Lázaro-Gredilla, Miguel

23 publications

TMLR 2025 Diffusion Model Predictive Control Guangyao Zhou, Sivaramakrishnan Swaminathan, Rajkumar Vasudeva Raju, J Swaroop Guntupalli, Wolfgang Lehrach, Joseph Ortiz, Antoine Dedieu, Miguel Lazaro-Gredilla, Kevin Patrick Murphy

ICML 2025 Improving Transformer World Models for Data-Efficient RL Antoine Dedieu, Joseph Ortiz, Xinghua Lou, Carter Wendelken, J Swaroop Guntupalli, Wolfgang Lehrach, Miguel Lazaro-Gredilla, Kevin Patrick Murphy

ICLRW 2025 Improving Transformer World Models for Data-Efficient RL Antoine Dedieu, Joseph Ortiz, Xinghua Lou, Carter Wendelken, Wolfgang Lehrach, J Swaroop Guntupalli, Miguel Lazaro-Gredilla, Kevin Patrick Murphy

NeurIPS 2024 DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors Joseph Ortiz, Antoine Dedieu, Wolfgang Lehrach, J. Swaroop Guntupalli, Carter Wendelken, Ahmad Humayun, Guangyao Zhou, Sivaramakrishnan Swaminathan, Miguel Lázaro-Gredilla, Kevin Murphy

ICML 2024 Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments Antoine Dedieu, Wolfgang Lehrach, Guangyao Zhou, Dileep George, Miguel Lazaro-Gredilla

MLOSS 2024 PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX Guangyao Zhou, Antoine Dedieu, Nishanth Kumar, Wolfgang Lehrach, Shrinu Kushagra, Dileep George, Miguel Lázaro-Gredilla

NeurIPS 2024 What Type of Inference Is Planning? Miguel Lázaro-Gredilla, Li Yang Ku, Kevin P. Murphy, Dileep George

ICCV 2023 3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6d Pose Estimation Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B. Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, Vikash K. Mansinghka

ICML 2023 Learning Noisy or Bayesian Networks with Max-Product Belief Propagation Antoine Dedieu, Guangyao Zhou, Dileep George, Miguel Lazaro-Gredilla

NeurIPS 2023 Schema-Learning and Rebinding as Mechanisms of In-Context Learning and Emergence Sivaramakrishnan Swaminathan, Antoine Dedieu, Rajkumar Vasudeva Raju, Murray Shanahan, Miguel Lazaro-Gredilla, Dileep George

NeurIPS 2021 Perturb-and-Max-Product: Sampling and Learning in Discrete Energy-Based Models Miguel Lazaro-Gredilla, Antoine Dedieu, Dileep George

AAAI 2021 Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables Miguel Lázaro-Gredilla, Wolfgang Lehrach, Nishad Gothoskar, Guangyao Zhou, Antoine Dedieu, Dileep George

AAAI 2021 Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models Antoine Dedieu, Miguel Lázaro-Gredilla, Dileep George

AISTATS 2018 Variational Rejection Sampling Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon

ICML 2017 Schema Networks: Zero-Shot Transfer with a Generative Causal Model of Intuitive Physics Ken Kansky, Tom Silver, David A. Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, Scott Phoenix, Dileep George

NeurIPS 2015 Local Expectation Gradients for Black Box Variational Inference Michalis Titsias RC Aueb, Miguel Lázaro-Gredilla

ICML 2014 Doubly Stochastic Variational Bayes for Non-Conjugate Inference Michalis Titsias, Miguel Lázaro-Gredilla

NeurIPS 2013 Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression Michalis Titsias RC Aueb, Miguel Lazaro-Gredilla

NeurIPS 2012 Bayesian Warped Gaussian Processes Miguel Lázaro-Gredilla

NeurIPS 2011 Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning Michalis K. Titsias, Miguel Lázaro-Gredilla

ICML 2011 Variational Heteroscedastic Gaussian Process Regression Miguel Lázaro-Gredilla, Michalis K. Titsias

JMLR 2010 Sparse Spectrum Gaussian Process Regression Miguel Lázaro-Gredilla, Joaquin Quiñnero-Candela, Carl Edward Rasmussen, Aníbal R. Figueiras-Vidal

NeurIPS 2009 Inter-Domain Gaussian Processes for Sparse Inference Using Inducing Features Miguel Lázaro-Gredilla, Aníbal Figueiras-Vidal