382 citations · 978 across the 50 of their papers we have counts for
6 papers · 1 filter
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 Monocular Reactive UAV Control in Cluttered Natural Environments
Stephane Ross, Narek Melik-Barkhudarov, Kumar Shaurya Shankar +4
Autonomous navigation for large Unmanned Aerial Vehicles (UAVs) is fairly straight-forward, as expensive sensors and monitoring devices can be employed. In contrast, obstacle avoid…
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…
Predicting Contextual Sequences via Submodular Function Maximization
Debadeepta Dey, Tian Yu Liu, Martial Hebert +1
Sequence optimization, where the items in a list are ordered to maximize some reward has many applications such as web advertisement placement, search, and control libraries in rob…