Author: Fransisca Susan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Digital marketplaces have access to a large amount of user data, which presents new opportunities for learning and data-driven decision-making. However, there are two fundamental challenges associated with the use of data in digital marketplaces. First, many decision-making processes in digital marketplaces involve evaluating and experimenting with a large number of options. While companies may be comfortable with conducting A/B testing when there are only two options, this becomes impractical when there are many more options to consider. Second, machine learning models often rely heavily on data that may not always be accurate or reliable, leading to poor performance when making predictions and later on causing poor decision-making. In addition, real-time decision-making is often required. This motivates the main theme in my thesis, which is to develop effective and efficient online algorithms that take advantage of structures and predictive information in time-varying combinatorial environments, facilitating decision-making in uncertain situations. Examples of such problems include assortment optimization, product ranking, and bid optimization for online advertising. The thesis overall investigates online combinatorial optimization in digital marketplaces with various applications, covering general combinatorial problems, non-parametric choice models, constrained bid optimization in auctions, and fairness-constrained assortment optimization in four parts. In the first chapter, we address the problem of making real-time decisions in a time-varying combinatorial environment where the decision maker needs to balance optimizing their decision and learning about the underlying environment. We propose a unified framework that transforms robust greedy approximation algorithms into their online counterparts, even with non-linear objective functions. This framework is applicable in both full-information and bandit feedback settings, obtaining [square root]T and T3/4 regret respectively. In the second chapter, we focus on the problem of learning non-parametric choice models on digital platforms in an active learning setting. This method involves influencing the data collection process to obtain more favorable data for estimation, in contrast to using only offline data or A/B testing, which might result in limited data sets. In the third and fourth chapters, we incorporate constraints into our optimization problems, which add complexity to the decision space, while still maintaining a large decision space. Specifically, in the third chapter, we propose a bidding strategy for budget-constrained advertisers participating on multiple platforms with different non-IC auction formats. Our proposed non-linear value-pacing-based strategy is optimal in the offline setting and has no-regret in the online setting. Lastly, in the fourth chapter, we incorporate fairness constraints to an assortment optimization problem in digital marketplaces with diverse demographics. We aim to maximize the total market share across groups subject to the condition that the market share of each group meets a predetermined threshold, while also considering legal issues that prevent personalization. We present optimal approximation algorithms for both the offline and online settings.
Online Combinatorial Optimization for Digital Marketplaces
Author: Fransisca Susan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Digital marketplaces have access to a large amount of user data, which presents new opportunities for learning and data-driven decision-making. However, there are two fundamental challenges associated with the use of data in digital marketplaces. First, many decision-making processes in digital marketplaces involve evaluating and experimenting with a large number of options. While companies may be comfortable with conducting A/B testing when there are only two options, this becomes impractical when there are many more options to consider. Second, machine learning models often rely heavily on data that may not always be accurate or reliable, leading to poor performance when making predictions and later on causing poor decision-making. In addition, real-time decision-making is often required. This motivates the main theme in my thesis, which is to develop effective and efficient online algorithms that take advantage of structures and predictive information in time-varying combinatorial environments, facilitating decision-making in uncertain situations. Examples of such problems include assortment optimization, product ranking, and bid optimization for online advertising. The thesis overall investigates online combinatorial optimization in digital marketplaces with various applications, covering general combinatorial problems, non-parametric choice models, constrained bid optimization in auctions, and fairness-constrained assortment optimization in four parts. In the first chapter, we address the problem of making real-time decisions in a time-varying combinatorial environment where the decision maker needs to balance optimizing their decision and learning about the underlying environment. We propose a unified framework that transforms robust greedy approximation algorithms into their online counterparts, even with non-linear objective functions. This framework is applicable in both full-information and bandit feedback settings, obtaining [square root]T and T3/4 regret respectively. In the second chapter, we focus on the problem of learning non-parametric choice models on digital platforms in an active learning setting. This method involves influencing the data collection process to obtain more favorable data for estimation, in contrast to using only offline data or A/B testing, which might result in limited data sets. In the third and fourth chapters, we incorporate constraints into our optimization problems, which add complexity to the decision space, while still maintaining a large decision space. Specifically, in the third chapter, we propose a bidding strategy for budget-constrained advertisers participating on multiple platforms with different non-IC auction formats. Our proposed non-linear value-pacing-based strategy is optimal in the offline setting and has no-regret in the online setting. Lastly, in the fourth chapter, we incorporate fairness constraints to an assortment optimization problem in digital marketplaces with diverse demographics. We aim to maximize the total market share across groups subject to the condition that the market share of each group meets a predetermined threshold, while also considering legal issues that prevent personalization. We present optimal approximation algorithms for both the offline and online settings.
