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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Causal Bandits with Propagating Inference
Akihiro Yabe,Daisuke Hatano,Hanna Sumita,Shinji Ito,Naonori Kakimura,Takuro Fukunaga,Ken-ichi Kawarabayashi +6 more
TL;DR: A novel causal bandit algorithm is proposed for an arbitrary set of interventions, which can propagate throughout the causal graph, and it is shown that it achieves the regret bound, where $\gamma*$ is determined by using a causal graph structure.
Posted Content
Deep Bayesian Bandits: Exploring in Online Personalized Recommendations
TL;DR: This work forms a display advertising recommender as a contextual bandit and implements exploration techniques that require sampling from the posterior distribution of click-through-rates in a computationally tractable manner, and benchmarks a number of different models in an offline simulation environment using a publicly available dataset of user-ads engagements.
Posted Content
Contextual Semibandits via Supervised Learning Oracles
TL;DR: This paper reduces contextual semibandits to supervised learning, allowing us to leverage powerful supervised learning methods in this partial-feedback setting and shows that this algorithm outperforms state-of-the-art approaches on real-world learning-to-rank datasets, demonstrating the advantage of oracle-based algorithms.
Proceedings ArticleDOI
CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation
TL;DR: Li et al. as discussed by the authors derive a causal graph to identify and analyze the visual bias of existing visual-aware recommender systems, and they propose a debiased visually-aware recommendation system, denoted as CausalRec, to effectively retain the supportive significance of the visual information and remove the visual biases.
Journal ArticleDOI
Information Bottleneck for Estimating Treatment Effects with Systematically Missing Covariates.
TL;DR: This paper trains an information bottleneck to perform a low-dimensional compression of covariates by explicitly considering the relevance of information for treatment effects and can reliably and accurately estimate treatment effects even in the absence of a full set of covariate information at test time.
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.