Miguel Lázaro-Gredilla

According to our database, Miguel Lázaro-Gredilla authored at least 59 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments.

Antoine Dedieu, Wolfgang Lehrach, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla

CoRR, 2024

2023

Fast exploration and learning of latent graphs with aliased observations.

Miguel Lázaro-Gredilla, Ishan Deshpande, Sivaramakrishnan Swaminathan, Meet Dave, Dileep George

CoRR, 2023

Graph schemas as abstractions for transfer learning, inference, and planning.

J. Swaroop Guntupalli, Rajkumar Vasudeva Raju, Shrinu Kushagra, Carter Wendelken, Danny Sawyer, Ishan Deshpande, Guangyao Zhou, Miguel Lázaro-Gredilla, Dileep George

CoRR, 2023

3D Neural Embedding Likelihood for Robust Sim-to-Real Transfer in Inverse Graphics.

Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B. Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, Vikash K. Mansinghka

CoRR, 2023

PushWorld: A benchmark for manipulation planning with tools and movable obstacles.

Ken Kansky, Skanda Vaidyanath, Scott Swingle, Xinghua Lou, Miguel Lázaro-Gredilla, Dileep George

CoRR, 2023

Schema-learning and rebinding as mechanisms of in-context learning and emergence.

Sivaramakrishnan Swaminathan, Antoine Dedieu, Rajkumar Vasudeva Raju, Murray Shanahan, Miguel Lázaro-Gredilla, Dileep George

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation.

Antoine Dedieu, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX.

Guangyao Zhou, Nishanth Kumar, Miguel Lázaro-Gredilla, Shrinu Kushagra, Dileep George

CoRR, 2022

DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model.

Nishad Gothoskar, Miguel Lázaro-Gredilla, Yasemin Bekiroglu, Abhishek Agarwal, Joshua B. Tenenbaum, Vikash K. Mansinghka, Dileep George

Proceedings of the 2022 International Conference on Robotics and Automation, 2022

2021

Graphical Models with Attention for Context-Specific Independence and an Application to Perceptual Grouping.

Guangyao Zhou, Wolfgang Lehrach, Antoine Dedieu, Miguel Lázaro-Gredilla, Dileep George

CoRR, 2021

Perturb-and-max-product: Sampling and learning in discrete energy-based models.

Miguel Lázaro-Gredilla, Antoine Dedieu, Dileep George

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models.

Antoine Dedieu, Miguel Lázaro-Gredilla, Dileep George

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

From CAPTCHA to Commonsense: How Brain Can Teach Us About Artificial Intelligence.

Dileep George, Miguel Lázaro-Gredilla, J. Swaroop Guntupalli

Frontiers Comput. Neurosci., 2020

Query Training: Learning and inference for directed and undirected graphical models.

Miguel Lázaro-Gredilla, Wolfgang Lehrach, Nishad Gothoskar, Guangyao Zhou, Antoine Dedieu, Dileep George

CoRR, 2020

From proprioception to long-horizon planning in novel environments: A hierarchical RL model.

Nishad Gothoskar, Miguel Lázaro-Gredilla, Dileep George

CoRR, 2020

Learning a generative model for robot control using visual feedback.

Nishad Gothoskar, Miguel Lázaro-Gredilla, Abhishek Agarwal, Yasemin Bekiroglu, Dileep George

CoRR, 2020

A Model of Fast Concept Inference with Object-Factorized Cognitive Programs.

Daniel P. Sawyer, Miguel Lázaro-Gredilla, Dileep George

Proceedings of the 42th Annual Meeting of the Cognitive Science Society, 2020

2019

Learning undirected models via query training.

Miguel Lázaro-Gredilla, Wolfgang Lehrach, Dileep George

CoRR, 2019

Learning higher-order sequential structure with cloned HMMs.

Antoine Dedieu, Nishad Gothoskar, Scott Swingle, Wolfgang Lehrach, Miguel Lázaro-Gredilla, Dileep George

CoRR, 2019

2018

Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs.

Miguel Lázaro-Gredilla, Dianhuan Lin, J. Swaroop Guntupalli, Dileep George

CoRR, 2018

Cortical Microcircuits from a Generative Vision Model.

Dileep George, Alexander Lavin, J. Swaroop Guntupalli, David A. Mély, Nick Hay, Miguel Lázaro-Gredilla

CoRR, 2018

Variational Rejection Sampling.

Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

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, D. Scott Phoenix, Dileep George

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Laplace Approximation for Divisive Gaussian Processes for Nonstationary Regression.

Luis Munoz-Gonzalez, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal

IEEE Trans. Pattern Anal. Mach. Intell., 2016

Hierarchical compositional feature learning.

Miguel Lázaro-Gredilla, Yi Liu, D. Scott Phoenix, Dileep George

CoRR, 2016

2015

Local Expectation Gradients for Black Box Variational Inference.

Michalis K. Titsias, Miguel Lázaro-Gredilla

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Biophysical parameter retrieval with warped Gaussian processes.

Jordi Muñoz-Marí, Jochem Verrelst, Miguel Lázaro-Gredilla, Gustau Camps-Valls

Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium, 2015

2014

Divisive Gaussian Processes for Nonstationary Regression.

Luis Munoz-Gonzalez, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal

IEEE Trans. Neural Networks Learn. Syst., 2014

A Gaussian Process Model for Data Association and a Semidefinite Programming Solution.

Miguel Lázaro-Gredilla, Steven Van Vaerenbergh

IEEE Trans. Neural Networks Learn. Syst., 2014

A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks.

