Romain Egele

According to our database, Romain Egele authored at least 16 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

CoRR, 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

The Unreasonable Effectiveness Of Early Discarding After One Epoch In Neural Network Hyperparameter Optimization.

Romain Egele, Felix Mohr, Tom J. Viering, Prasanna Balaprakash

CoRR, 2024

2023

Optimizing Distributed Training on Frontier for Large Language Models.

Sajal Dash, Isaac Lyngaas, Junqi Yin, Xiao Wang, Romain Egele, Guojing Cong, Feiyi Wang, Prasanna Balaprakash

CoRR, 2023

Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives.

Romain Egele, Tyler Chang, Yixuan Sun, Venkatram Vishwanath, Prasanna Balaprakash

CoRR, 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

Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization?

Romain Egele, Isabelle Guyon, Yixuan Sun, Prasanna Balaprakash

Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization.

Romain Égelé, Isabelle Guyon, Venkatram Vishwanath, Prasanna Balaprakash

Proceedings of the 19th IEEE International Conference on e-Science, 2023

2022

Asynchronous Distributed Bayesian Optimization at HPC Scale.

Romain Egele, Joceran Gouneau, Venkatram Vishwanath, Isabelle Guyon, Prasanna Balaprakash

CoRR, 2022

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification.

Romain Egele, Romit Maulik, Krishnan Raghavan, Bethany Lusch, Isabelle Guyon, Prasanna Balaprakash

Proceedings of the 26th International Conference on Pattern Recognition, 2022

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

Proceedings of the IEEE International Conference on Cluster Computing, 2022

2021

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification.

Romain Egele, Romit Maulik, Krishnan Raghavan, Prasanna Balaprakash, Bethany Lusch

CoRR, 2021

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

Proceedings of the International Conference for High Performance Computing, 2021

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

Recurrent neural network architecture search for geophysical emulation.

Romit Maulik, Romain Egele, Bethany Lusch, Prasanna Balaprakash

Proceedings of the International Conference for High Performance Computing, 2020

2019

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

Proceedings of the International Conference for High Performance Computing, 2019