Multi Armed Bandits For Preference Learning
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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.
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.
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.
Introduction To Multi Armed Bandits
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Author : Aleksandrs Slivkins
language : en
Publisher:
Release Date : 2019-10-31
Introduction To Multi Armed Bandits written by Aleksandrs Slivkins and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-31 with Computers categories.
Multi-armed bandits is a rich, multi-disciplinary area that has been studied since 1933, with a surge of activity in the past 10-15 years. This is the first book to provide a textbook like treatment of the subject.
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.
Collaborative Filtering Recommender Systems
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Author : Michael D. Ekstrand
language : en
Publisher: Now Publishers Inc
Release Date : 2011
Collaborative Filtering Recommender Systems written by Michael D. Ekstrand and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.
Collaborative Filtering Recommender Systems discusses a wide variety of the recommender choices available and their implications, providing both practitioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.
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 In Large Scale Complex Systems
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Author : Xiao Xu
language : en
Publisher:
Release Date : 2020
Multi Armed Bandits In Large Scale Complex Systems written by Xiao Xu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
This dissertation focuses on the multi-armed bandit problem (MAB) where the objective is a sequential arm selection policy that maximizes the total reward over time. In canonical formulations of MAB, the following assumptions are adopted: the size of the action space is much smaller than the length of the time horizon, computation resources such as memory are unlimited in the learning process, and the generative models of arm rewards are time-invariant. This dissertation aims to relax these assumptions, which are unrealistic in emerging applications involving large-scale complex systems, and develop corresponding techniques to address the resulting new issues. The first part of the dissertation aims to address the issue of a massive number of actions. A stochastic bandit problem with side information on arm similarity and dissimilarity is studied. The main results include a unit interval graph (UIG) representation of the action space that succinctly models the side information and a two-step learning structure that fully exploits the topological structure of the UIG to achieve an optimal scaling of the learning cost with the size of the action space. Specifically, in the UIG representation, each node represents an arm and the presence (absence) of an edge between two nodes indicates similarity (dissimilarity) between their mean rewards. Based on whether the UIG is fully revealed by the side information, two settings with complete and partial side information are considered. For each setting, a two-step learning policy consisting of an offline reduction of the action space and online aggregation of reward observations from similar arms is developed. The computation efficiency and the order optimality of the proposed strategies in terms of the size of the action space and the time length are established. Numerical experiments on both synthetic and real-world datasets are conducted to verify the performance of the proposed policies in practice. In the second part of the dissertation, the issue of limited memory during the learning process is studied in the adversarial bandit setting. Specifically, a learning policy can only store the statistics of a subset of arms summarizing their reward history. A general hierarchical learning structure that trades off the regret order with memory complexity is developed based on multi-level partitions of the arm set into groups and the time horizon into epochs. The proposed learning policy requires only a sublinear order of memory space in terms of the number of arms. Its sublinear regret orders with respect to the time horizon are established for both weak regret and shifting regret in expectation and/or with high probability, when appropriate learning strategies are adopted as subroutines at all levels. By properly choosing the number of levels in the adopted hierarchy, the policy adapts to different sizes of the available memory space. A memory-dependent regret bound is established to characterize the tradeoff between memory complexity and the regret performance of the policy. Numerical examples are provided to verify the performance of the policy. The third part of the dissertation focuses on the issue of time-varying rewards within the contextual bandit framework, which finds applications in various online recommendation systems. The main results include two reward models characterizing the fact that the preferences of users toward different items change asynchronously and distinctly, and a learning algorithm that adapts to the dynamic environment. In particular, the two models assume disjoint and hybrid rewards. In the disjoint setting, the mean reward of playing an arm is determined by an arm-specific preference vector, which is piecewise-stationary with asynchronous change times across arms. In the hybrid setting, the mean reward of an arm also depends on a joint coefficient vector shared by all arms representing the time-invariant component of user interests, in addition to the arm-specific one that is time-varying. Two algorithms based on change detection and restarts are developed in the two settings respectively, of which the performance is verified through simulations on both synthetic and real-world data. Theoretical regret analysis of the algorithm with certain modifications is provided under the disjoint reward model, which shows that a near-optimal regret order in the time length is achieved.
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.
Regret Analysis Of Stochastic And Nonstochastic Multi Armed Bandit Problems
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Author : Sébastien Bubeck
language : en
Publisher: Now Pub
Release Date : 2012
Regret Analysis Of Stochastic And Nonstochastic Multi Armed Bandit Problems written by Sébastien Bubeck and has been published by Now Pub this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
In this monograph, the focus is on two extreme cases in which the analysis of regret is particularly simple and elegant: independent and identically distributed payoffs and adversarial payoffs. Besides the basic setting of finitely many actions, it analyzes some of the most important variants and extensions, such as the contextual bandit model.