Publications — Algorithmic Fairness

All Discrete Optimization Machine Learning LLMs Algorithmic Fairness Quantum Computing Combinatorics

2026

Why Global LLM Leaderboards Are Misleading: Small Portfolios for Heterogeneous Supervised ML

Under submission, with Ayela Chughtai, Bhargavi Lanka, Swati Gupta.

Machine Learning LLMs Algorithmic Fairness

Many Preferences, Few Policies: Towards Scalable Language Model Personalization

Under submission, with Cheol Woo Kim, Roozbeh Nahavandi, Andrew Perrault, Milind Tambe, Swati Gupta.

Machine Learning LLMs Algorithmic Fairness

2025

Navigating the Social Welfare Frontier: Portfolios for Multi-objective Reinforcement Learning

ICML, with Cheol Woo Kim, Shresth Verma, Madeleine Pollack, Lingkai Kong, Milind Tambe, Swati Gupta.

Machine Learning Discrete Optimization Algorithmic Fairness LLMs

Balancing Notions of Equity: Trade-offs Between Fair Portfolio Sizes and Achievable Guarantees

SODA, with Swati Gupta and Mohit Singh.

Discrete Optimization Algorithmic Fairness

2023

Which $L_p$ norm is the fairest? Approximations for fair facility location across all “$p$”

Economics and Computation (EC), with Swati Gupta and Mohit Singh.

Discrete Optimization Algorithmic Fairness

2022

Multi Purpose Routing: New Perspectives and Approximation Algorithms

arXiv, with Majid Farhadi, Prasad Tetali, and Alejandro Toriello.

Discrete Optimization Algorithmic Fairness