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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Efficient Policy Learning
Susan Athey,Stefan Wager +1 more
TL;DR: This paper derives lower bounds for the minimax regret of policy learning under constraints, and proposes a method that attains this bound asymptotically up to a constant factor, Whenever the class of policies under consideration has a bounded Vapnik-Chervonenkis dimension.
Proceedings ArticleDOI
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility
TL;DR: In this article, the authors demonstrate how using data confounded in this way homogenizes user behavior without increasing utility, which creates a pernicious feedback loop, and demonstrate how to use confounded data to homogenize user behavior.
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Policy Learning With Observational Data
Susan Athey,Stefan Wager +1 more
TL;DR: Given a doubly robust estimator of the causal effect of assigning everyone to treatment, an algorithm for choosing whom to treat is developed, and strong guarantees for the asymptotic utilitarian regret of the resulting policy are established.
Proceedings Article
Balanced Policy Evaluation and Learning
TL;DR: This paper proposed a balance-based approach to evaluate and learn personalized decision policies from observational data of past contexts, decisions, and outcomes, which is equivalent to minimizing worst-case or posterior conditional mean square error.
Proceedings ArticleDOI
Software Engineering Challenges of Deep Learning
TL;DR: The challenges identified in this paper can be used to guide future research by the software engineering and DL communities and could enable a large number of companies to start taking advantage of the high potential of the DL technology.
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.