Lázaro-Gredilla, Miguel

24 publications

ICLR 2026 Code World Models for General Game Playing Wolfgang Lehrach, Daniel Hennes, Miguel Lazaro-Gredilla, Xinghua Lou, Carter Wendelken, Zun Li, Antoine Dedieu, Marc Lanctot, Atil Iscen, John Schultz, Marcus Chiam, Ian Gemp, Piotr Zielinski, Satinder Singh, Kevin Patrick Murphy

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