1 citations · 1 across the 6 of their papers we have counts for
6 papers
Generalized infinite dimensional Alpha-Procrustes based geometries
Salvish Goomanee, Andi Han, Pratik Jawanpuria +1
This work extends the recently introduced Alpha-Procrustes family of Riemannian metrics for symmetric positive definite (SPD) matrices by incorporating generalized versions of the…
Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method
Andi Han, Pierre-Louis Poirion, Akiko Takeda
Optimization with orthogonality constraints frequently arises in various fields such as machine learning. Riemannian optimization offers a powerful framework for solving these prob…
The Adaptive Complexity of Finding a Stationary Point
Huanjian Zhou, Andi Han, Akiko Takeda +1
In large-scale applications, such as machine learning, it is desirable to design non-convex optimization algorithms with a high degree of parallelization. In this work, we study th…
Riemannian coordinate descent algorithms on matrix manifolds
Andi Han, Pratik Jawanpuria, Bamdev Mishra
Many machine learning applications are naturally formulated as optimization problems on Riemannian manifolds. The main idea behind Riemannian optimization is to maintain the feasib…
SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining
Andi Han, Jiaxiang Li, Wei Huang +4
Large language models (LLMs) have shown impressive capabilities across various tasks. However, training LLMs from scratch requires significant computational power and extensive mem…
A Framework for Bilevel Optimization on Riemannian Manifolds
Andi Han, Bamdev Mishra, Pratik Jawanpuria +1
Bilevel optimization has gained prominence in various applications. In this study, we introduce a framework for solving bilevel optimization problems, where the variables in both t…