2 citations · 3 across the 8 of their papers we have counts for
8 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…
Discrete Bridges for Mutual Information Estimation
Iryna Zabarianska, Sergei Kholkin, Grigoriy Ksenofontov +2
Diffusion bridge models in both continuous and discrete state spaces have recently become powerful tools in the field of generative modeling. In this work, we leverage the discrete…
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…
InfoBridge: Mutual Information estimation via Bridge Matching
Sergei Kholkin, Ivan Butakov, Evgeny Burnaev +2
Diffusion bridge models have recently become a powerful tool in the field of generative modeling. In this work, we leverage their power to address another important problem in mach…
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…
Information Bottleneck Analysis of Deep Neural Networks via Lossy Compression
Ivan Butakov, Alexander Tolmachev, Sofia Malanchuk +3
The Information Bottleneck (IB) principle offers an information-theoretic framework for analyzing the training process of deep neural networks (DNNs). Its essence lies in tracking…