Miguel Lázaro-Gredilla

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 37

Top Topics

Graphical Models

Hidden Variables

Gaussian Process

Belief Propagation

Top Venues

IEEE Trans. Neural Networks Learn. Syst.

Publications

Xinghua Lou, Meet Dave, Shrinu Kushagra, Miguel Lázaro-Gredilla, Kevin Murphy
Model Predictive Simulation Using Structured Graphical Models and Transformers. CoRR (2024)
Antoine Dedieu, Wolfgang Lehrach, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla
Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments. CoRR (2024)
Miguel Lázaro-Gredilla, Li Yang Ku, Kevin P. Murphy, Dileep George
What type of inference is planning? CoRR (2024)
Antoine Dedieu, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla
Learning noisy-OR Bayesian Networks with Max-Product Belief Propagation. CoRR (2023)
Miguel Lázaro-Gredilla, Ishan Deshpande, Sivaramakrishnan Swaminathan, Meet Dave, Dileep George
Fast exploration and learning of latent graphs with aliased observations. CoRR (2023)
Sivaramakrishnan Swaminathan, Antoine Dedieu, Rajkumar Vasudeva Raju, Murray Shanahan, Miguel Lázaro-Gredilla, Dileep George
Schema-learning and rebinding as mechanisms of in-context learning and emergence. CoRR (2023)
Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B. Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, Vikash K. Mansinghka
3D Neural Embedding Likelihood for Robust Sim-to-Real Transfer in Inverse Graphics. CoRR (2023)
Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B. Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, Vikash K. Mansinghka
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation. ICCV (2023)
J. Swaroop Guntupalli, Rajkumar Vasudeva Raju, Shrinu Kushagra, Carter Wendelken, Danny Sawyer, Ishan Deshpande, Guangyao Zhou, Miguel Lázaro-Gredilla, Dileep George
Graph schemas as abstractions for transfer learning, inference, and planning. CoRR (2023)
Antoine Dedieu, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla
Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation. ICML (2023)
Ken Kansky, Skanda Vaidyanath, Scott Swingle, Xinghua Lou, Miguel Lázaro-Gredilla, Dileep George
PushWorld: A benchmark for manipulation planning with tools and movable obstacles. CoRR (2023)
Sivaramakrishnan Swaminathan, Antoine Dedieu, Rajkumar Vasudeva Raju, Murray Shanahan, Miguel Lázaro-Gredilla, Dileep George
Schema-learning and rebinding as mechanisms of in-context learning and emergence. NeurIPS (2023)
Nishad Gothoskar, Miguel Lázaro-Gredilla, Yasemin Bekiroglu, Abhishek Agarwal, Joshua B. Tenenbaum, Vikash K. Mansinghka, Dileep George
DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model. ICRA (2022)
Guangyao Zhou, Nishanth Kumar, Miguel Lázaro-Gredilla, Shrinu Kushagra, Dileep George
PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX. CoRR (2022)
Nishad Gothoskar, Miguel Lázaro-Gredilla, Yasemin Bekiroglu, Abhishek Agarwal, Joshua B. Tenenbaum, Vikash K. Mansinghka, Dileep George
DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model. CoRR (2022)
Antoine Dedieu, Miguel Lázaro-Gredilla, Dileep George
Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models. AAAI (2021)
Guangyao Zhou, Wolfgang Lehrach, Antoine Dedieu, Miguel Lázaro-Gredilla, Dileep George
Graphical Models with Attention for Context-Specific Independence and an Application to Perceptual Grouping. CoRR (2021)
Miguel Lázaro-Gredilla, Wolfgang Lehrach, Nishad Gothoskar, Guangyao Zhou, Antoine Dedieu, Dileep George
Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables. AAAI (2021)
Miguel Lázaro-Gredilla, Antoine Dedieu, Dileep George
Perturb-and-max-product: Sampling and learning in discrete energy-based models. NeurIPS (2021)
Miguel Lázaro-Gredilla, Antoine Dedieu, Dileep George
Perturb-and-max-product: Sampling and learning in discrete energy-based models. CoRR (2021)
Nishad Gothoskar, Miguel Lázaro-Gredilla, Abhishek Agarwal, Yasemin Bekiroglu, Dileep George
