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2020 – today
- 2024
[c29]Antoine Dedieu, Wolfgang Lehrach, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla:
Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments. ICML 2024- [i29]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 abs/2401.05946 (2024) - [i28]Miguel Lázaro-Gredilla, Li Yang Ku, Kevin P. Murphy, Dileep George:
What type of inference is planning? CoRR abs/2406.17863 (2024) - [i27]Xinghua Lou, Meet Dave, Shrinu Kushagra, Miguel Lázaro-Gredilla, Kevin Murphy:
Model Predictive Simulation Using Structured Graphical Models and Transformers. CoRR abs/2406.19635 (2024) - [i26]Joseph Ortiz, Antoine Dedieu, Wolfgang Lehrach, J. Swaroop Guntupalli, Carter Wendelken, Ahmad Humayun, Guangyao Zhou, Sivaramakrishnan Swaminathan, Miguel Lázaro-Gredilla, Kevin Murphy:
DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors. CoRR abs/2409.18330 (2024) - [i25]Guangyao Zhou, Sivaramakrishnan Swaminathan, Rajkumar Vasudeva Raju, J. Swaroop Guntupalli, Wolfgang Lehrach, Joseph Ortiz, Antoine Dedieu, Miguel Lázaro-Gredilla, Kevin Murphy:
Diffusion Model Predictive Control. CoRR abs/2410.05364 (2024) - 2023
- [c28]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: 21568-21579 - [c27]Antoine Dedieu, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla:
Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation. ICML 2023: 7426-7448 - [c26]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 - [i24]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 abs/2301.10289 (2023) - [i23]Antoine Dedieu, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla:
Learning noisy-OR Bayesian Networks with Max-Product Belief Propagation. CoRR abs/2302.00099 (2023) - [i22]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 abs/2302.03744 (2023) - [i21]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 abs/2302.07350 (2023) - [i20]Miguel Lázaro-Gredilla, Ishan Deshpande, Sivaramakrishnan Swaminathan, Meet Dave, Dileep George:
Fast exploration and learning of latent graphs with aliased observations. CoRR abs/2303.07397 (2023) - [i19]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 abs/2307.01201 (2023) - 2022
- [c25]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: 6674-6680 - [i18]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 abs/2202.03697 (2022) - [i17]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 abs/2202.04110 (2022) - 2021
- [c24]Antoine Dedieu, Miguel Lázaro-Gredilla, Dileep George:
Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models. AAAI 2021: 7193-7200 - [c23]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: 8252-8260 - [c22]Miguel Lázaro-Gredilla, Antoine Dedieu, Dileep George:
Perturb-and-max-product: Sampling and learning in discrete energy-based models. NeurIPS 2021: 928-940 - [i16]Miguel Lázaro-Gredilla, Antoine Dedieu, Dileep George:
Perturb-and-max-product: Sampling and learning in discrete energy-based models. CoRR abs/2111.02458 (2021) - [i15]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 abs/2112.03371 (2021) - 2020
- [j15]Dileep George, Miguel Lázaro-Gredilla, J. Swaroop Guntupalli:
From CAPTCHA to Commonsense: How Brain Can Teach Us About Artificial Intelligence. Frontiers Comput. Neurosci. 14: 554097 (2020) - [c21]Daniel P. Sawyer, Miguel Lázaro-Gredilla, Dileep George:
A Model of Fast Concept Inference with Object-Factorized Cognitive Programs. CogSci 2020 - [i14]Daniel P. Sawyer, Miguel Lázaro-Gredilla, Dileep George:
A Model of Fast Concept Inference with Object-Factorized Cognitive Programs. CoRR abs/2002.04021 (2020) - [i13]Nishad Gothoskar, Miguel Lázaro-Gredilla, Abhishek Agarwal, Yasemin Bekiroglu, Dileep George:
Learning a generative model for robot control using visual feedback. CoRR abs/2003.04474 (2020) - [i12]Nishad Gothoskar, Miguel Lázaro-Gredilla, Dileep George:
From proprioception to long-horizon planning in novel environments: A hierarchical RL model. CoRR abs/2006.06620 (2020) - [i11]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 abs/2006.06803 (2020) - [i10]Antoine Dedieu, Miguel Lázaro-Gredilla, Dileep George:
Sample-efficient L0-L2 constrained structure learning of sparse Ising models. CoRR abs/2012.01744 (2020)
2010 – 2019
- 2019
- [i9]Antoine Dedieu, Nishad Gothoskar, Scott Swingle, Wolfgang Lehrach, Miguel Lázaro-Gredilla, Dileep George:
Learning higher-order sequential structure with cloned HMMs. CoRR abs/1905.00507 (2019) - [i8]Miguel Lázaro-Gredilla, Wolfgang Lehrach, Dileep George:
Learning undirected models via query training. CoRR abs/1912.02893 (2019) - 2018
- [c20]Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon:
Variational Rejection Sampling. AISTATS 2018: 823-832 - [i7]Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon:
Variational Rejection Sampling. CoRR abs/1804.01712 (2018) - [i6]Dileep George, Alexander Lavin, J. Swaroop Guntupalli, David A. Mély, Nick Hay, Miguel Lázaro-Gredilla:
Cortical Microcircuits from a Generative Vision Model. CoRR abs/1808.01058 (2018) - [i5]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 abs/1812.02788 (2018) - 2017
- [c19]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: 1809-1818 - [i4]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 abs/1706.04317 (2017) - 2016
