Call for paper
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.
Important information
- The submission website: OpenReview and Template
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Submission Deadline: 4 - 8 pages (excluding references and appendices) are allowed. Using ICLR'25 template is also allowed.
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Submission Deadline: October 1st, 2024, GMT
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Author notification: October 9th, 2024, GMT
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Workshop date: December 14th, 2024
Invited speakers
Panel members
The FITML Organizers
Fanghui Liu (Warwick),
Grigorios Chrysos (UW-Madison),
Beidi Chen (CMU),
Rebekka Burkholz (CISPA),
Saleh Soltan (Amazon),
Angeliki Giannou (UW-Madison),
Masashi Sugiyama (UTokyo/RIKEN),
Volkan Cevher (EPFL)
The FITML Volunteers
Yongtao Wu (EPFL),
Yuanhe Zhang (Warwick)
Contact
preferred contact email: neurips24fitml@outlook.com or contact
Fanghui.
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