
The Information Bottleneck Problem and Its Applications in Machine Learning
Inference capabilities of machine learning (ML) systems skyrocketed in r...
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The Secrecy Capacity of CostConstrained Wiretap Channels
In many informationtheoretic communication problems, adding an input co...
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Capacity of Continuous Channels with Memory via Directed Information Neural Estimator
Calculating the capacity (with or without feedback) of channels with mem...
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Limit Distribution for Smooth Total Variation and χ^2Divergence in High Dimensions
Statistical divergences are ubiquitous in machine learning as tools for ...
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Limit Distribution Theory for Smooth Wasserstein Distance with Applications to Generative Modeling
The 1Wasserstein distance (W_1) is a popular proximity measure between ...
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GaussianSmooth Optimal Transport: Metric Structure and Statistical Efficiency
Optimal transport (OT), and in particular the Wasserstein distance, has ...
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Convergence of Smoothed Empirical Measures with Applications to Entropy Estimation
This paper studies convergence of empirical measures smoothed by a Gauss...
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Estimating Differential Entropy under Gaussian Convolutions
This paper studies the problem of estimating the differential entropy h(...
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Estimating Information Flow in Neural Networks
We study the flow of information and the evolution of internal represent...
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Information Storage in the Stochastic Ising Model
Most information systems store data by modifying the local state of matt...
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Wiretap and GelfandPinsker Channels Analogy and its Applications
A framework of analogy between wiretap channels (WTCs) and statedepende...
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Ziv Goldfeld
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