Learning a generative model for robot control using visual feedback. CoRR (2020)
Daniel P. Sawyer, Miguel Lázaro-Gredilla, Dileep George
A Model of Fast Concept Inference with Object-Factorized Cognitive Programs. CogSci (2020)
Nishad Gothoskar, Miguel Lázaro-Gredilla, Dileep George
From proprioception to long-horizon planning in novel environments: A hierarchical RL model. CoRR (2020)
Dileep George, Miguel Lázaro-Gredilla, J. Swaroop Guntupalli
From CAPTCHA to Commonsense: How Brain Can Teach Us About Artificial Intelligence. Frontiers Comput. Neurosci. 14 (2020)
Antoine Dedieu, Miguel Lázaro-Gredilla, Dileep George
Sample-efficient L0-L2 constrained structure learning of sparse Ising models. CoRR (2020)
Daniel P. Sawyer, Miguel Lázaro-Gredilla, Dileep George
A Model of Fast Concept Inference with Object-Factorized Cognitive Programs. CoRR (2020)
Miguel Lázaro-Gredilla, Wolfgang Lehrach, Nishad Gothoskar, Guangyao Zhou, Antoine Dedieu, Dileep George
Query Training: Learning and inference for directed and undirected graphical models. CoRR (2020)
Miguel Lázaro-Gredilla, Wolfgang Lehrach, Dileep George
Learning undirected models via query training. CoRR (2019)
Antoine Dedieu, Nishad Gothoskar, Scott Swingle, Wolfgang Lehrach, Miguel Lázaro-Gredilla, Dileep George
Learning higher-order sequential structure with cloned HMMs. CoRR (2019)
Dileep George, Alexander Lavin, J. Swaroop Guntupalli, David A. Mély, Nick Hay, Miguel Lázaro-Gredilla
Cortical Microcircuits from a Generative Vision Model. CoRR (2018)
Miguel Lázaro-Gredilla, Dianhuan Lin, J. Swaroop Guntupalli, Dileep George
Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs. CoRR (2018)
Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon
Variational Rejection Sampling. AISTATS (2018)
Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon
Variational Rejection Sampling. CoRR (2018)
Ken Kansky, Tom Silver, David A. Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, D. Scott Phoenix, Dileep George
Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics. ICML (2017)
Ken Kansky, Tom Silver, David A. Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, D. Scott Phoenix, Dileep George
Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics. CoRR (2017)
Luis Munoz-Gonzalez, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal
Laplace Approximation for Divisive Gaussian Processes for Nonstationary Regression. IEEE Trans. Pattern Anal. Mach. Intell. 38 (3) (2016)
Miguel Lázaro-Gredilla, Yi Liu, D. Scott Phoenix, Dileep George
Hierarchical compositional feature learning. CoRR (2016)
Jordi Muñoz-Marí, Jochem Verrelst, Miguel Lázaro-Gredilla, Gustau Camps-Valls
Biophysical parameter retrieval with warped Gaussian processes. IGARSS (2015)
Michalis K. Titsias, Miguel Lázaro-Gredilla
Local Expectation Gradients for Black Box Variational Inference. NIPS (2015)
Luis Munoz-Gonzalez, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal
Laplace approximation with Gaussian Processes for volatility forecasting. CIP (2014)
Luis Munoz-Gonzalez, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal
Divisive Gaussian Processes for Nonstationary Regression. IEEE Trans. Neural Networks Learn. Syst. 25 (11) (2014)
Miguel Lázaro-Gredilla, Michalis K. Titsias, Jochem Verrelst, Gustavo Camps-Valls
Retrieval of Biophysical Parameters With Heteroscedastic Gaussian Processes. IEEE Geosci. Remote. Sens. Lett. 11 (4) (2014)
Miguel Lázaro-Gredilla, Steven Van Vaerenbergh
A Gaussian Process Model for Data Association and a Semidefinite Programming Solution. IEEE Trans. Neural Networks Learn. Syst. 25 (11) (2014)
Michalis K. Titsias, Miguel Lázaro-Gredilla
Doubly Stochastic Variational Bayes for non-Conjugate Inference. ICML (2014)
J. Manco-Vasquez, Miguel Lázaro-Gredilla, David Ramírez, Javier Vía, Ignacio Santamaría
A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks. Signal Process. 96 (2014)
Miguel Lázaro-Gredilla, Michalis K. Titsias, Jochem Verrelst, Gustavo Camps-Valls
