Scalable reinforcement-learning-based neural architecture search for cancer deep learning research. - Prasanna Balaprakash; Romain Egele; Misha Salim; Stefan M. Wild; Venkatram Vishwanath; Fangfang Xia; Tom Brettin; Rick Stevens (SC, 2019)

Recurrent neural network architecture search for geophysical emulation. - Romit Maulik; Romain Egele; Bethany Lusch; Prasanna Balaprakash (SC, 2020)

AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data. - Romain Egele; Prasanna Balaprakash; Venkatram Vishwanath; Isabelle Guyon; Zhengying Liu (CoRR, 2020)

AgEBO-tabular: joint neural architecture and hyperparameter search with autotuned data-parallel training for tabular data. - Romain Égelé; Prasanna Balaprakash; Isabelle Guyon; Venkatram Vishwanath; Fangfang Xia; Rick Stevens; Zhengying Liu (SC, 2021)

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. - Romain Egele; Romit Maulik; Krishnan Raghavan; Prasanna Balaprakash; Bethany Lusch (CoRR, 2021)

HPC Storage Service Autotuning Using Variational- Autoencoder -Guided Asynchronous Bayesian Optimization. - Matthieu Dorier; Romain Egele; Prasanna Balaprakash; Jaehoon Koo; Sandeep Madireddy; Srinivasan Ramesh; Allen D. Malony; Robert B. Ross (CLUSTER, 2022)

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. - Romain Egele; Romit Maulik; Krishnan Raghavan; Bethany Lusch; Isabelle Guyon; Prasanna Balaprakash (ICPR, 2022)

Asynchronous Distributed Bayesian Optimization at HPC Scale. - Romain Egele; Joceran Gouneau; Venkatram Vishwanath; Isabelle Guyon; Prasanna Balaprakash (CoRR, 2022)

Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization. - Romain Égelé; Isabelle Guyon; Venkatram Vishwanath; Prasanna Balaprakash (e-Science, 2023)

Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization? - Romain Egele; Isabelle Guyon; Yixuan Sun; Prasanna Balaprakash (ESANN, 2023)

Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles. - Romit Maulik; Romain Egele; Krishnan Raghavan; Prasanna Balaprakash (CoRR, 2023)

Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives. - Romain Egele; Tyler H. Chang; Yixuan Sun; Venkatram Vishwanath; Prasanna Balaprakash (CoRR, 2023)

Optimizing Distributed Training on Frontier for Large Language Models. - Sajal Dash; Isaac Lyngaas; Junqi Yin; Xiao Wang 004; Romain Egele; Guojing Cong; Feiyi Wang; Prasanna Balaprakash (CoRR, 2023)

A Historical Handwritten Dataset for Ethiopic OCR with Baseline Models and Human-Level Performance. - Birhanu Hailu Belay; Isabelle Guyon; Tadele Mengiste; Bezawork Tilahun; Marcus Liwicki; Tesfa Tegegne; Romain Egele (ICDAR (3), 2024)

Optimizing Distributed Training on Frontier for Large Language Models. - Sajal Dash; Isaac Lyngaas; Junqi Yin; Xiao Wang 004; Romain Egele; J. Austin Ellis; Matthias Maiterth; Guojing Cong; Feiyi Wang; Prasanna Balaprakash (ISC, 2024)

Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach. - Yixuan Sun; Olaperi Yeside Sowunmi; Romain Egele; Sri Hari Krishna Narayanan; Luke Van Roekel; Prasanna Balaprakash (CoRR, 2024)

AI Competitions and Benchmarks: Dataset Development. - Romain Egele; Júlio C. S. Jacques Júnior; Jan N. van Rijn; Isabelle Guyon; Xavier Baró; Albert Clapés; Prasanna Balaprakash; Sergio Escalera; Thomas B. Moeslund; Jun Wan 001 (CoRR, 2024)

Refining computer tomography data with super-resolution networks to increase the accuracy of respiratory flow simulations. - Xin Liu; Mario Rüttgers; Alessio Quercia; Romain Egele; Elisabeth Pfaehler; Rushikesh Shende; Marcel Aach; Wolfgang Schröder 001; Prasanna Balaprakash; Andreas Lintermann (Future Gener. Comput. Syst., 2024)

The unreasonable effectiveness of early discarding after one epoch in neural network hyperparameter optimization. - Romain Egele; Felix Mohr; Tom J. Viering; Prasanna Balaprakash (Neurocomputing, 2024)

Optimization of Learning Workflows at Large Scale on High-Performance Computing Systems. (Optimisation de Processus d'Apprentissage à Grande Échelle sur des Systèmes de Calcul Haute-Performance). - Romain Egele (2024)

DeepHyper: A Python Package for Massively Parallel Hyperparameter Optimization in Machine Learning. - Romain Egele; Prasanna Balaprakash; Gavin M. Wiggins; Brett Eiffert (J. Open Source Softw., 2025)