A new cost function to build MLPs by means of regularized boosting. - Miguel Lázaro-Gredilla; Jaisiel Madrid-Sánchez; Aníbal R. Figueiras-Vidal (Computational Intelligence, 2006)

A Single Layer Perceptron Approach to Selective Multi-task Learning. - Jaisiel Madrid-Sánchez; Miguel Lázaro-Gredilla; Aníbal R. Figueiras-Vidal (IWINAC (1), 2007)

Inter-domain Gaussian Processes for Sparse Inference using Inducing Features. - Miguel Lázaro-Gredilla; Aníbal R. Figueiras-Vidal (NIPS, 2009)

Sparse Spectrum Gaussian Process Regression. - Miguel Lázaro-Gredilla; Joaquin Quiñonero Candela; Carl Edward Rasmussen; Aníbal R. Figueiras-Vidal (J. Mach. Learn. Res., 2010)

Marginalized neural network mixtures for large-scale regression. - Miguel Lázaro-Gredilla; Aníbal R. Figueiras-Vidal (IEEE Trans. Neural Networks, 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)

Tracking performance of adaptively biased adaptive filters. - Jerónimo Arenas-García; Miguel Lázaro-Gredilla (ICASSP, 2011)

Variational Heteroscedastic Gaussian Process Regression. - Miguel Lázaro-Gredilla; Michalis K. Titsias (ICML, 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 (MLSP, 2011)

A Bayesian approach to tracking with kernel recursive least-squares. - Miguel Lázaro-Gredilla; Steven Van Vaerenbergh; Ignacio Santamaría (MLSP, 2011)

Heteroscedastic Gaussian process regression using expectation propagation. - Luis Munoz-Gonzalez; Miguel Lázaro-Gredilla; Aníbal R. Figueiras-Vidal (MLSP, 2011)

Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning. - Michalis K. Titsias; Miguel Lázaro-Gredilla (NIPS, 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)

Adaptive One-Class Support Vector Machine. - Vanessa Gómez-Verdejo; Jerónimo Arenas-García; Miguel Lázaro-Gredilla; Ángel Navia-Vázquez (IEEE Trans. Signal Process., 2011)

Bayesian multiantenna sensing for cognitive radio. - Julio Manco-Vásquez; Miguel Lázaro-Gredilla; David Ramírez 001; Javier Vía; Ignacio Santamaría (SAM, 2012)

Estimation of the forgetting factor in kernel recursive least squares. - Steven Van Vaerenbergh; Ignacio Santamaría; Miguel Lázaro-Gredilla (MLSP, 2012)

Bayesian Warped Gaussian Processes. - Miguel Lázaro-Gredilla (NIPS, 2012)

Low-cost model selection for SVMs using local features. - Miguel Lázaro-Gredilla; Vanessa Gómez-Verdejo; Emilio Parrado-Hernández (Eng. Appl. Artif. Intell., 2012)

Overlapping Mixtures of Gaussian Processes for the data association problem. - Miguel Lázaro-Gredilla; Steven Van Vaerenbergh; Neil D. Lawrence (Pattern Recognit., 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)

Estimation of vegetation chlorophyll content with Variational Heteroscedastic Gaussian Processes. - Miguel Lázaro-Gredilla; Michalis K. Titsias; Jochem Verrelst; Gustavo Camps-Valls (IGARSS, 2013)

Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression. - Michalis K. Titsias; Miguel Lázaro-Gredilla (NIPS, 2013)

Gaussian Processes for Nonlinear Signal Processing - Fernando Pérez-Cruz; Steven Van Vaerenbergh; Juan José Murillo-Fuentes; Miguel Lázaro-Gredilla; Ignacio Santamaría (CoRR, 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)

Laplace approximation with Gaussian Processes for volatility forecasting. - Luis Munoz-Gonzalez; Miguel Lázaro-Gredilla; Aníbal R. Figueiras-Vidal (CIP, 2014)

Doubly Stochastic Variational Bayes for non-Conjugate Inference. - Michalis K. Titsias; Miguel Lázaro-Gredilla (ICML, 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)

A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks. - Julio Manco-Vásquez; Miguel Lázaro-Gredilla; David Ramírez 001; Javier Vía; Ignacio Santamaría (Signal Process., 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)

