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Related papers: Learning to Price Homogeneous Data

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Online Resource Allocation problem is a central problem in many areas of Computer Science, Operations Research, and Economics. In this problem, we sequentially receive $n$ stochastic requests for $m$ kinds of shared resources, where each…

Data Structures and Algorithms · Computer Science 2025-05-07 Rohan Ghuge , Sahil Singla , Yifan Wang

We study an online linear classification problem, in which the data is generated by strategic agents who manipulate their features in an effort to change the classification outcome. In rounds, the learner deploys a classifier, and an…

Machine Learning · Computer Science 2017-10-24 Jinshuo Dong , Aaron Roth , Zachary Schutzman , Bo Waggoner , Zhiwei Steven Wu

We study the online learning problem of a bidder who participates in repeated auctions. With the goal of maximizing his T-period payoff, the bidder determines the optimal allocation of his budget among his bids for $K$ goods at each period.…

Computer Science and Game Theory · Computer Science 2017-11-20 Sevi Baltaoglu , Lang Tong , Qing Zhao

Online platforms collect rich information about participants and then share some of this information back with them to improve market outcomes. In this paper we study the following information disclosure problem in two-sided markets: If a…

Theoretical Economics · Economics 2023-09-01 Bar Light , Ramesh Johari , Gabriel Weintraub

Training differentially private machine learning models requires constraining an individual's contribution to the optimization process. This is achieved by clipping the $2$-norm of their gradient at a predetermined threshold prior to…

Machine Learning · Computer Science 2024-01-09 Filippo Galli , Catuscia Palamidessi , Tommaso Cucinotta

Advertisers increasingly use automated bidding to optimize their ad campaigns on online advertising platforms. Autobidding optimizes an advertiser's objective subject to various constraints, e.g. average ROI and budget constraints. In this…

Computer Science and Game Theory · Computer Science 2024-04-16 Gagan Aggarwal , Giannis Fikioris , Mingfei Zhao

In this paper, we investigate the problem about how to bid in repeated contextual first price auctions. We consider a single bidder (learner) who repeatedly bids in the first price auctions: at each time $t$, the learner observes a context…

Machine Learning · Computer Science 2021-11-11 Ashwinkumar Badanidiyuru , Zhe Feng , Guru Guruganesh

This paper examines how data inputs shape competition among artificial intelligences (AIs) in pricing games. The dataset assigns labels to consumers and divides them into different markets, thereby inducing multimarket contact among AIs. We…

General Economics · Economics 2025-12-30 Zhang Xu , Mingsheng Zhang , Wei Zhao

We consider dynamic pricing with covariates under a generalized linear demand model: a seller can dynamically adjust the price of a product over a horizon of $T$ time periods, and at each time period $t$, the demand of the product is…

Machine Learning · Computer Science 2023-11-14 Hanzhao Wang , Kalyan Talluri , Xiaocheng Li

Motivated by posted price auctions where buyers are grouped in an unknown number of latent types characterized by their private values for the good on sale, we investigate revenue maximization in stochastic dynamic pricing when the…

Machine Learning · Computer Science 2019-03-06 Nicolò Cesa-Bianchi , Tommaso Cesari , Vianney Perchet

In diverse fields ranging from finance to omics, it is increasingly common that data is distributed and with multiple individual sources (referred to as ``clients'' in some studies). Integrating raw data, although powerful, is often not…

Methodology · Statistics 2022-11-08 Yuanxing Chen , Qingzhao Zhang , Shuangge Ma , Kuangnan Fang

When online sellers use AI learning algorithms to automatically compete on e-commerce platforms, there is concern that they will learn to coordinate on higher than competitive prices. However, this concern was primarily raised in…

General Economics · Economics 2025-11-03 Hangcheng Zhao , Ron Berman

Motivated by the dynamic assortment offerings and item pricings occurring in e-commerce, we study a general problem of allocating finite inventories to heterogeneous customers arriving sequentially. We analyze this problem under the…

Data Structures and Algorithms · Computer Science 2019-05-14 Will Ma , David Simchi-Levi

We study the following fundamental data-driven pricing problem. How can/should a decision-maker price its product based on data at a single historical price? How valuable is such data? We consider a decision-maker who optimizes over…

Computer Science and Game Theory · Computer Science 2022-03-30 Amine Allouah , Achraf Bahamou , Omar Besbes

This paper studies an online learning problem that seeks optimal testing policies for a stream of subjects, each of whom can be evaluated through a sequence of candidate tests drawn from a common pool. We refer to this problem as the Online…

Machine Learning · Computer Science 2025-09-05 Qiyuan Chen , Raed Al Kontar

We study a repeated trading problem in which a mechanism designer facilitates trade between a single seller and multiple buyers. Our model generalizes the classic bilateral trade setting to a multi-buyer environment. Specifically, the…

Computer Science and Game Theory · Computer Science 2025-03-04 Anna Lunghi , Matteo Castiglioni , Alberto Marchesi

The problem of market clearing is to set a price for an item such that quantity demanded equals quantity supplied. In this work, we cast the problem of predicting clearing prices into a learning framework and use the resulting models to…

Machine Learning · Computer Science 2019-06-25 Weiran Shen , Sébastien Lahaie , Renato Paes Leme

We study unconstrained Online Linear Optimization with Lipschitz losses. Motivated by the pursuit of instance optimality, we propose a new algorithm that simultaneously achieves ($i$) the AdaGrad-style second order gradient adaptivity; and…

Machine Learning · Computer Science 2024-02-23 Zhiyu Zhang , Heng Yang , Ashok Cutkosky , Ioannis Ch. Paschalidis

We consider systems that require timely monitoring of sources over a communication network, where the cost of delayed information is unknown, time-varying and possibly adversarial. For the single source monitoring problem, we design…

Networking and Internet Architecture · Computer Science 2021-05-31 Vishrant Tripathi , Eytan Modiano

We study the problem of learning the optimal item pricing for a unit-demand buyer with independent item values, and the learner has query access to the buyer's value distributions. We consider two common query models in the literature: the…

Computer Science and Game Theory · Computer Science 2025-06-04 Yifeng Teng , Yifan Wang
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