Uncertainty Challenge Workshop

The workshop will be held on December 14, 2024 at the Vancouver Convention Center in Vancouver, BC, Canada as a part of the 38th annual conference on Neural Information Processing Systems (NeurIPS).

Join the Challenge here Workshop Schedule

Workshop Schedule

Join us for the Higgs Uncertainty Challenge Workshop at NeurIPS 2024

Date: Saturday, December 14

Time: 9:00 AM - 12:00 PM

Location: West Meeting Room 215, 216

9:00 AM - 9:05 AM

Opening Ragansu Chakkappai · Wahid Bhimji · Jordan Dudley · Sascha Diefenbacher

9:05 AM - 9:45 AM

Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference Ann Lee

9:45 AM - 10:00 AM

DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methods Becky Nevin

10:00 AM - 10:15 AM

Neural network prediction of strong lensing systems with domain adaptation and uncertainty quantification Shrihan Agarwal

10:15 AM - 10:30 AM

Break Networking

10:30 AM - 11:00 AM

HiggsML Uncertainty Challenge Introduction Sascha Diefenbacher · Jordan Dudley

11:00 AM - 11:15 AM

2nd Place Competition Milestone Yota Hashizume

11:15 AM - 11:45 AM

1st Place Competition Milestone (Ensembles and Uncertainty Quantification) Ibrahim Elsharkawy

11:45 AM - 12:00 PM

Closeout Sascha Diefenbacher · David Rousseau · Shih-Chieh Hsu

For more details and uptodate schedule please check the NeurIPS Schedule.

About the Challenge

This NeurIPS 2024 Machine Learning competition is one of the first to strongly emphasise mastering uncertainties in the input training dataset and outputting credible confidence intervals. This challenge explores uncertainty-aware AI techniques for High Energy Physics (HEP).

The context is the measurement of the Higgs Boson signal like in HiggsML challenge on Kaggle in 2014. Participants should design an advanced analysis technique that can not only measure the signal strength but also provide a confidence interval

The confidence interval should include statistical and systematic uncertainties (concerning detector calibration, background levels, etc…). It is expected that advanced analysis techniques that can control the impact of systematics will perform best. This challenge presents an opportunity to push the boundaries of machine learning applications within physics while still focusing on essential ML skills like robust model development and uncertainty quantification.

Organizers

We gratefully acknowledge the efforts of the FAIR Universe Team, composed of researchers dedicated to advancing high energy physics, cosmology, and machine learning for the benefit of the scientific community.

For inquiries, please contact us at: fairuniverse@example.com

Wahid Bhimji
Benjamin Nachman
Paolo Calafiura
Peter Nugent
Benjamin Thorne
Chris Harris
Sascha Diefenbacher
Steven Farrell
David Rousseau
Ragansu Chakkapai
Mathis Reymond
Shih-Chieh Hsu
Elham Khoda
Yuan-Tang Chou
Yulei Zhang
Isabelle Guyon
Ihsan Ullah
Daniel Whiteson
Aishik Ghosh

Competition Material

Find all the competition resources below:

Codabench Challenge Page: This serves as the platform to submit entries to the competition.
Tutorial Slides: These slides will help you register and submit a sample dummy submission.
Training Data (6.5 GB): Download the training data if you you want to experiment with the data on your local machines.
Documentation: This contains detailed information about the science behind the challenge, the specifics of the data, and documents the code used to facilitate the evaluation of the competition. It also describes the evaluation metric.
GitHub Repository: This hosts the code for testing submissions, as well as the starting kit notebook. The starting kit is also available on Google Colab.
White Paper: This serves as a full breakdown of the competition in detail.

FAIR Universe: Uncertainty Challenge Workshop

Workshop Schedule

About the Challenge

Organizers

Competition Material