46 citations · 99 across the 9 of their papers we have counts for
9 papers
Pricing Experiments in Matching Marketplaces under Interference: Designs and Estimators
Arthur Delarue, Kleanthis Karakolios
Interference between treated and untreated units is a source of bias in marketplace experiments. In this paper, we specifically consider pricing interventions, in which a platform…
Adaptive Optimization for Prediction with Missing Data
Dimitris Bertsimas, Arthur Delarue, Jean Pauphilet
When training predictive models on data with missing entries, the most widely used and versatile approach is a pipeline technique where we first impute missing entries and then com…
Solving the Quadratic Assignment Problem using Deep Reinforcement Learning
Puneet S. Bagga, Arthur Delarue
The Quadratic Assignment Problem (QAP) is an NP-hard problem which has proven particularly challenging to solve: unlike other combinatorial problems like the traveling salesman pro…
Reducing Marketplace Interference Bias Via Shadow Prices
Ido Bright, Arthur Delarue, Ilan Lobel
Marketplace companies rely heavily on experimentation when making changes to the design or operation of their platforms. The workhorse of experimentation is the randomized controll…
Simple Imputation Rules for Prediction with Missing Data: Contrasting Theoretical Guarantees with Empirical Performance
Dimitris Bertsimas, Arthur Delarue, Jean Pauphilet
Missing data is a common issue in real-world datasets. This paper studies the performance of impute-then-regress pipelines by contrasting theoretical and empirical evidence. We est…
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing
Arthur Delarue, Ross Anderson, Christian Tjandraatmadja
Value-function-based methods have long played an important role in reinforcement learning. However, finding the best next action given a value function of arbitrary complexity is n…
From predictions to prescriptions: A data-driven response to COVID-19
Dimitris Bertsimas, Léonard Boussioux, Ryan Cory Wright +18
The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a d…
Optimal Explanations of Linear Models
Dimitris Bertsimas, Arthur Delarue, Patrick Jaillet +1
When predictive models are used to support complex and important decisions, the ability to explain a model's reasoning can increase trust, expose hidden biases, and reduce vulnerab…
The Price of Interpretability
Dimitris Bertsimas, Arthur Delarue, Patrick Jaillet +1
When quantitative models are used to support decision-making on complex and important topics, understanding a model's ``reasoning'' can increase trust in its predictions, expose hi…