382 citations · 978 across the 50 of their papers we have counts for
4 papers · 2 filters
Learning with Scope, with Application to Information Extraction and Classification
David Blei, J Andrew Bagnell, Andrew McCallum
In probabilistic approaches to classification and information extraction, one typically builds a statistical model of words under the assumption that future data will exhibit the s…
Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification
Brian D. Ziebart, Anind K. Dey, J Andrew Bagnell
Dealing with uncertainty in Bayesian Network structures using maximum a posteriori (MAP) estimation or Bayesian Model Averaging (BMA) is often intractable due to the superexponenti…
Convex Coding
David M. Bradley, J Andrew Bagnell
Inspired by recent work on convex formulations of clustering (Lashkari & Golland, 2008; Nowozin & Bakir, 2008) we investigate a new formulation of the Sparse Coding Problem (Olshau…
Agnostic System Identification for Model-Based Reinforcement Learning
Stephane Ross, J. Andrew Bagnell
A fundamental problem in control is to learn a model of a system from observations that is useful for controller synthesis. To provide good performance guarantees, existing methods…