Search Results for author:
Found 11 papers, 2 papers with code
Bayesian Strategic Classification
We initiate the study of partial information release by the learner in strategic classification.
Personalized Differential Privacy for Ridge Regression
DP requires to specify a uniform privacy level $\varepsilon$ that expresses the maximum privacy loss that each data point in the entire dataset is willing to tolerate.
Oracle Efficient Algorithms for Groupwise Regret
Our approach gives similar regret guarantees compared to [Blum & Lykouris]; however, we run in time linear in the number of groups, and are oracle-efficient in the hypothesis class.
Randomized Quantization is All You Need for Differential Privacy in Federated Learning
To the best of our knowledge, this is the first study that solely relies on randomized quantization without incorporating explicit discrete noise to achieve Renyi DP guarantees in Federated Learning systems.
Sequential Strategic Screening
We initiate the study of strategic behavior in screening processes with multiple classifiers.
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.
Algorithms and Learning for Fair Portfolio Design
We consider a variation on the classical finance problem of optimal portfolio design.
Gaming Helps! Learning from Strategic Interactions in Natural Dynamics
We consider an online regression setting in which individuals adapt to the regression model: arriving individuals are aware of the current model, and invest strategically in modifying their own features so as to improve the predicted score that the current model assigns to them.
Pipeline Interventions
We consider two objectives: social welfare maximization, and a fairness-motivated maximin objective that seeks to maximize the value to the population (starting node) with the \emph{least} expected value.
Downstream Effects of Affirmative Action
We show that both goals can be achieved when the college does not report grades.
Inference on Auctions with Weak Assumptions on Information
Given a sample of bids from independent auctions, this paper examines the question of inference on auction fundamentals (e. g. valuation distributions, welfare measures) under weak assumptions on information structure.
