6 citations · 7 across the 3 of their papers we have counts for
6 papers · 1 filter
Perturb-and-max-product: Sampling and learning in discrete energy-based models
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…
Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models
Antoine Dedieu, Miguel Lázaro-Gredilla, Dileep George
We consider the problem of learning the underlying graph of a sparse Ising model with nodes from i.i.d. samples. The most recent and best performing approaches combine an e…
Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables
Miguel Lázaro-Gredilla, Wolfgang Lehrach, Nishad Gothoskar +3
Probabilistic graphical models (PGMs) provide a compact representation of knowledge that can be queried in a flexible way: after learning the parameters of a graphical model once,…
An error bound for Lasso and Group Lasso in high dimensions
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. For Lasso, our bounds scale as $(k…
Learning higher-order sequential structure with cloned HMMs
Antoine Dedieu, Nishad Gothoskar, Scott Swingle +3
Variable order sequence modeling is an important problem in artificial and natural intelligence. While overcomplete Hidden Markov Models (HMMs), in theory, have the capacity to rep…
Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streaming
Antoine Dedieu, Rahul Mazumder, Zhen Zhu +1
An important metric of users' satisfaction and engagement within on-line streaming services is the user session length, i.e. the amount of time they spend on a service continuously…