NeurIPS 2024 Workshop
Fine-Tuning in Modern Machine Learning: Principles and Scalability (FITML)
at Dec. 14th (Satuday), East Exhibit Hall A
This FITML workshop aims to contribute to the recent radical paradigm shift for fine-tuning in modern machine learning, theoretically, computationally, and systematically.
It encourages researchers to push forward the frontiers of theoretical understanding of fine-tuning, devising expeditious and resource-efficient inference and fine-tuning methods in machine learning systems, enabling their deployment within constrained computational resources.
This FITML workshop explores theoretical and/or empirical results for understanding and advancing modern practices for efficiency in machine learning.
Invited Speakers
Panelist
Important Dates
Submission Deadline: October 1, 2024, GMT
Author notification: October 9, 2024, GMT
Workshop date: December 14 (Saturday), 2024
Workshop location: East Exhibit Hall A
Organizers
Fanghui Liu (Warwick)
large-scale kernel approximation, deep learning theory
Grigorios Chrysos (UW-Madison)
neural architecture design,
deep learning
Beidi Chen (CMU)
large scale machine learning,
algorithm-hardware design
Rebekka Burkholz (CISPA)
scalable machine learning,
deep learning theory
Saleh Soltan (Amazon)
natural language processing
LLMs
Angeliki Giannou (UW-Madison)
deep learning theory,
in-context learning
Masashi Sugiyama (RIKEN, UTokyo)
transfer learning, trustworthy ML
Volkan Cevher (EPFL)
non-convex optimization,
scalable machine learning
Volunteers
Yongtao Wu (EPFL)
trustworthy ML
Yuanhe Zhang (Warwick)
DL theory
Let us know if you'll be attending!