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Digital marketplaces have access to a large amount of user data, which presents new opportunities for learning and data-driven decision-making. However, there are two fundamental challenges associated with the use of data in digital marketplaces. First, many decision-making processes in digital marketplaces involve evaluating and experimenting with a large number of options. While companies may be comfortable with conducting A/B testing when there are only two options, this becomes impractical when there are many more options to consider. Second, machine learning models often rely heavily on data that may not always be accurate or reliable, leading to poor performance when making predictions and later on causing poor decision-making. In addition, real-time decision-making is often required. This motivates the main theme in my thesis, which is to develop effective and efficient online algorithms that take advantage of structures and predictive information in time-varying combinatorial environments, facilitating decision-making in uncertain situations. Examples of such problems include assortment optimization, product ranking, and bid optimization for online advertising. The thesis overall investigates online combinatorial optimization in digital marketplaces with various applications, covering general combinatorial problems, non-parametric choice models, constrained bid optimization in auctions, and fairness-constrained assortment optimization in four parts. In the first chapter, we address the problem of making real-time decisions in a time-varying combinatorial environment where the decision maker needs to balance optimizing their decision and learning about the underlying environment. We propose a unified framework that transforms robust greedy approximation algorithms into their online counterparts, even with non-linear objective functions. This framework is applicable in both full-information and bandit feedback settings, obtaining [square root]T and T3/4 regret respectively. In the second chapter, we focus on the problem of learning non-parametric choice models on digital platforms in an active learning setting. This method involves influencing the data collection process to obtain more favorable data for estimation, in contrast to using only offline data or A/B testing, which might result in limited data sets. In the third and fourth chapters, we incorporate constraints into our optimization problems, which add complexity to the decision space, while still maintaining a large decision space. Specifically, in the third chapter, we propose a bidding strategy for budget-constrained advertisers participating on multiple platforms with different non-IC auction formats. Our proposed non-linear value-pacing-based strategy is optimal in the offline setting and has no-regret in the online setting. Lastly, in the fourth chapter, we incorporate fairness constraints to an assortment optimization problem in digital marketplaces with diverse demographics. We aim to maximize the total market share across groups subject to the condition that the market share of each group meets a predetermined threshold, while also considering legal issues that prevent personalization. We present optimal approximation algorithms for both the offline and online settings.
Online Combinatorial Optimization
Author: Frank Xiao
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Nonlinear Combinatorial Optimization
Author: Ding-Zhu Du
Publisher: Springer
ISBN: 3030161943
Category : Mathematics
Languages : en
Pages : 315
Book Description
Graduate students and researchers in applied mathematics, optimization, engineering, computer science, and management science will find this book a useful reference which provides an introduction to applications and fundamental theories in nonlinear combinatorial optimization. Nonlinear combinatorial optimization is a new research area within combinatorial optimization and includes numerous applications to technological developments, such as wireless communication, cloud computing, data science, and social networks. Theoretical developments including discrete Newton methods, primal-dual methods with convex relaxation, submodular optimization, discrete DC program, along with several applications are discussed and explored in this book through articles by leading experts.
Publisher: Springer
ISBN: 3030161943
Category : Mathematics
Languages : en
Pages : 315
Book Description
Graduate students and researchers in applied mathematics, optimization, engineering, computer science, and management science will find this book a useful reference which provides an introduction to applications and fundamental theories in nonlinear combinatorial optimization. Nonlinear combinatorial optimization is a new research area within combinatorial optimization and includes numerous applications to technological developments, such as wireless communication, cloud computing, data science, and social networks. Theoretical developments including discrete Newton methods, primal-dual methods with convex relaxation, submodular optimization, discrete DC program, along with several applications are discussed and explored in this book through articles by leading experts.
