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Motivated by online retail, we consider the problem of selling one item (e.g., an ad slot) to two non-excludable buyers (say, a merchant and a brand). This problem captures, for example, situations where a merchant and a brand cooperatively…

Computer Science and Game Theory · Computer Science 2025-05-26 Gagan Aggarwal , Ashwinkumar Badanidiyuru , Paul Dütting , Federico Fusco

In the convex optimization approach to online regret minimization, many methods have been developed to guarantee a $O(\sqrt{T})$ bound on regret for subdifferentiable convex loss functions with bounded subgradients, by using a reduction to…

Machine Learning · Computer Science 2016-09-20 Arthur Flajolet , Patrick Jaillet

We study finite-state communication games in which the sender's preference is perturbed by random private idiosyncrasies. Persuasion is generically impossible within the class of statistically independent sender/receiver preferences --…

Theoretical Economics · Economics 2024-03-22 Alistair Barton

Decentralized Online Learning (online learning in decentralized networks) attracts more and more attention, since it is believed that Decentralized Online Learning can help the data providers cooperatively better solve their online problems…

Machine Learning · Computer Science 2019-05-30 Yawei Zhao , Chen Yu , Peilin Zhao , Hanlin Tang , Shuang Qiu , Ji Liu

We consider the online distributed non-stochastic experts problem, where the distributed system consists of one coordinator node that is connected to $k$ sites, and the sites are required to communicate with each other via the coordinator.…

Machine Learning · Computer Science 2012-11-15 Varun Kanade , Zhenming Liu , Bozidar Radunovic

Several techniques for domain adaptation have been proposed to account for differences in the distribution of the data used for training and testing. The majority of this work focuses on a binary domain label. Similar problems occur in a…

Machine Learning · Statistics 2020-02-18 Gilles Louppe , Michael Kagan , Kyle Cranmer

We study the problem of designing mechanisms for \emph{information acquisition} scenarios. This setting models strategic interactions between an uniformed \emph{receiver} and a set of informed \emph{senders}. In our model the senders…

Computer Science and Game Theory · Computer Science 2023-06-13 Federico Cacciamani , Matteo Castiglioni , Nicola Gatti

We study an online mixed discrete and continuous optimization problem where a decision maker interacts with an unknown environment for a number of $T$ rounds. At each round, the decision maker needs to first jointly choose a discrete and a…

Optimization and Control · Mathematics 2024-08-27 Lintao Ye , Ming Chi , Zhi-Wei Liu , Xiaoling Wang , Vijay Gupta

When a new product or technology is introduced, potential consumers can learn its quality by trying the product, at a risk, or by letting others try it and free-riding on the information that they generate. We propose a dynamic game to…

Economics · Quantitative Finance 2017-06-27 Matt V. Leduc , Matthew O. Jackson , Ramesh Johari

We analyze the following group learning problem in the context of opinion diffusion: Consider a network with $M$ users, each facing $N$ options. In a discrete time setting, at each time step, each user chooses $K$ out of the $N$ options,…

Machine Learning · Computer Science 2013-09-17 Yang Liu , Mingyan Liu

The classic Bayesian persuasion model assumes a Bayesian and best-responding receiver. We study a relaxation of the Bayesian persuasion model where the receiver can approximately best respond to the sender's signaling scheme. We show that,…

Computer Science and Game Theory · Computer Science 2024-02-23 Yiling Chen , Tao Lin

Information designers, such as online platforms, often do not know the beliefs of their receivers. We design learning algorithms so that the information designer can learn the receivers' prior belief from their actions through repeated…

Computer Science and Game Theory · Computer Science 2025-10-01 Ce Li , Tao Lin

In performative prediction, the deployment of a predictive model triggers a shift in the data distribution. As these shifts are typically unknown ahead of time, the learner needs to deploy a model to get feedback about the distribution it…

Machine Learning · Computer Science 2022-07-19 Meena Jagadeesan , Tijana Zrnic , Celestine Mendler-Dünner

We propose a framework which generalizes "decision making with structured observations" by allowing robust (i.e. multivalued) models. In this framework, each model associates each decision with a convex set of probability distributions over…

Machine Learning · Computer Science 2025-06-27 Alexander Appel , Vanessa Kosoy

Recent work has demonstrated that problems-- particularly imitation learning and structured prediction-- where a learner's predictions influence the input-distribution it is tested on can be naturally addressed by an interactive approach…

Machine Learning · Computer Science 2014-06-24 Stephane Ross , J. Andrew Bagnell

We consider distributed online learning for joint regret with communication constraints. In this setting, there are multiple agents that are connected in a graph. Each round, an adversary first activates one of the agents to issue a…

Machine Learning · Computer Science 2021-10-26 Dirk van der Hoeven , Hédi Hadiji , Tim van Erven

Persuasion, defined as the act of exploiting an informational advantage in order to effect the decisions of others, is ubiquitous. Indeed, persuasive communication has been estimated to account for almost a third of all economic activity in…

Computer Science and Game Theory · Computer Science 2016-02-16 Shaddin Dughmi , Haifeng Xu

In this paper, we consider a network of processors aiming at cooperatively solving mixed-integer convex programs subject to uncertainty. Each node only knows a common cost function and its local uncertain constraint set. We propose a…

Optimization and Control · Mathematics 2022-07-19 Mohammadreza Chamanbaz , Giuseppe Notarstefano , Francesco Sasso , Roland Bouffanais

Before purchase, a buyer of an experience good learns about the product's fit using various information sources, including some of which the seller may be unaware of. The buyer, however, can conclusively learn the fit only after purchasing…

Computer Science and Game Theory · Computer Science 2020-05-21 Toomas Hinnosaar , Keiichi Kawai

We study a long-run persuasion problem where a long-lived Sender repeatedly interacts with a sequence of short-lived Receivers who may adopt a misspecified model for belief updating. The Sender commits to a stationary information structure,…

Theoretical Economics · Economics 2025-08-05 Hyeonggyun Ko