Search Results for author: Antoine Dedieu

Found 14 papers, 8 papers with code

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

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

2 code implementations8 Feb 2022 Guangyao Zhou, Antoine Dedieu, Nishanth Kumar, Wolfgang Lehrach, Miguel Lázaro-Gredilla, Shrinu Kushagra, Dileep George

PGMax is an open-source Python package for (a) easily specifying discrete Probabilistic Graphical Models (PGMs) as factor graphs; and (b) automatically running efficient and scalable loopy belief propagation (LBP) in JAX.

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

1 code implementation6 Dec 2021 Guangyao Zhou, Wolfgang Lehrach, Antoine Dedieu, Miguel Lázaro-Gredilla, Dileep George

To demonstrate MAM's capabilities to capture CSIs at scale, we apply MAMs to capture an important type of CSI that is present in a symbolic approach to recurrent computations in perceptual grouping.

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.

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

1 code implementation3 Dec 2020 Antoine Dedieu, Miguel Lázaro-Gredilla, Dileep George

We consider the problem of learning the underlying graph of a sparse Ising model with $p$ nodes from $n$ i. i. d.

Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables

1 code implementation11 Jun 2020 Miguel Lázaro-Gredilla, Wolfgang Lehrach, Nishad Gothoskar, Guangyao Zhou, Antoine Dedieu, Dileep George

Here we introduce query training (QT), a mechanism to learn a PGM that is optimized for the approximate inference algorithm that will be paired with it.

Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives

1 code implementation17 Jan 2020 Antoine Dedieu, Hussein Hazimeh, Rahul Mazumder

We aim to bridge this gap in computation times by developing new MIP-based algorithms for $\ell_0$-regularized classification.

Variable Selection

An error bound for Lasso and Group Lasso in high dimensions

no code implementations21 Dec 2019 Antoine Dedieu

We leverage recent advances in high-dimensional statistics to derive new L2 estimation upper bounds for Lasso and Group Lasso in high-dimensions.

Vocal Bursts Intensity Prediction

Improved error rates for sparse (group) learning with Lipschitz loss functions

no code implementations20 Oct 2019 Antoine Dedieu

For L1 and Slope regularizations, our bounds scale as $(k^*/n) \log(p/k^*)$ -- $n\times p$ is the size of the design matrix and $k^*$ the dimension of the theoretical loss minimizer $\B{\beta}^*$ -- and match the optimal minimax rate achieved for the least-squares case.

L2 Regularization regression

Learning higher-order sequential structure with cloned HMMs

no code implementations1 May 2019 Antoine Dedieu, Nishad Gothoskar, Scott Swingle, Wolfgang Lehrach, Miguel Lázaro-Gredilla, Dileep George

We show that by constraining HMMs with a simple sparsity structure inspired by biology, we can make it learn variable order sequences efficiently.

Community Detection Language Modelling

Error bounds for sparse classifiers in high-dimensions

no code implementations7 Oct 2018 Antoine Dedieu

We prove an L2 recovery bound for a family of sparse estimators defined as minimizers of some empirical loss functions -- which include hinge loss and logistic loss.

Statistics Theory Statistics Theory

Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streaming

no code implementations4 Mar 2018 Antoine Dedieu, Rahul Mazumder, Zhen Zhu, Hossein Vahabi

In this work we present a novel framework inspired by hierarchical Bayesian modeling to predict, at the moment of login, the amount of time a user will spend in the streaming service.

Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is low

1 code implementation10 Aug 2017 Rahul Mazumder, Peter Radchenko, Antoine Dedieu

We conduct an extensive theoretical analysis of the predictive properties of the proposed approach and provide justification for its superior predictive performance relative to best subset selection when the noise-level is high.

regression Sparse Learning

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