Open AccessDissertation
Dynamic Pricing and Learning
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The article was published on 2013-02-07 and is currently open access. It has received 18 citations till now. The article focuses on the topics: Investment theory & Dynamic pricing.read more
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Journal Article
Dynamic pricing and learning: Historical origins, current research, and new directions
TL;DR: A brief introduction to the historical origins of quantitative research on pricing and demand estimation is provided, point to different subfields in the area of dynamic pricing, and an in-depth overview of the available literature on dynamic pricing and learning is provided.
Journal ArticleDOI
Dynamic pricing and learning: historical origins, current research, and new directions
TL;DR: In this paper, the authors provide a brief introduction to the historical origins of quantitative research on pricing and demand estimation, point to different subfields in the area of dynamic pricing, and provide an in-depth overview of the available literature on dynamic pricing and learning.
Journal ArticleDOI
A survey on risk-averse and robust revenue management
TL;DR: This paper motivates the consideration of risk-averse and robust revenue management and briefly introduces revenue managements’ two main methods – capacity control and dynamic pricing – in the classical, risk-neutral setting.
Proceedings Article
Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions
TL;DR: This work proposes two learning policies that are robust to strategic behavior in repeated contextual second-price auctions and uses the outcomes of the auctions, rather than the submitted bids, to estimate the preferences while controlling the long-term effect of the outcome of each auction on the future reserve prices.
Journal ArticleDOI
Reinforcement learning applied to airline revenue management
TL;DR: This work presents a new airline Revenue Management System (RMS) based on RL, which does not require a demand forecaster and integrates domain knowledge with a deep neural network trained on GPUs.
References
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Book
Generalized Linear Models
Peter McCullagh,John A. Nelder +1 more
TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Book
Markov Decision Processes: Discrete Stochastic Dynamic Programming
TL;DR: Puterman as discussed by the authors provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models, focusing primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous time discrete state models.
Journal ArticleDOI
Generalized Linear Models
TL;DR: This is the rst book on generalized linear models written by authors not mostly associated with the biological sciences, and it is thoroughly enjoyable to read.