38 citations · 81 across the 26 of their papers we have counts for
5 papers · 1 filter
A Constructive Approach to Function Realization by Neural Stochastic Differential Equations
Tanya Veeravalli, Maxim Raginsky
The problem of function approximation by neural dynamical systems has typically been approached in a top-down manner: Any continuous function can be approximated to an arbitrary ac…
Can Transformers Learn to Solve Problems Recursively?
Shizhuo Dylan Zhang, Curt Tigges, Stella Biderman +2
Neural networks have in recent years shown promise for helping software engineers write programs and even formally verify them. While semantic information plays a crucial part in t…
Majorizing Measures, Codes, and Information
Yifeng Chu, Maxim Raginsky
The majorizing measure theorem of Fernique and Talagrand is a fundamental result in the theory of random processes. It relates the boundedness of random processes indexed by elemen…
A unified framework for information-theoretic generalization bounds
Yifeng Chu, Maxim Raginsky
This paper presents a general methodology for deriving information-theoretic generalization bounds for learning algorithms. The main technical tool is a probabilistic decorrelation…
A Chain Rule for the Expected Suprema of Bernoulli Processes
Yifeng Chu, Maxim Raginsky
We obtain an upper bound on the expected supremum of a Bernoulli process indexed by the image of an index set under a uniformly Lipschitz function class in terms of properties of t…