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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Concrete Problems in AI Safety
TL;DR: A list of five practical research problems related to accident risk, categorized according to whether the problem originates from having the wrong objective function, an objective function that is too expensive to evaluate frequently, or undesirable behavior during the learning process, are presented.
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Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
TL;DR: This tutorial article aims to provide the reader with the conceptual tools needed to get started on research on offline reinforcement learning algorithms: reinforcementlearning algorithms that utilize previously collected data, without additional online data collection.
Proceedings Article
Hidden technical debt in Machine learning systems
D. Sculley,Gary Holt,Daniel Golovin,Eugene Davydov,Todd Phillips,Dietmar Ebner,Vinay Chaudhary,Michael Young,Jean-Francois Crespo,Dan Dennison +9 more
TL;DR: It is found it is common to incur massive ongoing maintenance costs in real-world ML systems, and several ML-specific risk factors to account for in system design are explored.
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Toward Causal Representation Learning
Bernhard Schölkopf,Francesco Locatello,Stefan Bauer,Nan Rosemary Ke,Nal Kalchbrenner,Anirudh Goyal,Yoshua Bengio +6 more
TL;DR: The authors reviewed fundamental concepts of causal inference and related them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research.
Posted Content
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Pang Wei Koh,Shiori Sagawa,Henrik Marklund,Sang Michael Xie,Marvin Zhang,Akshay Balsubramani,Weihua Hu,Michihiro Yasunaga,Richard Lanas Phillips,Irena Gao,Tony Lee,Etienne David,Ian Stavness,Wei Guo,Berton A. Earnshaw,Imran S. Haque,Sara Beery,Jure Leskovec,Anshul Kundaje,Emma Pierson,Sergey Levine,Chelsea Finn,Percy Liang +22 more
TL;DR: WILDS is presented, a benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications, and is hoped to encourage the development of general-purpose methods that are anchored to real-world distribution shifts and that work well across different applications and problem settings.
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Proceedings Article
PAC-Bayesian inequalities for martingales
TL;DR: The results extend the PAC-Bayesian analysis in learning theory from the i.i.d. setting to martingales opening the way for its application to importance weighted sampling, reinforcement learning, and other interactive learning domains, as well as many other domains in probability theory and statistics, where Martingales are encountered.
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Dynamic Auctions: A Survey
Dirk Bergemann,Maher Said +1 more
TL;DR: In this paper, the authors survey the recent literature on designing auctions and mechanisms for dynamic settings, including those with a dynamic population of agents or buyers whose private information remains fixed throughout time.
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Causal Inference of Ambiguous Manipulations
Peter Spirtes,Richard Scheines +1 more
TL;DR: This article revisited the question of precisely characterizing conditions and assumptions under which reliable inference about the effects of manipulations is possible, even when the possibility of "ambiguous manipulations" is allowed.
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PAC-Bayesian Inequalities for Martingales
TL;DR: In this paper, the PAC-Bayesian analysis was extended to martingales, and a set of high probability inequalities were derived to control the concentration of weighted averages of multiple evolving and interdependent martingale distributions.