Julio Manco-Vásquez, Miguel Lázaro-Gredilla, David Ramírez, Javier Vía, Ignacio Santamaría

Signal Process., 2014

Retrieval of Biophysical Parameters With Heteroscedastic Gaussian Processes.

Miguel Lázaro-Gredilla, Michalis K. Titsias, Jochem Verrelst, Gustavo Camps-Valls

IEEE Geosci. Remote. Sens. Lett., 2014

Doubly Stochastic Variational Bayes for non-Conjugate Inference.

Michalis K. Titsias, Miguel Lázaro-Gredilla

Proceedings of the 31th International Conference on Machine Learning, 2014

Laplace approximation with Gaussian Processes for volatility forecasting.

Luis Munoz-Gonzalez, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal

Proceedings of the 4th International Workshop on Cognitive Information Processing, 2014

2013

Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances.

Fernando Pérez-Cruz, Steven Van Vaerenbergh, Juan José Murillo-Fuentes, Miguel Lázaro-Gredilla, Ignacio Santamaría

IEEE Signal Process. Mag., 2013

Gaussian Processes for Nonlinear Signal Processing

Fernando Pérez-Cruz, Steven Van Vaerenbergh, Juan José Murillo-Fuentes, Miguel Lázaro-Gredilla, Ignacio Santamaría

CoRR, 2013

Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression.

Michalis K. Titsias, Miguel Lázaro-Gredilla

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Estimation of vegetation chlorophyll content with Variational Heteroscedastic Gaussian Processes.

Miguel Lázaro-Gredilla, Michalis K. Titsias, Jochem Verrelst, Gustavo Camps-Valls

Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium, 2013

2012

Kernel Recursive Least-Squares Tracker for Time-Varying Regression.

Steven Van Vaerenbergh, Miguel Lázaro-Gredilla, Ignacio Santamaría

IEEE Trans. Neural Networks Learn. Syst., 2012

Overlapping Mixtures of Gaussian Processes for the data association problem.

Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil D. Lawrence

Pattern Recognit., 2012

Low-cost model selection for SVMs using local features.

Miguel Lázaro-Gredilla, Vanessa Gómez-Verdejo, Emilio Parrado-Hernández

Eng. Appl. Artif. Intell., 2012

Bayesian Warped Gaussian Processes.

Miguel Lázaro-Gredilla

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Estimation of the forgetting factor in kernel recursive least squares.

Steven Van Vaerenbergh, Ignacio Santamaría, Miguel Lázaro-Gredilla

Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2012

Bayesian multiantenna sensing for cognitive radio.

Julio Manco-Vásquez, Miguel Lázaro-Gredilla, David Ramírez, Javier Vía, Ignacio Santamaría

Proceedings of the IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, 2012

2011

Adaptive One-Class Support Vector Machine.

Vanessa Gómez-Verdejo, Jerónimo Arenas-García, Miguel Lázaro-Gredilla, Ángel Navia-Vázquez

IEEE Trans. Signal Process., 2011

Support Vector Machines With Constraints for Sparsity in the Primal Parameters.

Vanessa Gómez-Verdejo, Manel Martínez-Ramón, Jerónimo Arenas-García, Miguel Lázaro-Gredilla, Harold Y. Molina-Bulla

IEEE Trans. Neural Networks, 2011

Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning.

Michalis K. Titsias, Miguel Lázaro-Gredilla

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Heteroscedastic Gaussian process regression using expectation propagation.

Luis Munoz-Gonzalez, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal

Proceedings of the 2011 IEEE International Workshop on Machine Learning for Signal Processing, 2011

A Bayesian approach to tracking with kernel recursive least-squares.

Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Ignacio Santamaría

Proceedings of the 2011 IEEE International Workshop on Machine Learning for Signal Processing, 2011

A block-based approach to adaptively bias the weights of adaptive filters.

Luis Antonio Azpicueta-Ruiz, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal, Jerónimo Arenas-García

Proceedings of the 2011 IEEE International Workshop on Machine Learning for Signal Processing, 2011

Variational Heteroscedastic Gaussian Process Regression.

Miguel Lázaro-Gredilla, Michalis K. Titsias

Proceedings of the 28th International Conference on Machine Learning, 2011

Tracking performance of adaptively biased adaptive filters.

Jerónimo Arenas-García, Miguel Lázaro-Gredilla

Proceedings of the IEEE International Conference on Acoustics, 2011

2010

Adaptively biasing the weights of adaptive filters.

Miguel Lázaro-Gredilla, Luis Antonio Azpicueta-Ruiz, Aníbal R. Figueiras-Vidal, Jerónimo Arenas-García

IEEE Trans. Signal Process., 2010

Marginalized neural network mixtures for large-scale regression.

Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal

IEEE Trans. Neural Networks, 2010

Sparse Spectrum Gaussian Process Regression.

Miguel Lázaro-Gredilla, Joaquin Quiñonero Candela, Carl Edward Rasmussen, Aníbal R. Figueiras-Vidal

J. Mach. Learn. Res., 2010

2009

Inter-domain Gaussian Processes for Sparse Inference using Inducing Features.

Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2007

A Single Layer Perceptron Approach to Selective Multi-task Learning.

Jaisiel Madrid-Sánchez, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal

Proceedings of the Bio-inspired Modeling of Cognitive Tasks, 2007

2006

A new cost function to build MLPs by means of regularized boosting.

Miguel Lázaro-Gredilla, Jaisiel Madrid-Sánchez, Aníbal R. Figueiras-Vidal

Proceedings of the Second IASTED International Conference on Computational Intelligence, 2006