516 citations · 800 across the 9 of their papers we have counts for
9 papers · 1 filter
Normalized Online Learning
Stephane Ross, Paul Mineiro, John Langford
We introduce online learning algorithms which are independent of feature scales, proving regret bounds dependent on the ratio of scales existent in the data rather than the absolut…
Reinforcement and Imitation Learning via Interactive No-Regret Learning
Stephane Ross, J. Andrew Bagnell
Recent work has demonstrated that problems-- particularly imitation learning and structured prediction-- where a learner's predictions influence the input-distribution it is tested…
A Credit Assignment Compiler for Joint Prediction
Kai-Wei Chang, He He, Hal Daumé +2
Many machine learning applications involve jointly predicting multiple mutually dependent output variables. Learning to search is a family of methods where the complex decision pro…
Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization
Jiaji Zhou, Stephane Ross, Yisong Yue +2
We study the problem of predicting a set or list of options under knapsack constraint. The quality of such lists are evaluated by a submodular reward function that measures both qu…
Normalized Online Learning
Stephane Ross, Paul Mineiro, John Langford
We introduce online learning algorithms which are independent of feature scales, proving regret bounds dependent on the ratio of scales existent in the data rather than the absolut…
Learning Policies for Contextual Submodular Prediction
Stephane Ross, Jiaji Zhou, Yisong Yue +2
Many prediction domains, such as ad placement, recommendation, trajectory prediction, and document summarization, require predicting a set or list of options. Such lists are often…
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…
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…
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
Stephane Ross, Geoffrey J. Gordon, J. Andrew Bagnell
Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statist…