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
Reads0
Chats0
TLDR
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
Citations
More filters
Posted Content
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.
Posted Content
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.
Journal ArticleDOI
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.
References
More filters
Proceedings ArticleDOI
Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms
TL;DR: In this paper, the authors introduce a replay methodology for contextual bandit algorithm evaluation, which is completely data-driven and very easy to adapt to different applications, and provide provably unbiased evaluations.
Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms
TL;DR: This paper introduces a replay methodology for contextual bandit algorithm evaluation that is completely data-driven and very easy to adapt to different applications and can provide provably unbiased evaluations.
Journal Article
Contextual bandits with similarity information
TL;DR: In this paper, the authors consider similarity information in the setting of contextual bandits, a natural extension of the basic MAB problem where before each round an algorithm is given the context--a hint about the payoffs in this round.
Proceedings ArticleDOI
Overlapping experiment infrastructure: more, better, faster experimentation
TL;DR: Google's overlapping experiment infrastructure is described, and the associated tools and educational processes required to use it effectively are discussed, which can be generalized and applied by any entity interested in using experimentation to improve search engines and other web applications.