Online Combinatorial Optimization Under Bandit Feedback
Author:
Publisher:
ISBN: 9789175958361
Category :
Languages : en
Pages : 133
Book Description
Publisher:
ISBN: 9789175958361
Category :
Languages : en
Pages : 133
Book Description
Combinatorial Optimization and Applications
Author: Donghyun Kim
Publisher: Springer
ISBN: 3030046516
Category : Computers
Languages : en
Pages : 760
Book Description
The conference proceeding LNCS 11346 constitutes the refereed proceedings of the 12th International Conference on Combinatorial Optimization and Applications, COCOA 2018, held in Atlanta, GA, USA, in December 2018. The 50 full papers presented were carefully reviewed and selected from 106 submissions. The papers cover most aspects of t graph algorithms, routing and network design problems, scheduling algorithms, network optimization, combinatorial algorithms, approximation algorithms, paths and connectivity problems and much more.
Publisher: Springer
ISBN: 3030046516
Category : Computers
Languages : en
Pages : 760
Book Description
The conference proceeding LNCS 11346 constitutes the refereed proceedings of the 12th International Conference on Combinatorial Optimization and Applications, COCOA 2018, held in Atlanta, GA, USA, in December 2018. The 50 full papers presented were carefully reviewed and selected from 106 submissions. The papers cover most aspects of t graph algorithms, routing and network design problems, scheduling algorithms, network optimization, combinatorial algorithms, approximation algorithms, paths and connectivity problems and much more.
Combinatorial Optimization
Author: Bernhard Korte
Publisher: Springer
ISBN: 3662560399
Category : Mathematics
Languages : en
Pages : 701
Book Description
This comprehensive textbook on combinatorial optimization places special emphasis on theoretical results and algorithms with provably good performance, in contrast to heuristics. It is based on numerous courses on combinatorial optimization and specialized topics, mostly at graduate level. This book reviews the fundamentals, covers the classical topics (paths, flows, matching, matroids, NP-completeness, approximation algorithms) in detail, and proceeds to advanced and recent topics, some of which have not appeared in a textbook before. Throughout, it contains complete but concise proofs, and also provides numerous exercises and references. This sixth edition has again been updated, revised, and significantly extended. Among other additions, there are new sections on shallow-light trees, submodular function maximization, smoothed analysis of the knapsack problem, the (ln 4+ɛ)-approximation for Steiner trees, and the VPN theorem. Thus, this book continues to represent the state of the art of combinatorial optimization.
Publisher: Springer
ISBN: 3662560399
Category : Mathematics
Languages : en
Pages : 701
Book Description
This comprehensive textbook on combinatorial optimization places special emphasis on theoretical results and algorithms with provably good performance, in contrast to heuristics. It is based on numerous courses on combinatorial optimization and specialized topics, mostly at graduate level. This book reviews the fundamentals, covers the classical topics (paths, flows, matching, matroids, NP-completeness, approximation algorithms) in detail, and proceeds to advanced and recent topics, some of which have not appeared in a textbook before. Throughout, it contains complete but concise proofs, and also provides numerous exercises and references. This sixth edition has again been updated, revised, and significantly extended. Among other additions, there are new sections on shallow-light trees, submodular function maximization, smoothed analysis of the knapsack problem, the (ln 4+ɛ)-approximation for Steiner trees, and the VPN theorem. Thus, this book continues to represent the state of the art of combinatorial optimization.
Combinatorial Optimization in Digital Communications
Author: Tobias Bernd Dietz
Publisher:
ISBN: 9783843949996
Category :
Languages : en
Pages :
Book Description
Publisher:
ISBN: 9783843949996
Category :
Languages : en
Pages :
Book Description
Combinatorial Optimization and Applications
Author: Weili Wu
Publisher: Springer
ISBN: 9783642174599
Category :
Languages : en
Pages : 442
Book Description
Publisher: Springer
ISBN: 9783642174599
Category :
Languages : en
Pages : 442
Book Description
Online Algorithms for Combinatorial Optimization Problems
Author: 康宁
Publisher:
ISBN:
Category : Combinatorial optimization
Languages : en
Pages : 143
Book Description
Publisher:
ISBN:
Category : Combinatorial optimization
Languages : en
Pages : 143
Book Description
Topics in Combinatorial Optimization
Author: S. Rinaldi
Publisher:
ISBN: 9783709132920
Category :
Languages : en
Pages : 200
Book Description
Publisher:
ISBN: 9783709132920
Category :
Languages : en
Pages : 200
Book Description