activity
20232026
most citedDMLR: Data-centric Machine Learning Research -- Past, Present and Future

6 citations · 10 across the 8 of their papers we have counts for

collaborators

8 papers

cs.AI2026

Croissant Tasks: A Metadata Format for Reproducible Machine Learning Evaluations

Omar Benjelloun, Leonardo Martins Bianco, Isabelle Guyon +8

Reproducibility is fundamental to the scientific method, yet remains a critical challenge in machine learning. Contributing factors include underspecified execution details and bri…

astro-ph.CO2026

FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology

Biwei Dai, Po-Wen Chang, Wahid Bhimji +15

Weak gravitational lensing, the correlated distortion of background galaxy shapes by foreground structures, is a powerful probe of the matter distribution in our universe and allow…

cs.CV2025

Stylized Meta-Album: Group-bias injection with style transfer to study robustness against distribution shifts

Romain Mussard, Aurélien Gauffre, Ihsan Ullah +4

We introduce Stylized Meta-Album (SMA), a new image classification meta-dataset comprising 24 datasets (12 content datasets, and 12 stylized datasets), designed to advance studies…

hep-ph2025

Fair Universe Higgs Uncertainty Challenge

Ragansu Chakkappai, Wahid Bhimji, Paolo Calafiura +16

This competition in high-energy physics (HEP) and machine learning was the first to strongly emphasise uncertainties in cross-section measurement. Partici…

cs.CL2024★ 4 cited

Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experiment

Alexander Goldberg, Ihsan Ullah, Thanh Gia Hieu Khuong +4

Large language models (LLMs) represent a promising, but controversial, tool in aiding scientific peer review. This study evaluates the usefulness of LLMs in a conference setting as…

math.OC2024

Meta-Learning from Learning Curves for Budget-Limited Algorithm Selection

Manh Hung Nguyen, Lisheng Sun-Hosoya, Isabelle Guyon

Training a large set of machine learning algorithms to convergence in order to select the best-performing algorithm for a dataset is computationally wasteful. Moreover, in a budget…

hep-ph2024

FAIR Universe HiggsML Uncertainty Dataset and Competition

Lisa Benato, Wahid Bhimji, Paolo Calafiura +26

The FAIR Universe HiggsML Uncertainty Challenge focused on measuring the physical properties of elementary particles with imperfect simulators. Participants were required to comput…

cs.LG2023★ 6 cited

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

Luis Oala, Manil Maskey, Lilith Bat-Leah +35

Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure developm…