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; et al. (ML4H@NeurIPS, 2020)

Interval Neural Networks: Uncertainty Scores. - Luis Oala; Cosmas Heiss; Jan MacDonald; Maximilian März; Wojciech Samek; Gitta Kutyniok (CoRR, 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 as Instability Detectors for Image Reconstructions. - Jan MacDonald; Maximilian März; Luis Oala; Wojciech Samek (Bildverarbeitung für die Medizin, 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 (ML4H@NeurIPS, 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)

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)

Post-Hoc Domain Adaptation via Guided Data Homogenization. - Kurt Willis; Luis Oala (CoRR, 2021)

More Than Meets The Eye: Semi-supervised Learning Under Non-IID Data. - Saúl Calderón Ramírez; Luis Oala (CoRR, 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)

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; et al. (J. Medical Syst., 2021)

Machine Learning for Health (ML4H) 2022. - Antonio Parziale; Monica Agrawal; Shengpu Tang; Kristen Severson 001; Luis Oala; Adarsh Subbaswamy; Sayantan Kumar; Elora D. M. Schörverth; Stefan Hegselmann; Helen Zhou; Ghada Zamzmi; Purity Mugambi; et al. (ML4H@NeurIPS, 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; et al. (CoRR, 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)

Machine Learning for Health (ML4H) 2023. - Stefan Hegselmann; Antonio Parziale; Divya Shanmugam; Shengpu Tang; Kristen Severson 001; Mercy Nyamewaa Asiedu; Serina Chang; Bonaventure F. P. Dossou; Qian Huang; Fahad Kamran; Haoran Zhang 003; Sujay Nagaraj; et al. (ML4H@NeurIPS, 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; et al. (NeurIPS, 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)

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)

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 018; Rotem Dror; Danilo Brajovic; Xiaozhe Yao; Max Bartolo; William Gaviria Rojas; et al. (CoRR, 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; et al. (Trans. Mach. Learn. Res., 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., 2023)

Data Models for Dataset Drift Controls in Machine Learning With Optical Images - Datasets. - 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; et al. (Zenodo, 2023)

Croissant: A Metadata Format for ML-Ready Datasets. - Mubashara Akhtar; Omar Benjelloun; Costanza Conforti; Pieter Gijsbers; Joan Giner-Miguelez; Nitisha Jain; Michael Kuchnik; Quentin Lhoest; Pierre Marcenac; Manil Maskey; Peter Mattson; Luis Oala; et al. (DEEM@SIGMOD, 2024)

Croissant: A Metadata Format for ML-Ready Datasets. - Mubashara Akhtar; Omar Benjelloun; Costanza Conforti; Luca Foschini 002; Joan Giner-Miguelez; Pieter Gijsbers; Sujata S. Goswami; Nitisha Jain; Michalis Karamousadakis; Michael Kuchnik; Satyapriya Krishna; Sylvain Lesage; et al. (NeurIPS, 2024)

Generative Fractional Diffusion Models. - Gabriel Nobis; Maximilian Springenberg; Marco Aversa; Michael Detzel; Rembert Daems; Roderick Murray-Smith; Shinichi Nakajima; Sebastian Lapuschkin; Stefano Ermon; Tolga Birdal; Manfred Opper; Christoph Knochenhauer; et al. (NeurIPS, 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; et al. (CoRR, 2024)

Introducing v0.5 of the AI Safety Benchmark from MLCommons. - Bertie Vidgen; Adarsh Agrawal; Ahmed M. Ahmed 004; Victor Akinwande; Namir Al-Nuaimi; Najla Alfaraj; Elie Alhajjar; Lora Aroyo; Trupti Bavalatti; Borhane Blili-Hamelin; Kurt D. Bollacker; Rishi Bomassani; et al. (CoRR, 2024)

A Standardized Machine-readable Dataset Documentation Format for Responsible AI. - Nitisha Jain; Mubashara Akhtar; Joan Giner-Miguelez; Rajat Shinde; Joaquin Vanschoren; Steffen Vogler; Subhra S. Goswami; Yuhan Rao; Tim Santos; Luis Oala; Michalis Karamousadakis; Manil Maskey; et al. (CoRR, 2024)

OpenML: Insights from 10 years and more than a thousand papers. - Bernd Bischl; Giuseppe Casalicchio; Taniya Das; Matthias Feurer; Sebastian Fischer; Pieter Gijsbers; Subhaditya Mukherjee; Andreas C. Müller 002; László Németh; Luis Oala; Lennart Purucker; Sahithya Ravi; et al. (Patterns, 2025)

AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons. - Shaona Ghosh; Heather Frase; Adina Williams; Sarah Luger; Paul Röttger; Fazl Barez; Sean McGregor; Kenneth Fricklas; Mala Kumar; Quentin Feuillade-Montixi; Kurt Bollacker; Felix Friedrich; et al. (CoRR, 2025)