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Interpretable AI-driven Guidelines for Type 2 Diabetes Treatment from Observational Data

with D. K. Agarwal, submitted to Diabetes Care, 2025

The R.O.A.D. to clinical trial emulation

with A. Koulouras, H. Nagata, C. Gao, J. Mizusawa, Y. Kanemitsu, and G. A. Margonis, submitted to Nature, 2025

A Machine Learning Approach to Two-Stage Adaptive Robust Optimization

with Cheol Woo Kim, European Journal of Operational Research, 2024

An exact method for a class of robust nonlinear optimization problems

with D. de Moor, D. den Hertog, T. Koukouvinos, J. Zhen, submitted to SIAM Journal on Optimization, 2024

Binary Classification: Is Boosting stronger than Bagging?

with V. Stoumpou, submitted to Machine Learning, 2024

Catastrophe Insurance: An Adaptive Robust Optimization Approach

with C. Zeng, submitted to MSOM, 2024

Compressed Sensing: A Discrete Optimization Approach

with N. Johnson, Machine Learning, appeared online, 2024

Constructing Uncertainty Sets from Covariates in Power Systems

with T. Koukouvinos, A. G. Koulouras, accepted by IEEE Transactions on Power Systems, 2024

Deep Trees for (Un)structured Data: Tractability, Performance, and Interpretability

with L. Everest, J. Gu, M. Peroni, V. Stoumpou, submitted to Machine Learning, 2024

Global Optimization: A Machine Learning Approach

with G. Margaritis, to appear in Journal of Global Optimization, 2024

Holistic Deep Learning

with L. Boussioux, K. Villalobos Carballo, M. Li, A. Paskov and I. Paskov, Machine Learning, 113, 159-183, 2024

M3H: Multimodal Multitask Machine Learning for Healthcare

with Y. Ma, submitted to Nature, 2024

Multimodal Prescriptive Deep Learning

with L. Everest and V. Stoumpou, submitted to Transactions on Machine Learning Research, 2024

Optimal Control of Fluid Restless Multi-armed Bandits: A Machine Learning Approach

with C.W. Kim and J. Nino-Mora, submitted to Machine Learning, 2024

Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning

with M. Peroni, submitted to Journal of Machine Learning Research, 2024

Simple Imputation Rules for Prediction with Missing Data: Theoretical Guarantees vs. Empirical Performance

with A. Delarue, J. Pauphillet, to appear in Transactions on Machine Learning, 2024

Slowly Varying Regression Under Sparsity

with V. Digalakis, M. Li and O. Skali Lami, Operations Research, appeared online, 2024

Solving Large-Scale Weapon Target Assignment Problems in Seconds

with A. Paskov, submitted to Naval Research Logistics, 2024

The R.O.A.D. to Precision Medicine

with A. Koulouras and G. A. Margonis, Nature Digital Medicine, 7, 307, 2024

Towards Optimal Valve Prescription for Transcatheter Aortic Valve Replacement (TAVR) Surgery: A Machine Learning Approach

with P. Paschalidis, V. Stoumpou, L. Everest, Y. Ma, T. Azemi, J. Haider, S. Zweibel, E. M. Protopapas, J.Mather, M. Tysarowski, G. E. Sarris, R. C. Hagberg, H. L. Haronian, submitted to Healthcare Management Science, 2024

Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences

with V. Digalakis, Y. Ma, P. Paschalidis, submitted to NeurIPS, 2024

Universal Neurons in GPT2 Language Models

with Wes Gurnee, Theo Horsley, Zifan Carl Guo, Tara Rezaei Kheirkhah, Qinyi Sun, Will Hathaway, Neel Nanda, to appear in Transactions on Machine Learning Research, 2024

A New Perspective on Low-Rank Optimization

with Ryan Cory-Wright, Jean Pauphilet, Mathematical Programming, 202, 47-92, 2023

A Prescriptive Machine Learning Approach to Mixed-Integer Convex Optimization

with Cheol Woo Kim, INFORMS Journal on Computing, 2023

A Simulated Annealing Approach to Designing Optimal Policy Trees

with Suleeporn Sujichantararat, submitted to Machine Learning, 2023