Luis Oala

According to our database, Luis Oala authored at least 22 papers between 2020 and 2024.

Collaborative distances:

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

2024

Croissant: A Metadata Format for ML-Ready Datasets.

Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Joan Giner-Miguelez, Nitisha Jain, Michael Kuchnik, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Pierre Ruyssen, Rajat Shinde, Elena Simperl, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Steffen Vogler, Carole-Jean Wu

CoRR, 2024

2023

Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets.

Saúl Calderón Ramírez, Luis Oala, Jordina Torrents-Barrena, Shengxiang Yang, David A. Elizondo, Armaghan Moemeni, Simon Colreavy-Donnelly, Wojciech Samek, Miguel A. Molina-Cabello, Ezequiel López-Rubio

IEEE Trans. Artif. Intell., April, 2023

Data Models for Dataset Drift Controls in Machine Learning With Optical Images.

Luis Oala, Marco Aversa, Gabriel Nobis, Kurt Willis, Yoan Neuenschwander, Michèle Buck, Christian Matek, Jérôme Extermann, Enrico Pomarico, Wojciech Samek, Roderick Murray-Smith, Christoph Clausen, Bruno Sanguinetti

Trans. Mach. Learn. Res., 2023

DMLR: Data-centric Machine Learning Research - Past, Present and Future.

Luis Oala, Manil Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gürel, Tzu-Sheng Kuo, Yang Liu, Rotem Dror, Danilo Brajovic, Xiaozhe Yao, Max Bartolo, William Gaviria Rojas, Ryan Hileman, Rainier Aliment, Michael W. Mahoney, Meg Risdal, Matthew Lease, Wojciech Samek, Debojyoti Dutta, Curtis G. Northcutt, Cody Coleman, Braden Hancock, Bernard Koch, Girmaw Abebe Tadesse, Bojan Karlas, Ahmed M. Alaa, Adji Bousso Dieng, Natasha F. Noy, Vijay Janapa Reddi, James Zou, Praveen K. Paritosh, Mihaela van der Schaar, Kurt D. Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson

CoRR, 2023

Generative Fractional Diffusion Models.

Gabriel Nobis, Marco Aversa, Maximilian Springenberg, Michael Detzel, Stefano Ermon, Shinichi Nakajima, Roderick Murray-Smith, Sebastian Lapuschkin, Christoph Knochenhauer, Luis Oala, Wojciech Samek

CoRR, 2023

Localized Data Work as a Precondition for Data-Centric ML: A Case Study of Full Lifecycle Crop Disease Identification in Ghana.

Darlington Akogo, Issah Abubakari Samori, Cyril Akafia, Harriet Fiagbor, Andrews Kangah, Donald Kwame Asiedu, Kwabena Fuachie, Luis Oala

CoRR, 2023

DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology.

Marco Aversa, Gabriel Nobis, Miriam Hägele, Kai Standvoss, Mihaela Chirica, Roderick Murray-Smith, Ahmed M. Alaa, Lukas Ruff, Daniela Ivanova, Wojciech Samek, Frederick Klauschen, Bruno Sanguinetti, Luis Oala

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Machine Learning for Health (ML4H) 2023.

Stefan Hegselmann, Antonio Parziale, Divya Shanmugam, Shengpu Tang, Kristen Severson, Mercy Nyamewaa Asiedu, Serina Chang, Bonaventure F. P. Dossou, Qian Huang, Fahad Kamran, Haoran Zhang, Sujay Nagaraj, Luis Oala, Shan Xu, Chinasa T. Okolo, Helen Zhou, Jessica Dafflon, Caleb Ellington, Sarah Jabbour, Hyewon Jeong, Harry Reyes Nieva, Yuzhe Yang, Ghada Zamzmi, Vishwali Mhasawade, Van Truong, Payal Chandak, Matthew Lee, Peniel Argaw, Kyle Heuton, Harvineet Singh, Thomas Hartvigsen

Proceedings of the Machine Learning for Health, 2023

2022

Machine Learning for Health symposium 2022 - Extended Abstract track.

Antonio Parziale, Monica Agrawal, Shalmali Joshi, Irene Y. Chen, Shengpu Tang, Luis Oala, Adarsh Subbaswamy

CoRR, 2022

Data Models for Dataset Drift Controls in Machine Learning With Images.

Luis Oala, Marco Aversa, Gabriel Nobis, Kurt Willis, Yoan Neuenschwander, Michèle Buck, Christian Matek, Jérôme Extermann, Enrico Pomarico, Wojciech Samek, Roderick Murray-Smith, Christoph Clausen, Bruno Sanguinetti

CoRR, 2022

Machine Learning for Health (ML4H) 2022.

