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Found 12 papers, 2 papers with code
Can Probabilistic Feedback Drive User Impacts in Online Platforms?
A common explanation for negative user impacts of content recommender systems is misalignment between the platform's objective and user welfare.
Preferences Evolve And So Should Your Bandits: Bandits with Evolving States for Online Platforms
We present online learning algorithms for any possible value of the evolution rate $\lambda$ and we show the robustness of our results to various model misspecifications.
Recommending to Strategic Users
We model this user behavior as a two-stage noisy signalling game between the recommendation system and users: the recommendation system initially commits to a recommendation policy, presents content to the users during a cold start phase which the users choose to strategically consume in order to affect the types of content they will be recommended in a recommendation phase.
Strategyproof Decision-Making in Panel Data Settings and Beyond
Unlike this classical setting, we permit the units generating the panel data to be strategic, i. e. units may modify their pre-intervention outcomes in order to receive a more desirable intervention.
Corruption-Robust Contextual Search through Density Updates
We study the problem of contextual search in the adversarial noise model.
Information Discrepancy in Strategic Learning
We initiate the study of the effects of non-transparency in decision rules on individuals' ability to improve in strategic learning settings.
Adaptive Discretization for Adversarial Lipschitz Bandits
We provide the first algorithm for adaptive discretization in the adversarial version, and derive instance-dependent regret bounds.
Contextual Search in the Presence of Adversarial Corruptions
We initiate the study of contextual search when some of the agents can behave in ways inconsistent with the underlying response model.
No-Regret and Incentive-Compatible Online Learning
First, we want the learning algorithm to be no-regret with respect to the best fixed expert in hindsight.
Learning Strategy-Aware Linear Classifiers
We address the question of repeatedly learning linear classifiers against agents who are strategically trying to game the deployed classifiers, and we use the Stackelberg regret to measure the performance of our algorithms.
Strategyproof Linear Regression in High Dimensions
This paper is part of an emerging line of work at the intersection of machine learning and mechanism design, which aims to avoid noise in training data by correctly aligning the incentives of data sources.
Learning to Bid Without Knowing your Value
We address online learning in complex auction settings, such as sponsored search auctions, where the value of the bidder is unknown to her, evolving in an arbitrary manner and observed only if the bidder wins an allocation.
