Search Results for author:
Found 4 papers, 2 papers with code
Efficient Guided Generation for Large Language Models
In this article we show how the problem of neural text generation can be constructively reformulated in terms of transitions between the states of a finite-state machine.
Lasso Meets Horseshoe
The goal of our paper is to survey and contrast the major advances in two of the most commonly used high-dimensional techniques, namely, the Lasso and horseshoe regularization methodologies.
Methodology Primary 62J07, 62J05, Secondary 62H15, 62F03
A Statistical Theory of Deep Learning via Proximal Splitting
In this paper we develop a statistical theory and an implementation of deep learning models.
Proximal Algorithms in Statistics and Machine Learning
We provide a discussion of convergence of non-descent algorithms with acceleration and for non-convex functions.
