Adaptive Preference Learning With Bandit Feedback
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Adaptive Preference Learning With Bandit Feedback
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Author : Bangrui Chen
language : en
Publisher:
Release Date : 2017
Adaptive Preference Learning With Bandit Feedback written by Bangrui Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
In this thesis, we study adaptive preference learning, in which a machine learning system learns users' preferences from feedback while simultaneously using these learned preferences to help them find preferred items. We study three different types of user feedback in three application setting: cardinal feedback with application in information filtering systems, ordinal feedback with application in personalized content recommender systems, and attribute feedback with application in review aggregators. We connect these settings respectively to existing work on classical multi-armed bandits, dueling bandits, and incentivizing exploration. For each type of feedback and application setting, we provide an algorithm and a theoretical analysis bounding its regret. We demonstrate through numerical experiments that our algorithms outperform existing benchmarks.
Multi Armed Bandits For Preference Learning
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Author : Sumeet Katariya
language : en
Publisher:
Release Date : 2018
Multi Armed Bandits For Preference Learning written by Sumeet Katariya and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
The multi-armed bandit (MAB) problem is one of the simplest instances of sequential or adaptive decision making, in which a learner needs to select options from a given set of alternatives repeatedly in an online manner. More specifically, the agent selects one option at a time, and observes a numerical (and typically noisy) reward signal providing information on the quality of that option, which informs its future selections. This thesis studies adaptive decision making under different circumstances. The first half of the thesis studies learning using pairwise comparisons. The algorithms depend on the objective of the experimenter. We study the objectives of finding the best item, and approximately ranking the given set of items. In the second half of the thesis, we study the problem of learning from user-clicks. A variety of models have been proposed to simulate user behavior on a search-engine results page, and we study learning in cold-start scenarios under two models: the dependent-click model and the position-based model. Finally, if partial prior information about the quality of items is available, we study learning in such warm-start circumstances. In these cases, our algorithm provides the experimenter means to control the exploration of the bandit algorithm. In all cases, we propose algorithms and prove theoretical guarantees about their performance. We also experimentally measure gains with respect to non-adaptive and state-of-the-art adaptive algorithms.
Preference Learning
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Author : Johannes Fürnkranz
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-11-19
Preference Learning written by Johannes Fürnkranz and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-19 with Computers categories.
The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. And, generalizing beyond training data, models thus learned may be used for preference prediction. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.
Bandits And Preference Learning
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Author : Aniruddha Bhargava
language : en
Publisher:
Release Date : 2017
Bandits And Preference Learning written by Aniruddha Bhargava and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
The internet revolution has brought a large population access to a vast array of infor- mation since the mid 1990s. More recently, with the advent of smartphones, it has become an essential part of our everyday life. This has lead to, among many other developments, the personalization of the online experience with great benefits to all involved. Companies have particular interest in showing products and advertisements that match what particular users are looking for, and users desire getting personalized recommendations from internet for entertainment and consumer goods that suit them as individuals. In machine learning, this is popularly achieved using the theory of the multi-armed bandits, methods which allow us to zero in on the consumer's personal preferences. The last few decades have seen great advances in the theory and practice of multi- armed bandits exploiting either the context of the user, the context of the objects, or both. Great theoretical improvements have brought algorithms' performance close to their theoretical optimal. However, various challenges exist in the practical use of multi-armed bandits. In this thesis, we explore some of these challenges and endeavor to overcome them. First, we examine how multiple populations can be catered to si- multaneously. We then address the issue of scaling multi-armed bandits to situations where there are many arms. We also look at how to incorporate generalized linear re- ward models while maintaining computational efficiency. Finally, we address how we can use feature feedback to focus the bandits exploration to a limited subset of features. This leads to algorithms that are still tractable for high-dimensional datasets where the preferences of the user are explained by a sparse subset of them.
Preference Learning
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Author : Johannes F Rnkranz
language : en
Publisher: Springer
Release Date : 2011-03-30
Preference Learning written by Johannes F Rnkranz and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-30 with Machine learning categories.
Bandit Algorithms
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Author : Tor Lattimore
language : en
Publisher: Cambridge University Press
Release Date : 2020-07-16
Bandit Algorithms written by Tor Lattimore and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-16 with Business & Economics categories.
A comprehensive and rigorous introduction for graduate students and researchers, with applications in sequential decision-making problems.
