4 citations · 8 across the 12 of their papers we have counts for
18 papers
A Complexity Measure for Active Learning in Multi-group Mean Estimation
Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub
We study a \emph{max-risk} objective for active learning in a multi-group mean estimation -armed bandits: a learner adaptively allocates a budget of samples across group…
Learning Fair Demand Models
Adam N. Elmachtoub, Hyemi Kim, Jonathan Y. Tan
Data-driven pricing is increasingly prevalent in sectors such as airlines, lending, insurance, and retail. By learning demand models from customer features and setting prices accor…
Fair Aggregation in Virtual Power Plants
Liudong Chen, Hyemi Kim, Adam N. Elmachtoub +1
A virtual power plant (VPP) is operated by an aggregator that acts as a market intermediary, aggregating consumers to participate in wholesale power markets. By setting incentive p…
Simple vs. Optimal Congestion Pricing
Devansh Jalota, Xuan Di, Adam N. Elmachtoub
Congestion pricing has emerged as an effective tool for mitigating traffic congestion, yet implementing welfare or revenue-optimal dynamic tolls is often impractical. Most real-wor…
Choice Modeling and Pricing for Scheduled Services
Adam N. Elmachtoub, Kumar Goutam, Roger Lederman
We describe a novel framework for discrete choice modeling and price optimization for settings where scheduled service options (often hierarchical) are offered to customers, which…
The Bias-Variance Tradeoff in Data-Driven Optimization: A Local Misspecification Perspective
Haixiang Lan, Luofeng Liao, Adam N. Elmachtoub +3
Data-driven stochastic optimization is ubiquitous in machine learning and operational decision-making problems. Sample average approximation (SAA) and model-based approaches such a…