Bertsimas, Dimitris

35 publications

TMLR 2026 Prescribe-Then-Select: Adaptive Policy Selection for Contextual Stochastic Optimization Caio de Próspero Iglesias, Kimberly Villalobos Carballo, Dimitris Bertsimas

MLJ 2025 Adaptive Optimization for Prediction with Missing Data Dimitris Bertsimas, Arthur Delarue, Jean Pauphilet

TMLR 2025 Sparse Multiple Kernel Learning: Alternating Best Response and Semidefinite Relaxations Dimitris Bertsimas, Caio de Próspero Iglesias, Nicholas A. G. Johnson

MLJ 2024 Compressed Sensing: A Discrete Optimization Approach Dimitris Bertsimas, Nicholas A. G. Johnson

MLJ 2024 Holistic Deep Learning Dimitris Bertsimas, Kimberly Villalobos Carballo, Léonard Boussioux, Michael Lingzhi Li, Alex Paskov, Ivan S. Paskov

JMLR 2024 Interpretable Algorithmic Fairness in Structured and Unstructured Data Hari Bandi, Dimitris Bertsimas, Thodoris Koukouvinos, Sofie Kupiec

NeurIPSW 2024 M3H: Multimodal Multitask Machine Learning for Healthcare Dimitris Bertsimas, Yu Ma

NeurIPSW 2024 Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning Matthew Peroni, Dimitris Bertsimas

TMLR 2024 Simple Imputation Rules for Prediction with Missing Data: Theoretical Guarantees vs. Empirical Performance Dimitris Bertsimas, Arthur Delarue, Jean Pauphilet

NeurIPSW 2024 Skip Transformers: Efficient Inference Through Skip-Routing Matthew Peroni, Dimitris Bertsimas

TMLR 2024 Universal Neurons in GPT2 Language Models Wes Gurnee, Theo Horsley, Zifan Carl Guo, Tara Rezaei Kheirkhah, Qinyi Sun, Will Hathaway, Neel Nanda, Dimitris Bertsimas

NeurIPSW 2023 Fast and Scalable Inference of Dynamical Systems via Integral Matching Baptiste T Rossi, Dimitris Bertsimas

TMLR 2023 Finding Neurons in a Haystack: Case Studies with Sparse Probing Wes Gurnee, Neel Nanda, Matthew Pauly, Katherine Harvey, Dmitrii Troitskii, Dimitris Bertsimas

JMLR 2023 Sparse PCA: A Geometric Approach Dimitris Bertsimas, Driss Lahlou Kitane

JMLR 2023 Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach Dimitris Bertsimas, Ryan Cory-Wright, Nicholas A. G. Johnson

MLJ 2023 Tensor Completion with Noisy Side Information Dimitris Bertsimas, Colin Pawlowski

MLJ 2022 Optimal Survival Trees Dimitris Bertsimas, Jack Dunn, Emma Gibson, Agni Orfanoudaki

JMLR 2022 Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet

JMLR 2022 Stable Classification Dimitris Bertsimas, Jack Dunn, Ivan Paskov

MLJ 2022 The Backbone Method for Ultra-High Dimensional Sparse Machine Learning Dimitris Bertsimas, Vassilis Digalakis

MLJ 2022 World-Class Interpretable Poker Dimitris Bertsimas, Alex Paskov

MLJ 2021 Imputation of Clinical Covariates in Time Series Dimitris Bertsimas, Agni Orfanoudaki, Colin Pawlowski

MLJ 2021 Interpretable Clustering: An Optimization Approach Dimitris Bertsimas, Agni Orfanoudaki, Holly M. Wiberg

MLJ 2021 Sparse Classification: A Scalable Discrete Optimization Perspective Dimitris Bertsimas, Jean Pauphilet, Bart P. G. Van Parys

MLJ 2021 The Voice of Optimization Dimitris Bertsimas, Bartolomeo Stellato

JMLR 2020 Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion Dimitris Bertsimas, Michael Lingzhi Li

MLHC 2020 Optimizing Influenza Vaccine Composition: From Predictions to Prescriptions Hari Bandi, Dimitris Bertsimas

MLJ 2020 Sparse Hierarchical Regression with Polynomials Dimitris Bertsimas, Bart P. G. Van Parys

JMLR 2020 Stable Regression: On the Power of Optimization over Randomization Dimitris Bertsimas, Ivan Paskov

NeurIPS 2018 Optimization over Continuous and Multi-Dimensional Decisions with Observational Data Dimitris Bertsimas, Christopher McCord

JMLR 2017 Certifiably Optimal Low Rank Factor Analysis Dimitris Bertsimas, Martin S. Copenhaver, Rahul Mazumder

MLJ 2017 Optimal Classification Trees Dimitris Bertsimas, Jack Dunn

NeurIPS 2012 An Integer Optimization Approach to Associative Classification Dimitris Bertsimas, Allison Chang, Cynthia Rudin

MLJ 1999 Estimation of Time-Varying Parameters in Statistical Models: An Optimization Approach Dimitris Bertsimas, David Gamarnik, John N. Tsitsiklis

COLT 1997 Estimation of Time-Varying Parameters in Statistical Models: An Optimization Approach Dimitris Bertsimas, David Gamarnik, John N. Tsitsiklis