BigOptiBase: Big Data Analytics for Base Station Energy Consumption Optimization. - Evdokia Kassela; Nikodimos Provatas; Asterios Tsiourvas; Ioannis Konstantinou; Nectarios Koziris (IEEE BigData, 2019)

A Mechanism Design and Learning Approach for Revenue Maximization on Cloud Dynamic Spot Markets. - Asterios Tsiourvas; Constantinos Bitsakos; Ioannis Konstantinou; Dimitris Fotakis 001; Nectarios Koziris (CLOUD, 2021)

Optimizing Objective Functions from Trained ReLU Neural Networks via Sampling. - Georgia Perakis; Asterios Tsiourvas (CoRR, 2022)

Learning Prescriptive ReLU Networks. - Wei Sun; Asterios Tsiourvas (ICML, 2023)

Discrete gradient flow approximations of high dimensional evolution partial differential equations via deep neural networks. - Emmanuil H. Georgoulis; Michail Loulakis; Asterios Tsiourvas (Commun. Nonlinear Sci. Numer. Simul., 2023)

COVID-19: Prediction, Prevalence, and the Operations of Vaccine Allocation. - Amine Bennouna; Joshua Joseph; David Nze-Ndong; Georgia Perakis; Divya Singhvi; Omar Skali Lami; Yannis Spantidakis; Leann Thayaparan; Asterios Tsiourvas (Manuf. Serv. Oper. Manag., 2023)

Manifold-Aligned Counterfactual Explanations for Neural Networks. - Asterios Tsiourvas; Wei Sun 031; Georgia Perakis (AISTATS, 2024)

Learning Optimal Projection for Forecast Reconciliation of Hierarchical Time Series. - Asterios Tsiourvas; Wei Sun 031; Georgia Perakis; Pin-Yu Chen; Yada Zhu (ICML, 2024)

Overcoming the Optimizer's Curse: Obtaining Realistic Prescriptions from Neural Networks. - Asterios Tsiourvas; Georgia Perakis (ICML, 2024)

Causal LLM Routing: End-to-End Regret Minimization from Observational Data. - Asterios Tsiourvas; Wei Sun 031; Georgia Perakis (CoRR, 2025)