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20192025
most citedThe Price of Interpretability

30 citations · 44 across the 3 of their papers we have counts for

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Showing cs.LGShow all

5 papers · 1 filter

cs.LG2024

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…

cs.LG2023

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…

cs.LG2020

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…

cs.LG20192 cited

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…

cs.LG201930 cited

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…