conftrace_

George Tucker

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

Achievements

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+11 more ↓

🌍 Conference Polyglot (6) 🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🏃 Academic Marathon (9)

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🏃 Academic Marathon (9) 👑 Triple Crown 👥 Mega-Team (22) 🔥 Unstoppable (8) 🗃️ Keyword Collector (88) 📈 Trend Setter 🚀 Conference Pioneer ⚡ Prolific Year (8) 💎 Century Club (29)

Conferences

NIPS (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

Reinforcement Learning (1) Robotics (1) Optimization & Theory (1)

Keywords

variational inference (5) offline reinforcement learning (4) variance reduction (4) continuous control (3) sample efficiency (3) gradient estimator (3) marginal likelihood (2) evidence lower bound (2) generative model (2) reinforcement learning (2) off-policy learning (2) model selection (2) policy gradient (2) deep reinforcement learning (2) importance sampling (2) discrete latent variable (2) uncertainty quantification (1) policy evaluation (1) ensemble learning (1) generative modeling (1)

Papers

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