2 citations · 2 across the 5 of their papers we have counts for
5 papers
Towards Diverse and Comprehensive Benchmarks for Mutual Information Estimation
Alberto Foresti, Ivan Butakov, Alexander Tolmachev +3
Mutual information (MI) estimation is a central problem in machine learning and statistics; however, existing benchmarks typically evaluate estimators on simplified, low-dimensiona…
FMMI: Flow Matching Mutual Information Estimation
Ivan Butakov, Alexander Semenenko, Valeriya Kirova +2
We introduce a novel Mutual Information (MI) estimator that fundamentally reframes the discriminative approach. Instead of training a classifier to discriminate between joint and m…
Curse of Slicing: Why Sliced Mutual Information is a Deceptive Measure of Statistical Dependence
Alexander Semenenko, Ivan Butakov, Alexey Frolov +1
Sliced Mutual Information (SMI) is widely used as a scalable alternative to mutual information for measuring non-linear statistical dependence. Despite its advantages, such as fast…
Efficient Distribution Matching of Representations via Noise-Injected Deep InfoMax
Ivan Butakov, Alexander Semenenko, Alexander Tolmachev +3
Deep InfoMax (DIM) is a well-established method for self-supervised representation learning (SSRL) based on maximization of the mutual information between the input and the output…
Mutual Information Estimation via Normalizing Flows
Ivan Butakov, Alexander Tolmachev, Sofia Malanchuk +2
We propose a novel approach to the problem of mutual information (MI) estimation via introducing a family of estimators based on normalizing flows. The estimator maps original data…