A major challenge in designing efficient statistical supervised learning algorithms is finding
representations that perform well not only on available training samples but also on unseen …

We study variational principles for metric mean dimension. First we prove that in the
variational principle of Lindenstrauss and Tsukamoto it suffices to take supremum over …

In this paper, we relax the definition of Rényi information dimension. The power law of the
Entropy-Layer in the Galton board is discovered and we calculate its information fractal …

In this paper, we establish novel data-dependent upper bounds on the generalization error
through the lens of a “variable-size compressibility” framework that we introduce newly here …

Wu and Verdú developed a theory of almost lossless analog compression, where one
imposes various regularity conditions on the compressor and the decompressor with the …

High-dimensional and incomplete (HDI) matrices are primarily generated in all kinds of big-
data-related practical applications. A latent factor analysis (LFA) model is capable of …

The concept of entropy has played a significant role in thermodynamics and information
theory, and is also a current research hotspot. Information entropy, as a measure of …

From ancient philosophers to modern economists, biologists, and other researchers, there
has been a continuous effort to unveil causal relations. The most formidable challenge lies …

Compute–forward is a coding technique that enables receiver (s) in a network to directly
decode one or more linear combinations of the transmitted codewords. Initial efforts focused …

From philosophers of ancient times to modern economists, biologists and other researchers
are engaged in revealing causal relations. The most challenging problem is inferring the …