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George Tucker

29 papers · 2016–2025 · 6 venues · across top CS/AI venues

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Achievements

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+11 more ↓ πŸ—ΊοΈ Taxonomy Completionist (48) πŸƒ Academic Marathon Β· Sprint 🌍 Conference Polyglot Β· Silver πŸŒ‰ Interdisciplinary Bridge Β· Silver 🐣 Hot Topic Early Bird
πŸ‘₯ Mega-Team Β· Squad πŸ‘‘ Triple Crown πŸ† Keyword Champion (2) 🧭 Keyword Pioneer 🌈 Renaissance Researcher (5) πŸ”₯ Unstoppable Β· Bronze ⚑ Prolific Year Β· Productive πŸš€ Conference Pioneer πŸ’Ž Century Club Β· Bronze πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (124)

Venues

NeurIPS (11) ICLR (10) ICML (5) AISTATS (1) Interspeech (1) RSS (1)

Top co-authors

Sergey Levine (9) Ofir Nachum (7) Aviral Kumar (6) Mohammad Norouzi (5) Bo Dai (4) Andriy Mnih (4) Rishabh Agarwal (4) Justin Fu (3) John Lawson (3) Cosmin Paduraru (3)

Research topics

Machine Learning (24) Reinforcement Learning (14) Optimization & Theory (14) Methods (13) Artificial Intelligence (12) Deep Learning (12) Learning Types (11) Core AI (8) Models (8) Core Methods (7) Application Areas (4) Applications (3) Bayesian & Probabilistic Β· Machine Learning (3) Bayesian & Probabilistic Β· Artificial Intelligence (2) Learning Paradigms (2)

Keywords

offline reinforcement learning (6) variational inference (6) variance reduction (4) gradient estimator (4) continuous control (4) representation learning (3) deep reinforcement learning (3) sample efficiency (3) reinforcement learning (3) latent variable model (2) generative model (2) contextual bandit (2) model-based reinforcement learning (2) off-policy learning (2) importance sampling (2) model selection (2) policy gradient (2) marginal likelihood (2) value function (2) policy selection (2)

Papers

Training Language Models to Self-Correct via Reinforcement Learning ICLR 2025 Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes ICLR 2023 Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research NeurIPS 2023 DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization ICLR 2022 Model Selection in Batch Policy Optimization ICML 2022 Offline Policy Selection under Uncertainty AISTATS 2022 Oracle Inequalities for Model Selection in Offline Reinforcement Learning NeurIPS 2022 Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization ICLR 2021 Benchmarks for Deep Off-Policy Evaluation ICLR 2021 Coupled Gradient Estimators for Discrete Latent Variables NeurIPS 2021 Conservative Q-Learning for Offline Reinforcement Learning NeurIPS 2020 DisARM: An Antithetic Gradient Estimator for Binary Latent Variables NeurIPS 2020 Meta-Learning without Memorization ICLR 2020 Model Based Reinforcement Learning for Atari ICLR 2020 Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse NeurIPS 2019 Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives ICLR 2019 Energy-Inspired Models: Learning with Sampler-Induced Distributions NeurIPS 2019 Guided evolutionary strategies: augmenting random search with surrogate gradients ICML 2019 Learning to Walk Via Deep Reinforcement Learning RSS 2019 On Variational Bounds of Mutual Information ICML 2019 Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction NeurIPS 2019 The Laplacian in RL: Learning Representations with Efficient Approximations ICLR 2019 Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling ICLR 2018 Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion NeurIPS 2018 Smoothed Action Value Functions for Learning Gaussian Policies ICML 2018 The Mirage of Action-Dependent Baselines in Reinforcement Learning ICML 2018 Filtering Variational Objectives NeurIPS 2017 REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models NeurIPS 2017 Model Compression Applied to Small-Footprint Keyword Spotting Interspeech 2016