71 citations · 138 across the 17 of their papers we have counts for
21 papers
Settling the Approximability of Submodular Facility Location
Adam N. Elmachtoub, Bento Natura, Meike Neuwohner
In submodular facility location (SFL), introduced by Svitkina and Tardos (SODA 2006), clients in a metric space are assigned to facilities . A facility s…
Tight Guarantees for Static Policies in Vehicle Sharing Networks
Adam N. Elmachtoub, Jiaqi Shi
We study pricing in a vehicle sharing network with locations and vehicles, motivated by on-demand bike, scooter, and car sharing, as well as robotaxi and ride sharing servi…
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…
An active learning framework for multi-group mean estimation
Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub
We study a fundamental learning problem over multiple groups with unknown data distributions, where an analyst would like to learn the mean of each group. Moreover, we want to ensu…
Dissecting the Impact of Model Misspecification in Data-driven Optimization
Adam N. Elmachtoub, Henry Lam, Haixiang Lan +1
Data-driven optimization aims to translate a machine learning model into decision-making by optimizing decisions on estimated costs. Such a pipeline can be conducted by fitting a d…
Price and Assortment Optimization under the Multinomial Logit Model with Opaque Products
Omar El Housni, Adam N. Elmachtoub, Harsh Sheth +1
An opaque product is a product for which only partial information is disclosed to the buyer at the time of purchase. Opaque products are common in sectors such as travel and online…
Static Pricing Guarantees for Queueing Systems
Jacob Bergquist, Adam N. Elmachtoub
We consider a general queueing system with price-sensitive customers in which the service provider seeks to balance two objectives, maximizing the average revenue rate and minimizi…
Estimate-Then-Optimize versus Integrated-Estimation-Optimization versus Sample Average Approximation: A Stochastic Dominance Perspective
Adam N. Elmachtoub, Henry Lam, Haofeng Zhang +1
In data-driven stochastic optimization, model parameters of the underlying distribution need to be estimated from data in addition to the optimization task. Recent literature consi…
Balanced Off-Policy Evaluation for Personalized Pricing
Adam N. Elmachtoub, Vishal Gupta, Yunfan Zhao
We consider a personalized pricing problem in which we have data consisting of feature information, historical pricing decisions, and binary realized demand. The goal is to perform…
The Power of Static Pricing for Reusable Resources
Adam N. Elmachtoub, Jiaqi Shi
We consider the problem of pricing a reusable resource service system. Potential customers arrive according to a Poisson process and purchase the service if their valuation exceeds…
Decision Trees for Decision-Making under the Predict-then-Optimize Framework
Adam N. Elmachtoub, Jason Cheuk Nam Liang, Ryan McNellis
We consider the use of decision trees for decision-making problems under the predict-then-optimize framework. That is, we would like to first use a decision tree to predict unknown…
Market Segmentation Trees
Ali Aouad, Adam N. Elmachtoub, Kris J. Ferreira +1
We seek to provide an interpretable framework for segmenting users in a population for personalized decision-making. We propose a general methodology, Market Segmentation Trees (MS…
Generalization Bounds in the Predict-then-Optimize Framework
Othman El Balghiti, Adam N. Elmachtoub, Paul Grigas +1
The predict-then-optimize framework is fundamental in many practical settings: predict the unknown parameters of an optimization problem, and then solve the problem using the predi…
Static Pricing: Universal Guarantees for Reusable Resources
Omar Besbes, Adam N. Elmachtoub, Yunjie Sun
We consider a fundamental pricing model in which a fixed number of units of a reusable resource are used to serve customers. Customers arrive to the system according to a stochasti…
Smart "Predict, then Optimize"
Adam N. Elmachtoub, Paul Grigas
Many real-world analytics problems involve two significant challenges: prediction and optimization. Due to the typically complex nature of each challenge, the standard paradigm is…
Maximizing the Spread of Cascades Using Network Design
Daniel Sheldon, Bistra Dilkina, Adam N. Elmachtoub +8
We introduce a new optimization framework to maximize the expected spread of cascades in networks. Our model allows a rich set of actions that directly manipulate cascade dynamics…