Publications [#356481] of Lawrence Carin

Papers Published

1. Cheng, P; Hao, W; Dai, S; Liu, J; Gan, Z; Carin, L, CLUB: A contrastive log-ratio upper bound of mutual information, 37th International Conference on Machine Learning, ICML 2020, vol. PartF168147-3 (January, 2020), pp. 1757-1766, ISBN 9781713821120
   (last updated on 2024/12/31)

   Abstract:
   Mutual information (MI) minimization has gained considerable interests in various machine learning tasks. However, estimating and minimizing MI in high-dimensional spaces remains a challenging problem, especially when only samples, rather than distribution forms, are accessible. Previous works mainly focus on MI lower bound approximation, which is not applicable to MI minimization problems. In this paper, we propose a novel Contrastive Log-ratio Upper Bound (CLUB) of mutual information. We provide a theoretical analysis of the properties of CLUB and its variational approximation. Based on this upper bound, we introduce a MI minimization training scheme and further accelerate it with a negative sampling strategy. Simulation studies on Gaussian distributions show the reliable estimation ability of CLUB. Real-world MI minimization experiments, including domain adaptation and information bottleneck, demonstrate the effectiveness of the proposed method. The code is at https://github.com/Linear95/CLUB.