Open AccessJournal Article
Counterfactual reasoning and learning systems: the example of computational advertising
Léon Bottou,Jonas Peters,Joaquin Quiñonero-Candela,Denis X. Charles,D. Max Chickering,Elon Portugaly,Dipankar Ray,Patrice Y. Simard,Ed Snelson +8 more
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This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system and allow both humans and algorithms to select the changes that would have improved the system performance.Abstract:
This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such predictions allow both humans and algorithms to select the changes that would have improved the system performance. This work is illustrated by experiments on the ad placement system associated with the Bing search engine.read more
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Offline Comparison of Ranking Functions using Randomized Data.
TL;DR: This paper proposes two approaches Trunc-match and Rand-interleaving for offline comparison using uniformly randomized data and shows that these methods can improve the data efficiency and also the comparison sensitivity based on one of the largest email search engines.
Book ChapterDOI
Stochastic Linear Bandits with Finitely Many Arms
Tor Lattimore,Csaba Szepesvári +1 more
TL;DR: The core idea is to introduce phases of determinisim into the algorithm so that within each phase the actions are chosen independently from the rewards.
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Contextual Bandits with Stochastic Experts
TL;DR: In this paper, the authors consider the problem of contextual bandits with stochastic experts, where each expert is a conditional distribution over the arms given a context, and propose two different importance sampling based estimators for the mean reward for each expert.
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Off-policy Confidence Sequences
TL;DR: In this article, the authors develop confidence bounds that hold uniformly over time for off-policy evaluation in the contextual bandit setting, based on recent ideas from martingale analysis and are nonasymptotic, non-parametric, and valid at arbitrary stopping times.
References
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Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
MonographDOI
Causality: models, reasoning, and inference
TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
Journal ArticleDOI
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
TL;DR: This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing stochastic units that are shown to make weight adjustments in a direction that lies along the gradient of expected reinforcement in both immediate-reinforcement tasks and certain limited forms of delayed-reInforcement tasks, and they do this without explicitly computing gradient estimates.
Book
Introduction to Reinforcement Learning
TL;DR: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.