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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Spatial Positioning Token (SPToken) for Smart Mobility
Roman Overko,Rodrigo H. Ordonez-Hurtado,Sergiy Zhuk,Pietro Ferraro,Andrew Cullen,Robert Shorten +5 more
TL;DR: In this article, a permissioned distributed ledger technology (DLT) design for crowdsourced smart mobility applications is presented, based on a directed acyclic graph architecture (similar to the IOTA tangle) and uses both Proof-of-Work and Proof-Of-Position mechanisms to provide protection against spam attacks and malevolent actors.
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Estimating Individual Advertising Effect in E-Commerce
TL;DR: This paper model the overall return as individual advertising effect in causal inference with multiple treatments and bound the expected estimation error with learnable factual loss and distance of treatment-specific context distributions and applies the learned causal effect in the online bidding engine of an industry-level sponsored search system.
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Learning from Human Feedback: Challenges for Real-World Reinforcement Learning in NLP.
TL;DR: This work presents a concise overview of challenges of using NLP tasks and the constraints of production systems in an offline reinforcement learning (RL) setting and discusses possible solutions.
Posted Content
Learning Decomposed Representation for Counterfactual Inference.
TL;DR: The proposed synergistic learning framework can precisely identify and balance confounders, while the estimation of the treatment effect performs better than the state-of-the-art methods on both synthetic and real-world datasets.
Posted Content
A Large-scale Open Dataset for Bandit Algorithms
TL;DR: This work builds and publicizes the Open Bandit Dataset and Pipeline to facilitate scalable and reproducible research on bandit algorithms and OPE estimators, and finds a counterfactual policy that significantly outperforms the historical ones.
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.