Relating reinforcement learning performance to classification performance

By J. Langford, Bianca Zadrozny. 2005

66 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Reinforcement learning in robotics: A survey (2013, methodology): “Reduction algorithms (Langford and Zadrozny, 2005) are used to convert effective solutions for one class of problems into effective solutions for others, and have proven to be a key technique in machine learning.”
Besting the Quiz Master: Crowdsourcing Incremental Classification Games (2012, methodology): “To learn a policy, we use a standard reinforcement learning technique (Langford and Zadrozny, 2005; Abbeel and Ng, 2004; Syed et al., 2008): given a representation of the state space, learn a classifier that can map from a state to an action.”
A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing (2014, methodology): “Instead of proposing a new hand designed parallel implementation of the core algorithm, we take inspiration from recent work on machine learning reductions [15, 19] and we reduce the Elastic Net to the squared hinge-loss SVM (without a bias term).”
Learning from Demonstrations: Is It Worth Estimating a Reward Function? (2013, methodology): “AL can be reduced to classification [7,3,6,11] where the agent tries to mimic the expert policy via a Supervised Learning (SL) method such as classification.”
Differentiable Sampling with Flexible Reference Word Order for Neural Machine Translation (2019, methodology): “This is a well-known issue in sequential decision making (Langford and Zadrozny, 2005; Co-hen and Carvalho, 2005; K¨a¨ari¨ainen and Lang-ford, 2006, i.a.) and it has been addressed in past work by incorporating the previous decoding choices into the training scheme, using imitation learning (Daum´e…”
Multistage Stochastic Programming: A Scenario Tree Based Approach to Planning under Uncertainty (2011, background): “The literature on this aspect is large: see, for instance, Lagoudakis and Parr (2003); Ernst, Geurts, and Wehenkel (2005); Langford and Zadrozny (2005); Munos and Szepesvári (2008).”
Analysis of Classification-based Policy Iteration Algorithms (2016, background): “Langford and Zadrozny (2005) provided a formal reduction from RL to classification, showing that ǫ-accurate classification implies near optimal RL.”