Search Results for author: Miguel Lazaro-Gredilla

Found 8 papers, 2 papers with code

Fast exploration and learning of latent graphs with aliased observations

no code implementations13 Mar 2023 Miguel Lazaro-Gredilla, Ishan Deshpande, Sivaramakrishnan Swaminathan, Meet Dave, Dileep George

We consider the problem of recovering a latent graph where the observations at each node are \emph{aliased}, and transitions are stochastic.

Efficient Exploration

Learning noisy-OR Bayesian Networks with Max-Product Belief Propagation

no code implementations31 Jan 2023 Antoine Dedieu, Guangyao Zhou, Dileep George, Miguel Lazaro-Gredilla

We evaluate both approaches on several benchmarks where VI is the state-of-the-art and show that our method (a) achieves better test performance than Ji et al. (2020) for learning noisy-OR BNs with hierarchical latent structures on large sparse real datasets; (b) recovers a higher number of ground truth parameters than Buhai et al. (2020) from cluttered synthetic scenes; and (c) solves the 2D blind deconvolution problem from Lazaro-Gredilla et al. (2021) and variant - including binary matrix factorization - while VI catastrophically fails and is up to two orders of magnitude slower.

Variational Inference

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

1 code implementation NeurIPS 2021 Miguel Lazaro-Gredilla, Antoine Dedieu, Dileep George

Perturb-and-MAP offers an elegant approach to approximately sample from a energy-based model (EBM) by computing the maximum-a-posteriori (MAP) configuration of a perturbed version of the model.

Learning undirected models via query training

no code implementations pproximateinference AABI Symposium 2019 Miguel Lazaro-Gredilla, Wolfgang Lehrach, Dileep George

We show that our approach generalizes to unseen probabilistic queries on also unseen test data, providing fast and flexible inference.

Cortical Microcircuits from a Generative Vision Model

no code implementations3 Aug 2018 Dileep George, Alexander Lavin, J. Swaroop Guntupalli, David Mely, Nick Hay, Miguel Lazaro-Gredilla

Understanding the information processing roles of cortical circuits is an outstanding problem in neuroscience and artificial intelligence.

Bayesian Inference

Variational Rejection Sampling

no code implementations5 Apr 2018 Aditya Grover, Ramki Gummadi, Miguel Lazaro-Gredilla, Dale Schuurmans, Stefano Ermon

Learning latent variable models with stochastic variational inference is challenging when the approximate posterior is far from the true posterior, due to high variance in the gradient estimates.

Variational Inference

Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression

no code implementations NeurIPS 2013 Michalis Titsias Rc Aueb, Miguel Lazaro-Gredilla

We introduce a novel variational method that allows to approximately integrate out kernel hyperparameters, such as length-scales, in Gaussian process regression.

regression Variational Inference

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