Best paper at ISIT 2025 workshop

Our paper

• U. K. Ganesan, G. Durisi, M. Zecchin, P. Popovski, and O. Simeone, “Online conformal compression for zero-delay communication with distortion guarantees,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Learn to Compress & Compress to Learn Workshop, Ann Arbor, MI, USA, June 2025. []

was selected as best paper at the 2025 International Conference on Information Theory Workshop “Learn to Compress and Compress to Learn”.

The idea of the paper is to revisit the problem of lossy source compression for the case in which both encoder and decoder are equipped with an arbitrary pre-trained sequence predictor (say a large-language model). We present a novel algorithm, referred to as online conformal compression, which leverages online conformal prediction, and comes with a guaranteed deterministic per-sequence upper bound on the distortion.