dual Reinforcement Learning for Linear MDPs AISTATS 2024 Dealing With Unbounded Gradients in Stochastic Saddle-point Optimization ICML 2024 Importance-Weighted Offline Learning Done Right ALT 2024 Adversarial Contextual Bandits Go Kernelized ALT 2024 Optimistic Information Directed Sampling COLT 2024 Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization ALT 2023 Optimistic Planning by Regularized Dynamic Programming ICML 2023 Online Learning with Off-Policy Feedback ALT 2023 Conference on Learning Theory 2023: Preface COLT 2023 First- and Second-Order Bounds for Adversarial Linear Contextual Bandits NIPS 2023 Nonstochastic Contextual Combinatorial Bandits AISTATS 2023 Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits NIPS 2022 Generalization Bounds via Convex Analysis COLT 2022 Proximal Point Imitation Learning NIPS 2022 Logistic Q-Learning AISTATS 2021 Online learning in MDPs with linear function approximation and bandit feedback. NIPS 2021 Information-Theoretic Generalization Bounds for Stochastic Gradient Descent COLT 2021 Faster saddle-point optimization for solving large-scale Markov decision processes L4DC 2020 A Unifying View of Optimism in Episodic Reinforcement Learning NIPS 2020 Algorithmic Learning Theory 2020: Preface ALT 2020 Fast Rates for Online Prediction with Abstention COLT 2020 Efficient and robust algorithms for adversarial linear contextual bandits COLT 2020 Bandit Principal Component Analysis COLT 2019 Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates NIPS 2019 Beating SGD Saturation with Tail-Averaging and Minibatching NIPS 2019 Online Influence Maximization with Local Observations ALT 2019 Iterate Averaging as Regularization for Stochastic Gradient Descent COLT 2018 Fast rates for online learning in Linearly Solvable Markov Decision Processes COLT 2017 Algorithmic Stability and Hypothesis Complexity ICML 2017 Boltzmann Exploration Done Right NIPS 2017 Online Learning with Noisy Side Observations AISTATS 2016 Importance Weighting Without Importance Weights: An Efficient Algorithm for Combinatorial Semi-Bandits JMLR 2016 Explore no more: Improved high-probability regret bounds for non-stochastic bandits NIPS 2015 First-order regret bounds for combinatorial semi-bandits COLT 2015 Efficient learning by implicit exploration in bandit problems with side observations NIPS 2014 Exploiting easy data in online optimization NIPS 2014 Online combinatorial optimization with stochastic decision sets and adversarial losses NIPS 2014 Online learning in episodic Markovian decision processes by relative entropy policy search NIPS 2013 Prediction by random-walk perturbation COLT 2013 The adversarial stochastic shortest path problem with unknown transition probabilities AISTATS 2012 Online Markov Decision Processes under Bandit Feedback NIPS 2010