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Found 34 papers, 6 papers with code
Multi-Sender Persuasion -- A Computational Perspective
The core solution concept here is the Nash equilibrium of senders' signaling policies.
Incentivized Truthful Communication for Federated Bandits
To enhance the efficiency and practicality of federated bandit learning, recent advances have introduced incentives to motivate communication among clients, where a client participates only when the incentive offered by the server outweighs its participation cost.
Learning in Online Principal-Agent Interactions: The Power of Menus
We study a ubiquitous learning challenge in online principal-agent problems during which the principal learns the agent's private information from the agent's revealed preferences in historical interactions.
Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation
We study a strategic variant of the multi-armed bandit problem, which we coin the strategic click-bandit.
Sample Complexity of Opinion Formation on Networks
In this paper, we initialize the study of sample complexity in opinion formation to solve this problem.
A Data-Centric Online Market for Machine Learning: From Discovery to Pricing
We envision that the synergy of our data and model discovery algorithm and pricing mechanism will be an important step towards building a new data-centric online market that serves ML users effectively.
How Bad is Top-$K$ Recommendation under Competing Content Creators?
Content creators compete for exposure on recommendation platforms, and such strategic behavior leads to a dynamic shift over the content distribution.
Multi-Player Bandits Robust to Adversarial Collisions
We provide the first decentralized and robust algorithm RESYNC for defenders whose performance deteriorates gracefully as $\tilde{O}(C)$ as the number of collisions $C$ from the attackers increases.
CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification
Our theoretical analysis shows the proposed value function is (essentially) the unique function that satisfies two desirable properties for evaluating data values in classification.
Learning Correlated Stackelberg Equilibrium in General-Sum Multi-Leader-Single-Follower Games
Many real-world strategic games involve interactions between multiple players.
Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning
To understand the security threats to reinforcement learning (RL) algorithms, this paper studies poisoning attacks to manipulate \emph{any} order-optimal learning algorithm towards a targeted policy in episodic RL and examines the potential damage of two natural types of poisoning attacks, i. e., the manipulation of \emph{reward} and \emph{action}.
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards
Incrementality, which is used to measure the causal effect of showing an ad to a potential customer (e. g. a user in an internet platform) versus not, is a central object for advertisers in online advertising platforms.
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning
This paper proposes a novel model of sequential information design, namely the Markov persuasion processes (MPPs), where a sender, with informational advantage, seeks to persuade a stream of myopic receivers to take actions that maximizes the sender's cumulative utilities in a finite horizon Markovian environment with varying prior and utility functions.
Online Bayesian Recommendation with No Regret
For each user with her own private preference and belief, the platform commits to a recommendation strategy to utilize his information advantage on the product state to persuade the self-interested user to follow the recommendation.
Learning from a Learning User for Optimal Recommendations
In real-world recommendation problems, especially those with a formidably large item space, users have to gradually learn to estimate the utility of any fresh recommendations from their experience about previously consumed items.
Least Square Calibration for Peer Reviews
Peer review systems such as conference paper review often suffer from the issue of miscalibration.
Uncoupled Bandit Learning towards Rationalizability: Benchmarks, Barriers, and Algorithms
Under the uncoupled learning setup, the last-iterate convergence guarantee towards Nash equilibrium is shown to be impossible in many games.
(Almost) Free Incentivized Exploration from Decentralized Learning Agents
In this work, we break this barrier and study incentivized exploration with multiple and long-term strategic agents, who have more complicated behaviors that often appear in real-world applications.
Least Square Calibration for Peer Review
Peer review systems such as conference paper review often suffer from the issue of miscalibration.
When Are Linear Stochastic Bandits Attackable?
We study adversarial attacks on linear stochastic bandits: by manipulating the rewards, an adversary aims to control the behaviour of the bandit algorithm.
Learning the Optimal Recommendation from Explorative Users
We propose a new problem setting to study the sequential interactions between a recommender system and a user.
Algorithmic Information Design in Multi-Player Games: Possibility and Limits in Singleton Congestion
This paper initiates the algorithmic information design of both \emph{public} and \emph{private} signaling in a fundamental class of games with negative externalities, i. e., singleton congestion games, with wide application in today's digital economy, machine scheduling, routing, etc.
Diffusion Source Identification on Networks with Statistical Confidence
Diffusion source identification on networks is a problem of fundamental importance in a broad class of applications, including rumor controlling and virus identification.
Incentivizing Exploration in Linear Bandits under Information Gap
We study the problem of incentivizing exploration for myopic users in linear bandits, where the users tend to exploit arm with the highest predicted reward instead of exploring.
Learning to Persuade on the Fly: Robustness Against Ignorance
We study a repeated persuasion setting between a sender and a receiver, where at each time $t$, the sender observes a payoff-relevant state drawn independently and identically from an unknown prior distribution, and shares state information with the receiver, who then myopically chooses an action.
Saving Stochastic Bandits from Poisoning Attacks via Limited Data Verification
In particular, for the case of unlimited verifications, we show that with $O(\log T)$ expected number of verifications, a simple modified version of the ETC type bandit algorithm can restore the order optimal $O(\log T)$ regret irrespective of the amount of contamination used by the attacker.
PAC-Learning for Strategic Classification
In this paper, we generalize both of these through a unified framework for strategic classification, and introduce the notion of strategic VC-dimension (SVC) to capture the PAC-learnability in our general strategic setup.
Collapsing Bandits and Their Application to Public Health Intervention
Our main contributions are as follows: (i) Building on the Whittle index technique for RMABs, we derive conditions under which the Collapsing Bandits problem is indexable.
Collapsing Bandits and Their Application to Public Health Interventions
(ii) We exploit the optimality of threshold policies to build fast algorithms for computing the Whittle index, including a closed-form.
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation
We prove that all three algorithms achieve a regret upper bound $\mathcal{O}(\max \{ B, K\ln T\})$ where $B$ is the total budget across arms, $K$ is the total number of arms and $T$ is length of the time horizon.
Human Spermbots for Cancer-Relevant Drug Delivery
Here, we successfully load human sperm with a chemotherapeutic drug and perform treatment of relevant 3D cervical cancer and patient-representative 3D ovarian cancer cell cultures, resulting in strong anti-cancer effects.
Mitigating the Curse of Correlation in Security Games by Entropy Maximization
To mitigate this issue, we propose to design entropy-maximizing defending strategies for spatio-temporal security games, which frequently suffer from CoC.
Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty - An Extended Version
This paper presents HEALER, a software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth.
Security Games with Information Leakage: Modeling and Computation
Our experiments confirm the necessity of handling information leakage and the advantage of our algorithms.
