Substantial progress has been made recently on developing provably accurate and efficient
algorithms for low-rank matrix factorization via nonconvex optimization. While conventional …

Low-rank matrices play a fundamental role in modeling and computational methods for
signal processing and machine learning. In many applications where low-rank matrices …

This work studies post-training parameter quantization in large language models (LLMs).
We introduce quantization with incoherence processing (QuIP), a new method based on the …

Deep neural networks have shown the ability to extract universal feature representations
from data such as images and text that have been useful for a variety of learning tasks …

We address the problem of Federated Learning (FL) where users are distributed and
partitioned into clusters. This setup captures settings where different groups of users have …

Machine learning develops rapidly, which has made many theoretical breakthroughs and is
widely applied in various fields. Optimization, as an important part of machine learning, has …

Deep learning has emerged as a strong and efficient framework that can be applied to a
broad spectrum of complex learning problems which were difficult to solve using the …

Online streaming feature selection (OSFS) has attracted extensive attention during the past
decades. Current approaches commonly assume that the feature space of fixed data …

Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …