We are excited to announce the first edition of 'Learn to Compress' workshop happening at ISIT 2024. This satellite workshop will be held on Sunday 7th July 2024 (full day) in Athens, Greece. 

This workshop will act as a dynamic platform for fostering interdisciplinary collaborations, featuring distinguished experts in machine learning and computer science, who will contribute valuable practical insights to the real-world applications of data compression. For ISIT regulars, it will acquaint them with recent advances in data compression, enabling them to explore the interplay between classical and contemporary methods. For those new to the field and experts alike, it will provide a chance to learn from experienced researchers from the industry and academia, and connect with peers who share similar research interests.

The program will include keynote talks from:
* Johannes Ballé (Google Research)
* Jose Miguel Hernandez Lobato (University of Cambridge)
* Shirin Jalali (Rutgers University)
* Lucas Theis (Google DeepMind)
and four spotlight presentations, and a poster session. 

We invite researchers from data compression and related fields to submit their latest work on theory and applications of data compression to the workshop. All accepted papers will be presented as posters during the poster session. Some papers will also be selected for spotlight presentations. Topics of interest include but are not limited to:
* Source coding (e.g., images, video, abstract sources) in the era of machine learning.
* Learning-based approaches to information theory.
* Theoretical foundations of learned compression.
* Quantization, entropy coding, stochastic coding, and channel synthesis.
* Compression under perceptual/realism metrics.
* Learning-based approaches to distributed and multi-terminal compression.

Submissions are due March 3rd. For more details (including the program and call for papers), visit our workshop website: https://learn-to-compress-workshop-isit.github.io/

The submission link is currently open on OpenReview: https://openreview.net/group?id=IEEE.org/ISIT/2024/Workshop/LCW