- [j14]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): 618-624 (2016) - [i3]Miguel Lázaro-Gredilla, Yi Liu, D. Scott Phoenix, Dileep George:
Hierarchical compositional feature learning. CoRR abs/1611.02252 (2016) - 2015
- [c18]Jordi Muñoz-Marí, Jochem Verrelst, Miguel Lázaro-Gredilla, Gustau Camps-Valls:
Biophysical parameter retrieval with warped Gaussian processes. IGARSS 2015: 13-16 - [c17]Michalis K. Titsias, Miguel Lázaro-Gredilla:
Local Expectation Gradients for Black Box Variational Inference. NIPS 2015: 2638-2646 - 2014
- [j13]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): 838-842 (2014) - [j12]Julio Manco-Vásquez, Miguel Lázaro-Gredilla, David Ramírez, Javier Vía, Ignacio Santamaría:
A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks. Signal Process. 96: 228-240 (2014) - [j11]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): 1967-1979 (2014) - [j10]Luis Munoz-Gonzalez, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal:
Divisive Gaussian Processes for Nonstationary Regression. IEEE Trans. Neural Networks Learn. Syst. 25(11): 1991-2003 (2014) - [c16]Luis Munoz-Gonzalez, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal:
Laplace approximation with Gaussian Processes for volatility forecasting. CIP 2014: 1-6 - [c15]Michalis K. Titsias, Miguel Lázaro-Gredilla:
Doubly Stochastic Variational Bayes for non-Conjugate Inference. ICML 2014: 1971-1979 - 2013
- [j9]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): 40-50 (2013) - [c14]Miguel Lázaro-Gredilla, Michalis K. Titsias, Jochem Verrelst, Gustavo Camps-Valls:
Estimation of vegetation chlorophyll content with Variational Heteroscedastic Gaussian Processes. IGARSS 2013: 3010-3013 - [c13]Michalis K. Titsias, Miguel Lázaro-Gredilla:
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression. NIPS 2013: 279-287 - [i2]Fernando Pérez-Cruz, Steven Van Vaerenbergh, Juan José Murillo-Fuentes, Miguel Lázaro-Gredilla, Ignacio Santamaría:
Gaussian Processes for Nonlinear Signal Processing. CoRR abs/1303.2823 (2013) - 2012
- [j8]Miguel Lázaro-Gredilla, Vanessa Gómez-Verdejo, Emilio Parrado-Hernández:
Low-cost model selection for SVMs using local features. Eng. Appl. Artif. Intell. 25(6): 1203-1211 (2012) - [j7]Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil D. Lawrence:
Overlapping Mixtures of Gaussian Processes for the data association problem. Pattern Recognit. 45(4): 1386-1395 (2012) - [j6]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): 1313-1326 (2012) - [c12]Julio Manco-Vásquez, Miguel Lázaro-Gredilla, David Ramírez, Javier Vía, Ignacio Santamaría:
Bayesian multiantenna sensing for cognitive radio. SAM 2012: 77-80 - [c11]Steven Van Vaerenbergh, Ignacio Santamaría, Miguel Lázaro-Gredilla:
Estimation of the forgetting factor in kernel recursive least squares. MLSP 2012: 1-6 - [c10]Miguel Lázaro-Gredilla:
Bayesian Warped Gaussian Processes. NIPS 2012: 1628-1636 - 2011
- [j5]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): 1269-1283 (2011) - [j4]Vanessa Gómez-Verdejo, Jerónimo Arenas-García, Miguel Lázaro-Gredilla, Ángel Navia-Vázquez:
Adaptive One-Class Support Vector Machine. IEEE Trans. Signal Process. 59(6): 2975-2981 (2011) - [c9]Jerónimo Arenas-García, Miguel Lázaro-Gredilla:
Tracking performance of adaptively biased adaptive filters. ICASSP 2011: 4128-4131 - [c8]Miguel Lázaro-Gredilla, Michalis K. Titsias:
Variational Heteroscedastic Gaussian Process Regression. ICML 2011: 841-848 - [c7]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: 1-6 - [c6]Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Ignacio Santamaría:
A Bayesian approach to tracking with kernel recursive least-squares. MLSP 2011: 1-6 - [c5]Luis Munoz-Gonzalez, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal:
Heteroscedastic Gaussian process regression using expectation propagation. MLSP 2011: 1-6 - [c4]Michalis K. Titsias, Miguel Lázaro-Gredilla:
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning. NIPS 2011: 2339-2347 - [i1]Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil D. Lawrence:
Overlapping Mixtures of Gaussian Processes for the Data Association Problem. CoRR abs/1108.3372 (2011) - 2010
- [j3]Miguel Lázaro-Gredilla, Joaquin Quiñonero Candela, Carl Edward Rasmussen, Aníbal R. Figueiras-Vidal:
Sparse Spectrum Gaussian Process Regression. J. Mach. Learn. Res. 11: 1865-1881 (2010) - [j2]Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal:
Marginalized neural network mixtures for large-scale regression. IEEE Trans. Neural Networks 21(8): 1345-1351 (2010) - [j1]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): 3890-3895 (2010)
2000 – 2009
- 2009
- [c3]Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal:
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features. NIPS 2009: 1087-1095 - 2007
- [c2]Jaisiel Madrid-Sánchez, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal:
A Single Layer Perceptron Approach to Selective Multi-task Learning. IWINAC (1) 2007: 272-281 - 2006
- [c1]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: 162-167
Coauthor Index
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