Miguel Lázaro-Gredilla
20 papers · 2009–2025 · 6 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (45) 🏃 Academic Marathon · Full 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
🔬
Deep Specialist
(10)
🤝
Dynamic Duo
(10)
🧬
Topic Evolution
🏆
Keyword Champion
📈
Trend Setter
🗃️
Keyword Collector
(120)
❓
The Questioner
🔥
Unstoppable · Bronze
💎
Century Club · Bronze
🌈
Renaissance Researcher
(6)
Top co-authors
Research topics
Artificial Intelligence
(17)
Machine Learning
(16)
Bayesian & Probabilistic · Artificial Intelligence
(12)
Core Methods
(11)
Optimization & Theory
(11)
Bayesian & Probabilistic · Machine Learning
(8)
Deep Learning
(7)
Core AI
(5)
Learning Types
(4)
Models
(4)
Reinforcement Learning
(3)
Applications
(3)
Mathematics & Optimization
(2)
Architectures
(2)
Learning Paradigms
(2)
Keywords
variational inference
(8)
gaussian process
(4)
bayesian inference
(4)
probabilistic graphical model
(3)
gaussian process regression
(3)
approximate inference
(2)
gaussian processes
(2)
belief propagation
(2)
covariance function
(2)
generative model
(2)
markov random field
(2)
stochastic variational inference
(2)
uncertainty quantification
(1)
structure learning
(1)
offline reinforcement learning
(1)
representation learning
(1)
logistic regression
(1)
ising model
(1)
multi-task learning
(1)
transformer
(1)
Papers
Improving Transformer World Models for Data-Efficient RL
ICML 2025
DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors
NeurIPS 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
What type of inference is planning?
NeurIPS 2024
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation
ICCV 2023
Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation
ICML 2023
Schema-learning and rebinding as mechanisms of in-context learning and emergence
NeurIPS 2023
Perturb-and-max-product: Sampling and learning in discrete energy-based models
NeurIPS 2021
Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables
AAAI 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
NeurIPS 2015
Doubly Stochastic Variational Bayes for non-Conjugate Inference
ICML 2014
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression
NeurIPS 2013
Bayesian Warped Gaussian Processes
NeurIPS 2012
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning
NeurIPS 2011
Sparse Spectrum Gaussian Process Regression
JMLR 2010
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features
NeurIPS 2009