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Found 19 papers, 0 papers with code
Learning Adversarial MDPs with Stochastic Hard Constraints
To the best of our knowledge, our work is the first to study CMDPs involving both adversarial losses and hard constraints.
Markov Persuasion Processes: Learning to Persuade from Scratch
Recently, Markov persuasion processes (MPPs) have been introduced to capture sequential scenarios where a sender faces a stream of myopic receivers in a Markovian environment.
No-Regret Learning in Bilateral Trade via Global Budget Balance
Bilateral trade models the problem of intermediating between two rational agents -- a seller and a buyer -- both characterized by a private valuation for an item they want to trade.
Learning Optimal Contracts: How to Exploit Small Action Spaces
We design an algorithm that learns an approximately-optimal contract with high probability in a number of rounds polynomial in the size of the outcome space, when the number of actions is constant.
Bandits with Replenishable Knapsacks: the Best of both Worlds
The bandits with knapsack (BwK) framework models online decision-making problems in which an agent makes a sequence of decisions subject to resource consumption constraints.
A Best-of-Both-Worlds Algorithm for Constrained MDPs with Long-Term Constraints
We study online learning in episodic constrained Markov decision processes (CMDPs), where the goal of the learner is to collect as much reward as possible over the episodes, while guaranteeing that some long-term constraints are satisfied during the learning process.
Online Learning under Budget and ROI Constraints via Weak Adaptivity
Finally, we show how to instantiate the framework to optimally bid in various mechanisms of practical relevance, such as first- and second-price auctions.
A Unifying Framework for Online Optimization with Long-Term Constraints
We present the first best-of-both-world type algorithm for this general class of problems, with no-regret guarantees both in the case in which rewards and constraints are selected according to an unknown stochastic model, and in the case in which they are selected at each round by an adversary.
Sequential Information Design: Learning to Persuade in the Dark
At each round, the sender observes the realizations of random events in the SDM problem.
Best of Many Worlds Guarantees for Online Learning with Knapsacks
We study online learning problems in which a decision maker wants to maximize their expected reward without violating a finite set of $m$ resource constraints.
Safe Online Bid Optimization with Return-On-Investment and Budget Constraints subject to Uncertainty
More interestingly, we provide an algorithm, namely GCB_{safe}(\psi,\phi), guaranteeing both sublinear pseudo-regret and safety w. h. p.
Multi-Receiver Online Bayesian Persuasion
Then, we focus on the case of submodular sender's utility functions and we show that, in this case, it is possible to design a polynomial-time no-$(1 - \frac{1}{e})$-regret algorithm.
Persuading Voters in District-based Elections
We study both private signaling, in which the sender can use a private communication channel per receiver, and public signaling, in which the sender can use a single communication channel for all the receivers.
Online Bayesian Persuasion
We are interested in no-regret algorithms prescribing a signaling scheme at each round of the repeated interaction with performances close to that of the best-in-hindsight signaling scheme.
Public Bayesian Persuasion: Being Almost Optimal and Almost Persuasive
Unlike prior works on this problem, we study the public persuasion problem in the general setting with: (i) arbitrary state spaces; (ii) arbitrary action spaces; (iii) arbitrary sender's utility functions.
Signaling in Bayesian Network Congestion Games: the Subtle Power of Symmetry
A natural question is the following: is it possible for an informed sender to reduce the overall social cost via the strategic provision of information to players who update their beliefs rationally?
Election Manipulation on Social Networks: Seeding, Edge Removal, Edge Addition
We focus on the election manipulation problem through social influence, where a manipulator exploits a social network to make her most preferred candidate win an election.
Persuading Voters: It's Easy to Whisper, It's Hard to Speak Loud
In the former, we show that an optimal signaling scheme can be computed efficiently both under a $k$-voting rule and plurality voting.
Election Manipulation on Social Networks with Messages on Multiple Candidates
We study the problem of election control through social influence when the manipulator is allowed to use the locations that she acquired on the network for sending \emph{both} positive and negative messages on \emph{multiple} candidates, widely extending the previous results available in the literature that study the influence of a single message on a single candidate.
Computer Science and Game Theory
