activity
20132026
most citedInference on Auctions with Weak Assumptions on Information

21 citations · 46 across the 39 of their papers we have counts for

collaborators

45 papers

cs.GT2026

Linear Last-Iterate Convergence Rates for Optimistic Learning in Harmonic Games

John Lazarsfeld, Anas Barakat, Jacob Abernethy +4

We prove that the last iterate of Optimistic Follow-The-Regularized-Leader (OFTRL) converges at a linear rate to a Nash equilibrium in multiplayer harmonic games. Our result holds…

cs.LG2026

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…

cs.GT2026

Effort Matters in Score-Based Admissions: How Retaking and Aggregation Shape Test Scores

Christine Ling, Diptangshu Sen, Juba Ziani

Observed standardized test scores are the result of an endogenous process: students strategically allocate effort across multiple retake attempts to improve their outcomes. Because…

cs.GT2026

The Impact of Competition on Outcomes of Score-Based College Admissions

George Bentley, Diptangshu Sen, Juba Ziani

We study how the design of admissions policies affects the ability of students admitted to universities. In our model, applicants have a multi-dimensional ability, which is a combi…

cs.IT2026

Local Differential Privacy with Correlated Noise Achieves Central-DP Optimal Cost

Madhura Pathegama, Srikanth Avasarala, Viveck R. Cadambe +1

We study privately estimating the sum of user-held values in the presence of an honest-but-curious server. This motivates requiring privacy not only at data release but also th…

cs.CR2026

Limits of Personalizing Differential Privacy Budgets

Edwige Cyffers, Juba Ziani

A key technical difficulty in differential privacy is selecting a privacy budget that satisfies privacy requirements while maximizing utility. A natural and well-studied workaround…

cs.GT2026

Data Sharing with Endogenous Choices over Differential Privacy Levels

Raef Bassily, Kate Donahue, Diptangshu Sen +2

Motivated by the rapid push to decentralize sharing of data, we study whether large-scale data sharing coalitions can form in a decentralized manner under differential privacy when…

cs.CY2025

Fixed Points and Stochastic Meritocracies: A Long-Term Perspective

Gaurab Pokharel, Diptangshu Sen, Sanmay Das +1

We study group fairness in the context of feedback loops induced by meritocratic selection into programs that themselves confer additional advantage, like college admissions. We in…

math.OC2025

Finding a Multiple Follower Stackelberg Equilibrium: A Fully First-Order Method

April Niu, Kai Wang, Juba Ziani

In this work, we propose the first fully first-order method to compute an epsilon stationary Stackelberg equilibrium with convergence guarantees. To achieve this, we first reframe…

cs.IR2025

GLoSS: Generative Language Models with Semantic Search for Sequential Recommendation

Krishna Acharya, Aleksandr V. Petrov, Juba Ziani

We propose Generative Low-rank language model with Semantic Search (GLoSS), a generative recommendation framework that combines large language models with dense retrieval for seque…

cs.GT2025

The Disparate Effects of Partial Information in Bayesian Strategic Learning

Srikanth Avasarala, Serena Wang, Juba Ziani

We study how partial information about scoring rules affects fairness in strategic learning settings. In strategic learning, a learner deploys a scoring rule, and agents respond st…

cs.CR2025

Optimal Allocation of Privacy Budget on Hierarchical Data Release

Joonhyuk Ko, Juba Ziani, Ferdinando Fioretto

Releasing useful information from datasets with hierarchical structures while preserving individual privacy presents a significant challenge. Standard privacy-preserving mechanisms…

cs.LG2025

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…

cs.GT2025

Incentivizing Desirable Effort Profiles in Strategic Classification: The Role of Causality and Uncertainty

Valia Efthymiou, Chara Podimata, Diptangshu Sen +1

We study strategic classification in binary decision-making settings where agents can modify their features in order to improve their classification outcomes. Importantly, our work…

cs.GT2025

Last-iterate Convergence for Symmetric, General-sum, Games Under The Exponential Weights Dynamic

Guanghui Wang, Krishna Acharya, Lokranjan Lakshmikanthan +2

We conduct a comprehensive analysis of the discrete-time exponential-weights dynamic with a constant step size on all general-sum and symmetric normal-form games, i.e.…

cs.CY2025

When Should a Principal Delegate to an Agent in Selection Processes?

Benjamin Fish, Diptangshu Sen, Juba Ziani

Decision-makers in high-stakes selection processes often face a fundamental choice: whether to make decisions themselves or to delegate authority to another entity whose incentives…

cs.GT2025

The Cost of Balanced Training-Data Production in an Online Data Market

Augustin Chaintreau, Roland Maio, Juba Ziani

Many ethical issues in machine learning are connected to the training data. Online data markets are an important source of training data, facilitating both production and distribut…

cs.CR2024

Differential Privacy Overview and Fundamental Techniques

Ferdinando Fioretto, Pascal Van Hentenryck, Juba Ziani

This chapter is meant to be part of the book "Differential Privacy in Artificial Intelligence: From Theory to Practice" and provides an introduction to Differential Privacy. It sta…

cs.GT2024★ 2 cited

Algorithmic Collusion Without Threats

Eshwar Ram Arunachaleswaran, Natalie Collina, Sampath Kannan +2

There has been substantial recent concern that pricing algorithms might learn to ``collude.'' Supra-competitive prices can emerge as a Nash equilibrium of repeated pricing games, i…

cs.CR2024

Differentially Private Data Release on Graphs: Inefficiencies and Unfairness

Ferdinando Fioretto, Diptangshu Sen, Juba Ziani

Networks are crucial components of many sectors, including telecommunications, healthcare, finance, energy, and transportation.The information carried in such networks often contai…

cs.GT2024

On Rider Strategic Behavior in Ride-Sharing Platforms

Jay Mulay, Diptangshu Sen, Juba Ziani

Over the past decade, ride-sharing services have become increasingly important, with U.S. market leaders such as Uber and Lyft expanding to over 900 cities worldwide and facilitati…

cs.CR2024

Fairness Issues and Mitigations in (Differentially Private) Socio-Demographic Data Processes

Joonhyuk Ko, Juba Ziani, Saswat Das +2

Statistical agencies rely on sampling techniques to collect socio-demographic data crucial for policy-making and resource allocation. This paper shows that surveys of important soc…

cs.GT2024

Equilibria of Data Marketplaces with Privacy-Aware Sellers under Endogenous Privacy Costs

Diptangshu Sen, Jingyan Wang, Juba Ziani

We study a two-sided online data ecosystem comprised of an online platform, users on the platform, and downstream learners or data buyers. The learners can buy user data on the pla…

cs.LG2024

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…