Robust Preference Learning Based Reinforcement Learning
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Author : Riad Akrour
language : en
Publisher:
Release Date : 2014
Robust Preference Learning Based Reinforcement Learning written by Riad Akrour and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
The thesis contributions resolves around sequential decision taking and more precisely Reinforcement Learning (RL). Taking its root in Machine Learning in the same way as supervised and unsupervised learning, RL quickly grow in popularity within the last two decades due to a handful of achievements on both the theoretical and applicative front. RL supposes that the learning agent and its environment follow a stochastic Markovian decision process over a state and action space. The process is said of decision as the agent is asked to choose at each time step an action to take. It is said stochastic as the effect of selecting a given action in a given state does not systematically yield the same state but rather defines a distribution over the state space. It is said to be Markovian as this distribution only depends on the current state-action pair. Consequently to the choice of an action, the agent receives a reward. The RL goal is then to solve the underlying optimization problem of finding the behaviour that maximizes the sum of rewards all along the interaction of the agent with its environment. From an applicative point of view, a large spectrum of problems can be cast onto an RL one, from Backgammon (TD-Gammon, was one of Machine Learning first success giving rise to a world class player of advanced level) to decision problems in the industrial and medical world. However, the optimization problem solved by RL depends on the prevous definition of a reward function that requires a certain level of domain expertise and also knowledge of the internal quirks of RL algorithms. As such, the first contribution of the thesis was to propose a learning framework that lightens the requirements made to the user. The latter does not need anymore to know the exact solution of the problem but to only be able to choose between two behaviours exhibited by the agent, the one that matches more closely the solution. Learning is interactive between the agent and the user and resolves around the three main following points: i) The agent demonstrates a behaviour ii) The user compares it w.r.t. to the current best one iii) The agent uses this feedback to update its preference model of the user and uses it to find the next behaviour to demonstrate. To reduce the number of required interactions before finding the optimal behaviour, the second contribution of the thesis was to define a theoretically sound criterion making the trade-off between the sometimes contradicting desires of complying with the user's preferences and demonstrating sufficiently different behaviours. The last contribution was to ensure the robustness of the algorithm w.r.t. the feedback errors that the user might make. Which happens more often than not in practice, especially at the initial phase of the interaction, when all the behaviours are far from the expected solution.
Algorithmic Learning Theory
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Author : Peter Auer
language : en
Publisher: Springer
Release Date : 2014-10-01
Algorithmic Learning Theory written by Peter Auer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-01 with Computers categories.
This book constitutes the proceedings of the 25th International Conference on Algorithmic Learning Theory, ALT 2014, held in Bled, Slovenia, in October 2014, and co-located with the 17th International Conference on Discovery Science, DS 2014. The 21 papers presented in this volume were carefully reviewed and selected from 50 submissions. In addition the book contains 4 full papers summarizing the invited talks. The papers are organized in topical sections named: inductive inference; exact learning from queries; reinforcement learning; online learning and learning with bandit information; statistical learning theory; privacy, clustering, MDL, and Kolmogorov complexity.
Reinforcement Learning Second Edition
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Author : Richard S. Sutton
language : en
Publisher: MIT Press
Release Date : 2018-11-13
Reinforcement Learning Second Edition written by Richard S. Sutton and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-13 with Computers categories.
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Ki 2010 Advances In Artificial Intelligence
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Author : Rüdiger Dillmann
language : en
Publisher: Springer
Release Date : 2010-09-08
Ki 2010 Advances In Artificial Intelligence written by Rüdiger Dillmann and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-08 with Computers categories.
The 33rd Annual German Conference on Arti?cial Intelligence (KI 2010) took place at the Karlsruhe Institute of Technology KIT, September 21–24, 2010, under the motto “Anthropomatic Systems.” In this volume you will ?nd the keynote paper and 49 papers of oral and poster presentations. The papers were selected from 73 submissions, resulting in an acceptance rate of 67%. As usual at the KI conferences, two entire days were allocated for targeted workshops—seventhis year—andone tutorial. The workshopand tutorialma- rials are not contained in this volume, but the conference website, www.ki2010.kit.edu,will provide information and references to their contents. Recent trends in AI research have been focusing on anthropomatic systems, which address synergies between humans and intelligent machines. This trend is emphasized through the topics of the overall conference program. They include learning systems, cognition, robotics, perception and action, knowledge rep- sentation and reasoning, and planning and decision making. Many topics deal with uncertainty in various scenarios and incompleteness of knowledge. Summarizing, KI 2010 provides a cross section of recent research in modern AI methods and anthropomatic system applications. We are very grateful that Jos ́ edel Mill ́ an, Hans-Hellmut Nagel, Carl Edward Rasmussen, and David Vernon accepted our invitation to give a talk.