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8 papers · 2 filters
Learning efficient sparse and low rank models
Pablo Sprechmann, Alex M. Bronstein, Guillermo Sapiro
Parsimony, including sparsity and low rank, has been shown to successfully model data in numerous machine learning and signal processing tasks. Traditionally, such modeling approac…
Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs
Gavin Taylor, Ron Parr
Recently, Petrik et al. demonstrated that L1Regularized Approximate Linear Programming (RALP) could produce value functions and policies which compared favorably to established lin…
Nested Dictionary Learning for Hierarchical Organization of Imagery and Text
Lingbo Li, XianXing Zhang, Mingyuan Zhou +1
A tree-based dictionary learning model is developed for joint analysis of imagery and associated text. The dictionary learning may be applied directly to the imagery from patches,…
A Bayesian Nonparametric Approach to Image Super-resolution
Gungor Polatkan, Mingyuan Zhou, Lawrence Carin +2
Super-resolution methods form high-resolution images from low-resolution images. In this paper, we develop a new Bayesian nonparametric model for super-resolution. Our method uses…
Greedy Algorithms for Sparse Reinforcement Learning
Christopher Painter-Wakefield, Ronald Parr
Feature selection and regularization are becoming increasingly prominent tools in the efforts of the reinforcement learning (RL) community to expand the reach and applicability of…
Inferring Latent Structure From Mixed Real and Categorical Relational Data
Esther Salazar, Matthew Cain, Elise Darling +2
We consider analysis of relational data (a matrix), in which the rows correspond to subjects (e.g., people) and the columns correspond to attributes. The elements of the matrix may…