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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Posted Content
Explaining Transition Systems through Program Induction.
TL;DR: This work introduces the $\pi$-machine (program-induction machine) -- an architecture able to induce interpretable LISP-like programs from observed data traces and proposes an optimisation procedure for program learning based on backpropagation, gradient descent and A* search.
Dissertation
Clusterisation incrémentale, multicritères de données hétérogènes pour la personnalisation d’expérience utilisateur
TL;DR: L'objectif global de cette these est de proposer une methode generique d'A/B test permettant une allocation dynamique en temps reel capable of prendre en compte les caracteristiques des sujets, qu'elles soient temporelles ou non, and interpretable a posteriori.
Journal ArticleDOI
Learning-Based Decentralized Offloading Decision Making in an Adversarial Environment
Byungjin Cho,Yu Xiao +1 more
TL;DR: In this article, a new adversarial online learning algorithm with bandit feedback based on the adversarial multi-armed bandit theory was developed to enable scalable and low-complexity offloading decision making.
Proceedings ArticleDOI
Causal Inference and Counterfactual Reasoning
Amit Sharma,Emre Kiciman +1 more
TL;DR: This tutorial will introduce concepts in causal inference and counterfactual reasoning, drawing from a broad literature on the topic from statistics, social sciences and machine learning, and describe the most popular frameworks based on Bayesian graphical models and potential outcomes.
Proceedings ArticleDOI
Predicting Individual Treatment Effects of Large-scale Team Competitions in a Ride-sharing Economy
TL;DR: This study analyzes data collected from more than 500 large-scale team competitions organized by a leading ride-sharing platform, building machine learning models to predict individual treatment effects and discovers many novel and actionable insights regarding how to optimize the design and the execution of team competitions onride-sharing platforms.
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.