382 citations · 978 across the 50 of their papers we have counts for
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cs.LG2011★ 11 cited
Stability Conditions for Online Learnability
Stephane Ross, J. Andrew Bagnell
Stability is a general notion that quantifies the sensitivity of a learning algorithm's output to small change in the training dataset (e.g. deletion or replacement of a single tra…
cs.LG2011★ 21 cited
Generalized Boosting Algorithms for Convex Optimization
Alexander Grubb, J. Andrew Bagnell
Boosting is a popular way to derive powerful learners from simpler hypothesis classes. Following previous work (Mason et al., 1999; Friedman, 2000) on general boosting frameworks,…
cs.GT2011
Computational Rationalization: The Inverse Equilibrium Problem
Kevin Waugh, Brian D. Ziebart, J. Andrew Bagnell
Modeling the purposeful behavior of imperfect agents from a small number of observations is a challenging task. When restricted to the single-agent decision-theoretic setting, inve…