ML Interpretability for Scientific Discovery

Call for Papers

Update

All events will be streamed live via Zoom webinar and Youtube.

Registered ICML attendees please join us at the webinar link here.

Event will be streamed live on Youtube here: https://www.youtube.com/watch?v=0Q-4EQriYJs

Call for Papers

ICML 2020 will be a fully virtual conference. We will follow the guidance laid out by the ICML 2020 organizers with regard to the workshop participation.

We invite 2-4 page extended abstracts of unpublished works, as well as previously published works that are in the theme of ML interpretability for scientific discovery.

Topics

Topics of interest include but are not limited to

  • Applications of deep learning based interpretability techniques to scientific domains.

  • Causality - causal models.

  • Combining structure with ML for discovery.

  • Interpretability techniques (or problems requiring interpretablity) for different data modalities: images, time-series, audio/speech, text, multi-modal data, voxels as images, point clouds, very high definition images e.g. MRI, etc.

  • GANs / generative modeling for representation understanding, visual interpretation etc.

  • Simulations and or synthetic experiments to evaluate or enable interpretation.

  • Representation learning in a world where there’s a rich, structure.

  • Interpretable / Disentangled representation learning

  • Visualizations for model or data explanation.

  • New datasets , challenges, benchmarks.

This is not an exhaustive list. We welcome submissions from a wide range of sciences including but not limited to brain sciences, behavioral sciences, weather/climate science, physics, chemistry, biology, medical applications, and others. We also invite folks from the ML community with suggestions on tools and members of the science communities with problems.

Important Dates

All deadlines are at 11:59 p.m. Anywhere on Earth on the listed dates.

Submission deadline: 26 May 2020 9 June 2020

Notification to authors: 18 June 2020 20 June 2020

Final submission: 2 July 2020 (Links to accepted papers will be posted on the website.)

Date of the workshop: July 17 (see schedule here)

Submission Instructions

Extended abstracts can be upto 4 pages (without including the references) in the ICML style. These should be anonymized for double-blind review. Please refer to the ICML style and author instructions. Previously published works can be submitted directly without any modifications or change in format/page length (these will only be lightly reviewed for suitability to the workshop).

Please submit your papers via the CMT system using link below:

https://cmt3.research.microsoft.com/MLI4SDW2020

This workshop is non-archival. Papers submitted to this workshop can be in-submission or can later be submitted to other venues (conferences or journals) without violating dual submission policy.

Final Submission

Links to accepted papers and artifacts such as slides/videos/posters will be made available on the website. The workshop will not have a formal proceedings. The authors are encouraged to post their works on arxiv or make them available at a location that is publicly accessible (e.g. Drive, Dropbox, GitHub, etc.) and provide links.

Program Committee


Program committee members and reviewers:

  • Akinori Mitani (Google)

  • Amir Feder (Technion - Israel Institute of Technology)

  • Amirata Ghorbani (Stanford University)

  • Arunachalam Narayanaswamy (Google)

  • Avinash Varadarajan (Google)

  • Awa Dieng (Google)

  • Benjamin Sanchez-Lengeling (Google)

  • Bo Dai (Google Brain)

  • Chih-Kuan Yeh (Carnegie Mellon University)

  • Hanna Levitin (Columbia)

  • Katy Blumer (Cornell)

  • Kevin Wu (Stanford University)

  • Martin Forsythe (Lightmatter Inc.)

  • Miles Cranmer (Princeton University)

  • Pang Wei Koh (Stanford University)

  • Ramin Ansari (University of Michigan)

  • Stephan Hoyer (Google)

  • Subham Sekhar Sahoo (Google)

  • Suhani Vora (Google)

  • Wesley Wei Qian (University of Illinois at Urbana-Champaign)