Estimation of vegetation chlorophyll content with Variational Heteroscedastic Gaussian Processes. IGARSS (2013)
Fernando Pérez-Cruz, Steven Van Vaerenbergh, Juan José Murillo-Fuentes, Miguel Lázaro-Gredilla, Ignacio Santamaría
Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances. IEEE Signal Process. Mag. 30 (4) (2013)
Michalis K. Titsias, Miguel Lázaro-Gredilla
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression. NIPS (2013)
Fernando Pérez-Cruz, Steven Van Vaerenbergh, Juan José Murillo-Fuentes, Miguel Lázaro-Gredilla, Ignacio Santamaría
Gaussian Processes for Nonlinear Signal Processing CoRR (2013)
Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil D. Lawrence
Overlapping Mixtures of Gaussian Processes for the data association problem. Pattern Recognit. 45 (4) (2012)
J. Manco-Vasquez, Miguel Lázaro-Gredilla, David Ramírez, Javier Vía, Ignacio Santamaría
Bayesian multiantenna sensing for cognitive radio. SAM (2012)
Miguel Lázaro-Gredilla, Vanessa Gómez-Verdejo, Emilio Parrado-Hernández
Low-cost model selection for SVMs using local features. Eng. Appl. Artif. Intell. 25 (6) (2012)
Steven Van Vaerenbergh, Miguel Lázaro-Gredilla, Ignacio Santamaría
Kernel Recursive Least-Squares Tracker for Time-Varying Regression. IEEE Trans. Neural Networks Learn. Syst. 23 (8) (2012)
Miguel Lázaro-Gredilla
Bayesian Warped Gaussian Processes. NIPS (2012)
Steven Van Vaerenbergh, Ignacio Santamaría, Miguel Lázaro-Gredilla
Estimation of the forgetting factor in kernel recursive least squares. MLSP (2012)
Vanessa Gómez-Verdejo, Manel Martínez-Ramón, Jerónimo Arenas-García, Miguel Lázaro-Gredilla, Harold Y. Molina-Bulla
Support Vector Machines With Constraints for Sparsity in the Primal Parameters. IEEE Trans. Neural Networks 22 (8) (2011)
Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Ignacio Santamaría
A Bayesian approach to tracking with kernel recursive least-squares. MLSP (2011)
Jerónimo Arenas-García, Miguel Lázaro-Gredilla
Tracking performance of adaptively biased adaptive filters. ICASSP (2011)
Miguel Lázaro-Gredilla, Michalis K. Titsias
Variational Heteroscedastic Gaussian Process Regression. ICML (2011)
Luis Antonio Azpicueta-Ruiz, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal, Jerónimo Arenas-García
A block-based approach to adaptively bias the weights of adaptive filters. MLSP (2011)
Michalis K. Titsias, Miguel Lázaro-Gredilla
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning. NIPS (2011)
Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil D. Lawrence
Overlapping Mixtures of Gaussian Processes for the Data Association Problem CoRR (2011)
Vanessa Gómez-Verdejo, Jerónimo Arenas-García, Miguel Lázaro-Gredilla, Ángel Navia-Vázquez
Adaptive One-Class Support Vector Machine. IEEE Trans. Signal Process. 59 (6) (2011)
Luis Munoz-Gonzalez, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal
Heteroscedastic Gaussian process regression using expectation propagation. MLSP (2011)
Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal
Marginalized neural network mixtures for large-scale regression. IEEE Trans. Neural Networks 21 (8) (2010)
Miguel Lázaro-Gredilla, Luis Antonio Azpicueta-Ruiz, Aníbal R. Figueiras-Vidal, Jerónimo Arenas-García
Adaptively biasing the weights of adaptive filters. IEEE Trans. Signal Process. 58 (7) (2010)
Miguel Lázaro-Gredilla, Joaquin Quiñonero Candela, Carl Edward Rasmussen, Aníbal R. Figueiras-Vidal
Sparse Spectrum Gaussian Process Regression. J. Mach. Learn. Res. 11 (2010)
Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features. NIPS (2009)
Jaisiel Madrid-Sánchez, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal
A Single Layer Perceptron Approach to Selective Multi-task Learning. IWINAC (1) (2007)
Miguel Lázaro-Gredilla, Jaisiel Madrid-Sánchez, Aníbal R. Figueiras-Vidal
A new cost function to build MLPs by means of regularized boosting. Computational Intelligence (2006)