Divisive Gaussian Processes for Nonstationary Regression. - Luis Munoz-Gonzalez; Miguel Lázaro-Gredilla; Aníbal R. Figueiras-Vidal (IEEE Trans. Neural Networks Learn. Syst., 2014)

Biophysical parameter retrieval with warped Gaussian processes. - Jordi Muñoz-Marí; Jochem Verrelst; Miguel Lázaro-Gredilla; Gustau Camps-Valls (IGARSS, 2015)

Local Expectation Gradients for Black Box Variational Inference. - Michalis K. Titsias; Miguel Lázaro-Gredilla (NIPS, 2015)

Hierarchical compositional feature learning. - Miguel Lázaro-Gredilla; Yi Liu 055; D. Scott Phoenix; Dileep George (CoRR, 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)

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 (ICML, 2017)

Variational Rejection Sampling. - Aditya Grover; Ramki Gummadi; Miguel Lázaro-Gredilla; Dale Schuurmans; Stefano Ermon (AISTATS, 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)

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)

Learning higher-order sequential structure with cloned HMMs. - Antoine Dedieu; Nishad Gothoskar; Scott Swingle; Wolfgang Lehrach; Miguel Lázaro-Gredilla; Dileep George (CoRR, 2019)

Learning undirected models via query training. - Miguel Lázaro-Gredilla; Wolfgang Lehrach; Dileep George (CoRR, 2019)

A Model of Fast Concept Inference with Object-Factorized Cognitive Programs. - Daniel P. Sawyer; Miguel Lázaro-Gredilla; Dileep George (CogSci, 2020)

Learning a generative model for robot control using visual feedback. - Nishad Gothoskar; Miguel Lázaro-Gredilla; Abhishek Agarwal; Yasemin Bekiroglu; 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)

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 CAPTCHA to Commonsense: How Brain Can Teach Us About Artificial Intelligence. - Dileep George; Miguel Lázaro-Gredilla; J. Swaroop Guntupalli (Frontiers Comput. Neurosci., 2020)

Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models. - Antoine Dedieu; Miguel Lázaro-Gredilla; 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)

Perturb-and-max-product: Sampling and learning in discrete energy-based models. - Miguel Lázaro-Gredilla; Antoine Dedieu; Dileep George (NeurIPS, 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)

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 (ICRA, 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)

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 (ICCV, 2023)

Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation. - Antoine Dedieu; Guangyao Zhou; Dileep George; Miguel Lázaro-Gredilla (ICML, 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 (NeurIPS, 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)

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)

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)

Fast exploration and learning of latent graphs with aliased observations. - Miguel Lázaro-Gredilla; Ishan Deshpande; Sivaramakrishnan Swaminathan; Meet Dave; Dileep George (CoRR, 2023)

Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments. - Antoine Dedieu; Wolfgang Lehrach; Guangyao Zhou; Dileep George; Miguel Lázaro-Gredilla (ICML, 2024)

What type of inference is planning? - Miguel Lázaro-Gredilla; Li Yang Ku; Kevin P. Murphy; Dileep George (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; Sivaramakrishnan Swaminathan; Guangyao Zhou; Miguel Lázaro-Gredilla; Kevin P. Murphy (NeurIPS, 2024)

Model Predictive Simulation Using Structured Graphical Models and Transformers. - Xinghua Lou; Meet Dave; Shrinu Kushagra; Miguel Lázaro-Gredilla; Kevin Murphy (CoRR, 2024)

Diffusion Model Predictive Control. - Guangyao Zhou; Sivaramakrishnan Swaminathan; Rajkumar Vasudeva Raju; J. Swaroop Guntupalli; Wolfgang Lehrach; Joseph Ortiz; Antoine Dedieu; Miguel Lázaro-Gredilla; Kevin Patrick Murphy (Trans. Mach. Learn. Res., 2025)

Improving Transformer World Models for Data-Efficient RL. - Antoine Dedieu; Joseph Ortiz; Xinghua Lou; Carter Wendelken; Wolfgang Lehrach; J. Swaroop Guntupalli; Miguel Lázaro-Gredilla; Kevin Patrick Murphy (CoRR, 2025)