7 papers
Maximin Relative Improvement: Fair Learning as a Bargaining Problem
Jiwoo Han, Moulinath Banerjee, Yuekai Sun
When deploying a single predictor across multiple subpopulations, we propose a fundamentally different approach: interpreting group fairness as a bargaining problem among subpopula…
Online Price Competition under Generalized Linear Demands
Daniele Bracale, Moulinath Banerjee, Cong Shi +1
We study a sequential price competition among sellers, each influenced by the pricing decisions of their rivals. Specifically, the demand function for each seller follows t…
Revenue Maximization Under Sequential Price Competition Via The Estimation Of s-Concave Demand Functions
Daniele Bracale, Moulinath Banerjee, Cong Shi +1
We consider price competition among multiple sellers over a selling horizon of periods. In each period, sellers simultaneously offer their prices (which are made public) and su…
Dynamic Pricing in the Linear Valuation Model using Shape Constraints
Daniele Bracale, Moulinath Banerjee, Yuekai Sun +2
We propose a shape-constrained approach to dynamic pricing for censored data in the linear valuation model eliminating the need for tuning parameters commonly required by existing…
Bridging Human and LLM Judgments: Understanding and Narrowing the Gap
Felipe Maia Polo, Xinhe Wang, Mikhail Yurochkin +3
Large language models are increasingly used as judges (LLM-as-a-judge) to evaluate model outputs at scale, but their assessments often diverge systematically from human judgments.…
Optimal Intervention for Self-triggering Spatial Networks with Application to Urban Crime Analytics
Pramit Das, Moulinath Banerjee, Yuekai Sun
In many network systems, events at one node trigger further activity at other nodes, e.g., social media users reacting to each other's posts or the clustering of criminal activity…