← Authors

Miguel Lázaro-Gredilla

20 papers · 2009–2025 · 6 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌍 Conference Polyglot (6) 🌈 Renaissance Researcher (5) 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (16) 🐝 Cross-Pollinator (13) 🗺️ Taxonomy Completionist (14) 🧭 Keyword Pioneer 🤝 Dynamic Duo (10) 🧬 Topic Evolution 💎 Century Club (20) 📈 Trend Setter 🔥 Unstoppable (7) ❓ The Questioner 🗃️ Keyword Collector (104)

Conferences

NIPS (9) ICML (5) AAAI (2) JMLR (2) AISTATS (1) ICCV (1)

Top co-authors

Dileep George (10) Antoine Dedieu (9) Guangyao Zhou (6) Wolfgang Lehrach (5) Xinghua Lou (2) Carter Wendelken (2) Michalis Titsias RC AUEB (2) Joseph Ortiz (2) Nishad Gothoskar (2) Sivaramakrishnan Swaminathan (2)

Keywords

variational inference (8) gaussian process (4) bayesian inference (3) gaussian process regression (3) probabilistic graphical model (3) approximate inference (2) belief propagation (2) gaussian processes (2) markov random field (2) generative model (2) uncertainty quantification (1) structure learning (1) offline reinforcement learning (1) representation learning (1) logistic regression (1) marginal likelihood (1) multi-task learning (1) ising model (1) depth estimation (1) sample complexity (1)

Papers

Improving Transformer World Models for Data-Efficient RL ICML 2025
What type of inference is planning? NIPS 2024
Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments ICML 2024
PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX JMLR 2024
DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors NIPS 2024
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation ICCV 2023
Schema-learning and rebinding as mechanisms of in-context learning and emergence NIPS 2023
Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation ICML 2023
Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables AAAI 2021
Perturb-and-max-product: Sampling and learning in discrete energy-based models NIPS 2021
Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models AAAI 2021
Variational Rejection Sampling AISTATS 2018
Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics ICML 2017
Local Expectation Gradients for Black Box Variational Inference NIPS 2015
Doubly Stochastic Variational Bayes for non-Conjugate Inference ICML 2014
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression NIPS 2013
Bayesian Warped Gaussian Processes NIPS 2012
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning NIPS 2011
Sparse Spectrum Gaussian Process Regression JMLR 2010
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features NIPS 2009