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Related papers: No-Regret Learning in Partially-Informed Auctions

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We consider a stochastic lost-sales inventory control system with a lead time $L$ over a planning horizon $T$. Supply is uncertain, and is a function of the order quantity (due to random yield/capacity, etc). We aim to minimize the…

Optimization and Control · Mathematics 2023-11-01 Boxiao Chen , Jiashuo Jiang , Jiawei Zhang , Zhengyuan Zhou

Bilateral trade models the task of intermediating between two strategic agents, a seller and a buyer, who wish to trade a good. We study this problem from the perspective of a profit-maximizing broker within an online learning framework,…

Computer Science and Game Theory · Computer Science 2026-05-14 Simone Di Gregorio , Paul Dütting , Federico Fusco , Chris Schwiegelshohn

We study repeated first-price auctions and general repeated Bayesian games between two players, where one player, the learner, employs a no-regret learning algorithm, and the other player, the optimizer, knowing the learner's algorithm,…

Computer Science and Game Theory · Computer Science 2024-02-14 Aviad Rubinstein , Junyao Zhao

In digital health and EdTech, recommendation systems face a significant challenge: users often choose impulsively, in ways that conflict with the platform's long-term payoffs. This misalignment makes it difficult to effectively learn to…

Machine Learning · Computer Science 2024-02-22 Arpit Agarwal , Rad Niazadeh , Prathamesh Patil

In this tutorial article, we give an overview of new challenges and representative results on distributed no-regret learning in multi-agent systems modeled as repeated unknown games. Four emerging game characteristics---dynamicity,…

Computer Science and Game Theory · Computer Science 2020-02-24 Xiao Xu , Qing Zhao

Companies like Google and Microsoft run billions of auctions every day to sell advertising opportunities. Any change to the rules of these auctions can have a tremendous effect on the revenue of the company and the welfare of the…

Computer Science and Game Theory · Computer Science 2019-11-07 Saeed Alaei , Ashwinkumar Badanidiyuru , Mohammad Mahdian , Sadra Yazdanbod

We study online learning problems in which a decision maker has to take a sequence of decisions subject to $m$ long-term constraints. The goal of the decision maker is to maximize their total reward, while at the same time achieving small…

Machine Learning · Computer Science 2022-09-16 Matteo Castiglioni , Andrea Celli , Alberto Marchesi , Giulia Romano , Nicola Gatti

The exploration/exploitation trade-off is an inherent challenge in data-driven adaptive control. Though this trade-off has been studied for multi-armed bandits (MAB's) and reinforcement learning for linear systems; it is less well-studied…

Optimization and Control · Mathematics 2023-01-30 Ilgin Dogan , Zuo-Jun Max Shen , Anil Aswani

In this paper, we study the problem of learning to bid in repeated first-price auctions with budget constraints. In each period, the decision maker needs to submit a bid to win the auction and maximize the total collected reward, subject to…

Optimization and Control · Mathematics 2026-03-10 Zeng Fu , Jiashuo Jiang , Yuan Zhou

Internet advertisers (buyers) repeatedly procure ad impressions from ad platforms (sellers) with the aim to maximize total conversion (i.e. ad value) while respecting both budget and return-on-investment (ROI) constraints for efficient…

Computer Science and Game Theory · Computer Science 2023-02-08 Negin Golrezaei , Patrick Jaillet , Jason Cheuk Nam Liang , Vahab Mirrokni

Bayesian persuasion studies how an informed sender should influence beliefs of rational receivers who take decisions through Bayesian updating of a common prior. We focus on the online Bayesian persuasion framework, in which the sender…

Computer Science and Game Theory · Computer Science 2023-03-03 Martino Bernasconi , Matteo Castiglioni , Andrea Celli , Alberto Marchesi , Nicola Gatti , Francesco Trovò

In a typical optimization problem, the task is to pick one of a number of options with the lowest cost or the highest value. In practice, these cost/value quantities often come through processes such as measurement or machine learning,…

Data Structures and Algorithms · Computer Science 2022-07-20 Mohammad Mahdian , Jieming Mao , Kangning Wang

We study a demand response problem from utility (also referred to as operator)'s perspective with realistic settings, in which the utility faces uncertainty and limited communication. Specifically, the utility does not know the cost…

Optimization and Control · Mathematics 2017-08-11 Pan Li , Hao Wang , Baosen Zhang

Bilateral trade, a fundamental topic in economics, models the problem of intermediating between two strategic agents, a seller and a buyer, willing to trade a good for which they hold private valuations. In this paper, we cast the bilateral…

Computer Science and Game Theory · Computer Science 2021-09-28 Nicolò Cesa-Bianchi , Tommaso Cesari , Roberto Colomboni , Federico Fusco , Stefano Leonardi

We develop an online learning algorithm for identifying unlabeled data points that are most informative for training (i.e., active learning). By formulating the active learning problem as the prediction with sleeping experts problem, we…

Machine Learning · Computer Science 2022-02-24 Cenk Baykal , Lucas Liebenwein , Dan Feldman , Daniela Rus

We study the model-based undiscounted reinforcement learning for partially observable Markov decision processes (POMDPs). The oracle we consider is the optimal policy of the POMDP with a known environment in terms of the average reward over…

Machine Learning · Computer Science 2022-07-19 Yi Xiong , Ningyuan Chen , Xuefeng Gao , Xiang Zhou

Developing efficient sequential bidding strategies for repeated auctions is an important practical challenge in various marketing tasks. In this setting, the bidding agent obtains information, on both the value of the item at sale and the…

Machine Learning · Computer Science 2021-03-01 Juliette Achddou , Olivier Cappé , Aurélien Garivier

We consider a class of learning problems in which an agent liquidates a risky asset while creating both transient price impact driven by an unknown convolution propagator and linear temporary price impact with an unknown parameter. We…

Trading and Market Microstructure · Quantitative Finance 2025-01-23 Eyal Neuman , Yufei Zhang

Auctions are key for maximizing sellers' revenue and ensuring truthful bidding among buyers. Recently, an approach known as differentiable economics based on machine learning (ML) has shown promise in learning powerful auction mechanisms…

Computer Science and Game Theory · Computer Science 2025-10-02 Roy Maor Lotan , Inbal Talgam-Cohen , Yaniv Romano

We study sequential bilateral trade where sellers and buyers valuations are completely arbitrary (i.e., determined by an adversary). Sellers and buyers are strategic agents with private valuations for the good and the goal is to design a…

Computer Science and Game Theory · Computer Science 2024-10-11 Yossi Azar , Amos Fiat , Federico Fusco
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