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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A Survey on Semantic Parsing.
Aishwarya Kamath,Rajarshi Das +1 more
TL;DR: The field of semantic parsing deals with converting natural language utterances to logical forms that can be easily executed on a knowledge base as mentioned in this paper, and a significant amount of information in today's world is stored in structured and semi-structured knowledge bases.
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Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting
TL;DR: A new method to compute a lower bound on the value of an arbitrary target policy given some logged data in contextual bandits for a desired coverage is proposed, built around the so-called Self-normalized Importance Weighting (SN) estimator.
Journal ArticleDOI
The details matter: methodological nuances in the evaluation of student models
TL;DR: Three important methodological issues in student modeling are discussed: the impact of data collection, the splitting of data into a training set and a test set, and the details concerning averaging in the computation of predictive accuracy metrics.
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Efficient adjustment sets for population average treatment effect estimation in non-parametric causal graphical models.
TL;DR: For point interventions, this paper provides a sound and complete graphical criterion for determining when a non-parametric optimally adjusted estimator of an interventional mean, or of a contrast of interventional means, is as efficient as an efficient estimators of the same parameter that exploits the information in the conditional independencies encoded in the non- Parametric causal graphical model.
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Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation
TL;DR: Two bootstrapping off-policy evaluation methods which use learned MDP transition models in order to estimate lower confidence bounds on policy performance with limited data in both continuous and discrete state spaces are proposed.
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.