14 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…
A Latent Variable Framework for Scaling Laws in Large Language Models
Peiyao Cai, Chengyu Cui, Felipe Maia Polo +6
We propose a statistical framework built on latent variable modeling for scaling laws of large language models (LLMs). Our work is motivated by the rapid emergence of numerous new…
A Statistical Framework for Learning Preferences from the Past
Tamojit Sadhukhan, Moulinath Banerjee, Krishanu Maulik +1
In many real-world settings such as online recommendation or consumer choice modeling, individuals make repeated choices from a fixed set of options. Accurately estimating their un…
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…