21 citations · 46 across the 39 of their papers we have counts for
9 papers · 1 filter
The Role of Causality in Algorithmic Recourse
Srikanth Avasarala, Varun Gupta, Shahin Jabbari +2
Algorithmic recourse aims to provide individuals with actionable changes to improve their predicted outcomes in high-stakes classification settings, such as loan and mortgage appli…
KL-regularization Itself is Differentially Private in Bandits and RLHF
Yizhou Zhang, Kishan Panaganti, Laixi Shi +2
Differential Privacy (DP) provides a rigorous framework for privacy, ensuring the outputs of data-driven algorithms remain statistically indistinguishable across datasets that diff…
Bayesian Strategic Classification
Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl +2
In strategic classification, agents modify their features, at a cost, to ideally obtain a positive classification from the learner's classifier. The typical response of the learner…
Personalized Differential Privacy for Ridge Regression
Krishna Acharya, Franziska Boenisch, Rakshit Naidu +1
The increased application of machine learning (ML) in sensitive domains requires protecting the training data through privacy frameworks, such as differential privacy (DP). DP requ…
Oracle Efficient Algorithms for Groupwise Regret
Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan +2
We study the problem of online prediction, in which at each time step , an individual arrives, whose label we must predict. Each individual is associated with various grou…
Randomized Quantization is All You Need for Differential Privacy in Federated Learning
Yeojoon Youn, Zihao Hu, Juba Ziani +1
Federated learning (FL) is a common and practical framework for learning a machine model in a decentralized fashion. A primary motivation behind this decentralized approach is data…
Sequential Strategic Screening
Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl +2
We initiate the study of strategic behavior in screening processes with multiple classifiers. We focus on two contrasting settings: a conjunctive setting in which an individual mus…
Algorithms and Learning for Fair Portfolio Design
Emily Diana, Travis Dick, Hadi Elzayn +5
We consider a variation on the classical finance problem of optimal portfolio design. In our setting, a large population of consumers is drawn from some distribution over risk tole…
Gaming Helps! Learning from Strategic Interactions in Natural Dynamics
Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu +1
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 modify…