Meger, David

30 publications

NeurIPS 2025 Convergence Theorems for Entropy-Regularized and Distributional Reinforcement Learning Yash Jhaveri, Harley Wiltzer, Patrick Shafto, Marc G Bellemare, David Meger

NeurIPS 2025 Epistemic Uncertainty Estimation in Regression Ensemble Models with Pairwise Epistemic Estimators Lucas Berry, David Meger

NeurIPS 2024 Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning Harley Wiltzer, Marc G. Bellemare, David Meger, Patrick Shafto, Yash Jhaveri

NeurIPS 2024 Parseval Regularization for Continual Reinforcement Learning Wesley Chung, Lynn Cherif, David Meger, Doina Precup

JMLR 2024 Policy Gradient Methods in the Presence of Symmetries and State Abstractions Prakash Panangaden, Sahand Rezaei-Shoshtari, Rosie Zhao, David Meger, Doina Precup

UAI 2024 Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Models Lucas Berry, Axel Brando, David Meger

NeurIPS 2023 For SALE: State-Action Representation Learning for Deep Reinforcement Learning Scott Fujimoto, Wei-Di Chang, Edward Smith, Shixiang Gu, Doina Precup, David Meger

AAAI 2023 Hypernetworks for Zero-Shot Transfer in Reinforcement Learning Sahand Rezaei-Shoshtari, Charlotte Morissette, François Robert Hogan, Gregory Dudek, David Meger

AAAI 2023 Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling Lucas Berry, David Meger

NeurIPSW 2022 Bayesian Q-Learning with Imperfect Expert Demonstrations Fengdi Che, Xiru Zhu, Doina Precup, David Meger, Gregory Dudek

NeurIPSW 2022 Bayesian Q-Learning with Imperfect Expert Demonstrations Fengdi Che, Xiru Zhu, Doina Precup, David Meger, Gregory Dudek

NeurIPS 2022 Continuous MDP Homomorphisms and Homomorphic Policy Gradient Sahand Rezaei-Shoshtari, Rosie Zhao, Prakash Panangaden, David Meger, Doina Precup

ICML 2022 Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning Harley E Wiltzer, David Meger, Marc G. Bellemare

NeurIPSW 2022 Learning Successor Feature Representations to Train Robust Policies for Multi-Task Learning Melissa Mozifian, Dieter Fox, David Meger, Fabio Ramos, Animesh Garg

ICML 2022 Why Should I Trust You, Bellman? the Bellman Error Is a Poor Replacement for Value Error Scott Fujimoto, David Meger, Doina Precup, Ofir Nachum, Shixiang Shane Gu

ICML 2021 A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation Scott Fujimoto, David Meger, Doina Precup

NeurIPS 2021 Active 3D Shape Reconstruction from Vision and Touch Edward Smith, David Meger, Luis Pineda, Roberto Calandra, Jitendra Malik, Adriana Romero Soriano, Michal Drozdzal

AAAI 2021 Learning Intuitive Physics with Multimodal Generative Models Sahand Rezaei-Shoshtari, Francois Robert Hogan, Michael Jenkin, David Meger, Gregory Dudek

WACV 2021 Seeing Through Your Skin: Recognizing Objects with a Novel Visuotactile Sensor Francois R. Hogan, Michael Jenkin, Sahand Rezaei-Shoshtari, Yogesh Girdhar, David Meger, Gregory Dudek

NeurIPS 2020 3D Shape Reconstruction from Vision and Touch Edward Smith, Roberto Calandra, Adriana Romero, Georgia Gkioxari, David Meger, Jitendra Malik, Michal Drozdzal

NeurIPS 2020 An Equivalence Between Loss Functions and Non-Uniform Sampling in Experience Replay Scott Fujimoto, David Meger, Doina Precup

ICML 2019 GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects Edward Smith, Scott Fujimoto, Adriana Romero, David Meger

ICML 2019 Off-Policy Deep Reinforcement Learning Without Exploration Scott Fujimoto, David Meger, Doina Precup

ICML 2018 Addressing Function Approximation Error in Actor-Critic Methods Scott Fujimoto, Herke Hoof, David Meger

AAAI 2018 Deep Reinforcement Learning That Matters Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger

NeurIPS 2018 Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation Edward Smith, Scott Fujimoto, David Meger

AAAI 2018 OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup

CoRL 2017 Improved Adversarial Systems for 3D Object Generation and Reconstruction Edward J. Smith, David Meger

AAAI 2007 Hybrid Inference for Sensor Network Localization Using a Mobile Robot Dimitri Marinakis, David Meger, Ioannis M. Rekleitis, Gregory Dudek

AAAI 2007 The UBC Semantic Robot Vision System Scott Helmer, David Meger, Per-Erik Forssén, Tristram Southey, Sancho McCann, Pooyan Fazli, Jim Little, David G. Lowe