output
20012024
most citedGenerative artificial intelligence enhances creativity but reduces the diversity of novel content

714 citations

17 papers

cs.LG2024★ 8 cited

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.HC2023★ 714 cited

Generative artificial intelligence enhances creativity but reduces the diversity of novel content

Anil R. Doshi, Oliver P. Hauser

Creativity is core to being human. Generative artificial intelligence (GenAI) holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on G…

cs.LG2023★ 12 cited

Patient Outcome Predictions Improve Operations at a Large Hospital Network

Liangyuan Na, Kimberly Villalobos Carballo, Jean Pauphilet +8

Problem definition: Access to accurate predictions of patients' outcomes can enhance medical staff's decision-making, which ultimately benefits all stakeholders in the hospitals. A…

math.OC2023★ 120 cited

Operational Research: Methods and Applications

Fotios Petropoulos, Gilbert Laporte, Emel Aktas +79

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This…

math.OC2023★ 6 cited

A Stochastic Benders Decomposition Scheme for Large-Scale Stochastic Network Design

Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet +1

Network design problems involve constructing edges in a transportation or supply chain network to minimize construction and daily operational costs. We study a stochastic version w…

econ.GN2022★ 34 cited

The Rise of Age-Friendly Jobs

Daron Acemoglu, Nicolaj Søndergaard Mühlbach, Andrew J. Scott

In 1990, one in five U.S. workers were aged over 50 years whereas today it is one in three. One possible explanation for this is that occupations have become more accommodating to…

math.OC2022★ 8 cited

Sparse PCA With Multiple Components

Ryan Cory-Wright, Jean Pauphilet

Sparse Principal Component Analysis (sPCA) is a cardinal technique for obtaining combinations of features, or principal components (PCs), that explain the variance of high-dimensio…

cs.HC2022★ 12 cited

The Best Decisions Are Not the Best Advice: Making Adherence-Aware Recommendations

Julien Grand-Clément, Jean Pauphilet

Many high-stake decisions follow an expert-in-loop structure in that a human operator receives recommendations from an algorithm but is the ultimate decision maker. Hence, the algo…

stat.ME2021

Robust and Heterogenous Odds Ratio: Estimating Price Sensitivity for Unbought Items

Jean Pauphilet

Problem definition: Mining for heterogeneous responses to an intervention is a crucial step for data-driven operations, for instance to personalize treatment or pricing. We investi…

math.OC2021★ 8 cited

A new perspective on low-rank optimization

Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet

A key question in many low-rank problems throughout optimization, machine learning, and statistics is to characterize the convex hulls of simple low-rank sets and judiciously apply…

econ.TH2021★ 13 cited

A Pricing Mechanism to Jointly Mitigate Market Power and Environmental Externalities in Electricity Markets

Lamia Varawala, Mohammad Reza Hesamzadeh, György Dán +2

The electricity industry has been one of the first to face technological changes motivated by sustainability concerns. Whilst efficiency aspects of market design have tended to foc…

math.OC2020★ 16 cited

Mixed-Projection Conic Optimization: A New Paradigm for Modeling Rank Constraints

Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet

We propose a framework for modeling and solving low-rank optimization problems to certifiable optimality. We introduce symmetric projection matrices that satisfy , the matri…

econ.EM2020★ 64 cited

A Model of the Fed's View on Inflation

Thomas Hasenzagl, Filippo Pellegrino, Lucrezia Reichlin +1

We develop a medium-size semi-structural time series model of inflation dynamics that is consistent with the view - often expressed by central banks - that three components are imp…

math.OC2020★ 11 cited

Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality

Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet

Sparse principal component analysis (PCA) is a popular dimensionality reduction technique for obtaining principal components which are linear combinations of a small subset of the…

stat.AP2020★ 19 cited

Forecasting the Intra-Day Spread Densities of Electricity Prices

Ekaterina Abramova, Derek Bunn

Intra-day price spreads are of interest to electricity traders, storage and electric vehicle operators. This paper formulates dynamic density functions, based upon skewed-t and sim…

math.OC2017★ 34 cited

Sparse Classification: a scalable discrete optimization perspective

Dimitris Bertsimas, Jean Pauphilet, Bart Van Parys

We formulate the sparse classification problem of samples with features as a binary convex optimization problem and propose a cutting-plane algorithm to solve it exactly. F…

cs.CY2001★ 1 cited

Standardization versus Coverage in Wireless Telephone Networks

Neil Gandal, David Salant, Leonard Waverman

The issue of market-based versus mandated standards has been addressed in many settings. In most settings in which network effects are present, compatibility across platforms has b…