Antonio Parziale, Monica Agrawal, Shengpu Tang, Kristen Severson, Luis Oala, Adarsh Subbaswamy, Sayantan Kumar, Elora D. M. Schörverth, Stefan Hegselmann, Helen Zhou, Ghada Zamzmi, Purity Mugambi, Elena Sizikova, Girmaw Abebe Tadesse, Yuyin Zhou, Taylor W. Killian, Haoran Zhang, Fahad Kamran, Andrea Hobby, Mars Huang, Ahmed M. Alaa, Harvineet Singh, Irene Y. Chen, Shalmali Joshi

Proceedings of the Machine Learning for Health, 2022

2021

Machine Learning for Health: Algorithm Auditing & Quality Control.

Luis Oala, Andrew G. Murchison, Pradeep Balachandran, Shruti Choudhary, Jana Fehr, Alixandro Werneck Leite, Peter G. Goldschmidt, Christian Johner, Elora D. M. Schörverth, Rose Nakasi, Martin Meyer, Federico Cabitza, Pat Baird, Carolin Prabhu, Eva Weicken, Xiaoxuan Liu, Markus Wenzel, Steffen Vogler, Darlington Ahiale Akogo, Shada Alsalamah, Emre Kazim, Adriano S. Koshiyama, Sven Piechottka, Sheena Macpherson, Ian Shadforth, Regina Geierhofer, Christian Matek, Joachim Krois, Bruno Sanguinetti, Matthew Arentz, Pavol Bielik, Saúl Calderón Ramírez, Auss Abbood, Nicolas Langer, Stefan Haufe, Ferath Kherif, Sameer Pujari, Wojciech Samek, Thomas Wiegand

J. Medical Syst., 2021

A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021.

Fabian Falck, Yuyin Zhou, Emma Rocheteau, Liyue Shen, Luis Oala, Girmaw Abebe, Subhrajit Roy, Stephen Pfohl, Emily Alsentzer, Matthew B. A. McDermott

CoRR, 2021

More Than Meets The Eye: Semi-supervised Learning Under Non-IID Data.

Saúl Calderón Ramírez, Luis Oala

CoRR, 2021

Post-Hoc Domain Adaptation via Guided Data Homogenization.

Kurt Willis, Luis Oala

CoRR, 2021

Detecting failure modes in image reconstructions with interval neural network uncertainty.

Luis Oala, Cosmas Heiß, Jan MacDonald, Maximilian März, Gitta Kutyniok, Wojciech Samek

Int. J. Comput. Assist. Radiol. Surg., 2021

Improving Uncertainty Estimation With Semi-Supervised Deep Learning for COVID-19 Detection Using Chest X-Ray Images.

Saúl Calderón Ramírez, Shengxiang Yang, Armaghan Moemeni, Simon Colreavy-Donnelly, David A. Elizondo, Luis Oala, Jorge Rodríguez-Capitán, Manuel Jiménez-Navarro, Ezequiel López-Rubio, Miguel A. Molina-Cabello

IEEE Access, 2021

Machine Learning for Health (ML4H) 2021.

Subhrajit Roy, Stephen Pfohl, Girmaw Abebe Tadesse, Luis Oala, Fabian Falck, Yuyin Zhou, Liyue Shen, Ghada Zamzmi, Purity Mugambi, Ayah Zirikly, Matthew B. A. McDermott, Emily Alsentzer

Proceedings of the Machine Learning for Health, 2021

Interval Neural Networks as Instability Detectors for Image Reconstructions.

Jan MacDonald, Maximilian März, Luis Oala, Wojciech Samek

Proceedings of the Bildverarbeitung für die Medizin 2021, 2021

2020

MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures.

Saúl Calderón Ramírez, Luis Oala, Jordina Torrents-Barrena, Shengxiang Yang, Armaghan Moemeni, Wojciech Samek, Miguel A. Molina-Cabello

CoRR, 2020

Interval Neural Networks: Uncertainty Scores.

Luis Oala, Cosmas Heiss, Jan MacDonald, Maximilian März, Wojciech Samek, Gitta Kutyniok

CoRR, 2020

ML4H Auditing: From Paper to Practice.

Luis Oala, Jana Fehr, Luca Gilli, Pradeep Balachandran, Alixandro Werneck Leite, Saúl Calderón Ramírez, Danny Xie Li, Gabriel Nobis, Erick Alejandro Muñoz Alvarado, Giovanna Jaramillo-Gutierrez, Christian Matek, Arun Shroff, Ferath Kherif, Bruno Sanguinetti, Thomas Wiegand

Proceedings of the Machine Learning for Health Workshop, 2020