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Found 8 papers, 2 papers with code
Normalized Online Learning
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 absolute scale.
Reinforcement and Imitation Learning via Interactive No-Regret Learning
Recent work has demonstrated that problems-- particularly imitation learning and structured prediction-- where a learner's predictions influence the input-distribution it is tested on can be naturally addressed by an interactive approach and analyzed using no-regret online learning.
A Credit Assignment Compiler for Joint Prediction
Many machine learning applications involve jointly predicting multiple mutually dependent output variables.
Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization
We study the problem of predicting a set or list of options under knapsack constraint.
Normalized Online Learning
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 absolute scale.
Learning Policies for Contextual Submodular Prediction
Many prediction domains, such as ad placement, recommendation, trajectory prediction, and document summarization, require predicting a set or list of options.
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i. i. d.
Theoretical Analysis of Heuristic Search Methods for Online POMDPs
The algorithm uses search heuristics based on an error analysis of lookahead search, to guide the online search towards reachable beliefs with the most potential to reduce error.
