[Call for Papers] ICML 2024 Workshop on Theoretical Foundations of Foundation Models

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Berivan Isik

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May 28, 2024, 11:26:15 AM5/28/24

to Women in Machine Learning

Dear WiML Community,

We are excited to announce and invite submissions of papers to the ICML 2024 Workshop on Theoretical Foundations of Foundation Models: https://sites.google.com/view/tf2m.

The workshop proposes a platform for bringing together researchers and practitioners from the foundation model and theory communities (including statistics, information theory, optimization, and learning theory), to discuss advances and challenges in addressing these concerns, with a focus on the following three themes: (1) efficiency, (2) responsibility, and principled foundations.

We invite researchers working on theoretical aspects of foundation models to submit their work for consideration in the TF2M workshop. We welcome submissions that make theoretical contributions on topics including, but not limited to:

Efficient training, finetuning, and inference algorithms.
Data-efficient training and fine-tuning strategies.
Theoretical foundations of model compression, pruning, and distillation.
Fairness and bias mitigation in foundation models.
Principles of model alignment, and safety.
Directions in privacy and security for foundation models.
Statistical and information-theoretic perspectives on model capabilities.
Optimization theory for model training and fine-tuning.
Emergent capabilities of LLMs, such as in-context learning.
Understanding of neural architectures behind modern neural models such as transformers.

We solicit short workshop paper submissions of up to 4 pages + unlimited references/appendices. Please format submissions in ICML style. Submissions will be double-blind. Accepted papers will be selected to be presented either in a poster session or as a contributed talk. This workshop is non-archival and submissions can be submitted to other venues. Accepted papers will be publicly available through openreview before the start of the workshop.

If you are interested in helping as a reviewer, please fill out this form.

Keynote Speakers:

Ananda Theertha Suresh (Google Research)

Dan Alistarh (IST Austria)

Hannaneh Hajishirzi (U Washington & Allen Institute for AI)

Jason Lee (Princeton)

Kamalika Chaudhuri (UC San Diego & Meta AI)

Ryan Cotterell (ETH Zürich)

Yuandong Tian (Meta AI)

Important Dates: