Akshay Ramachandran

13 papers A* 4C 1Journal 8
YearRankTypeTitle / Venue / Authors
2025 J jnl
Computational aspects of the trace norm contraction coefficient.
CoRR
Idris Delsol, Omar Fawzi, Jan Kochanowski, Akshay Ramachandran
2025 J jnl
Hidden Convexity, Optimization, and Algorithms on Rotation Matrices.
Math. Oper. Res.
Akshay Ramachandran, Kevin Shu, Alex L. Wang
2025 J jnl
Optimal Bounds for Tyler's M-Estimator for Elliptical Distributions.
CoRR
Lap Chi Lau, Akshay Ramachandran
2024 A* conf
Strongly Polynomial Frame Scaling to High Precision.
SODA
Daniel Dadush, Akshay Ramachandran
2024 J jnl
Strongly Polynomial Frame Scaling to High Precision.
CoRR
Daniel Dadush, Akshay Ramachandran
2021 J jnl
Near optimal sample complexity for matrix and tensor normal models via geodesic convexity.
CoRR
Cole Franks, Rafael Mendes de Oliveira, Akshay Ramachandran, Michael Walter
2021 J jnl
Spectral Analysis of Matrix Scaling and Operator Scaling.
SIAM J. Comput.
Tsz Chiu Kwok, Lap Chi Lau, Akshay Ramachandran
2019 A* conf
Spectral Analysis of Matrix Scaling and Operator Scaling.
FOCS
Tsz Chiu Kwok, Lap Chi Lau, Akshay Ramachandran
2019 J jnl
Spectral analysis of matrix scaling and operator scaling.
CoRR
Tsz Chiu Kwok, Lap Chi Lau, Akshay Ramachandran
2018 C conf
MLNoC: A Machine Learning Based Approach to NoC Design.
SBAC-PAD
Nishant Rao, Akshay Ramachandran, Amish Shah
2018 A* conf
The Paulsen problem, continuous operator scaling, and smoothed analysis.
STOC
Tsz Chiu Kwok, Lap Chi Lau, Yin Tat Lee, Akshay Ramachandran
2017 A* conf
Sandpile prediction on a tree in near linear time.
SODA
Akshay Ramachandran, Aaron Schild
2017 J jnl
The Paulsen Problem, Continuous Operator Scaling, and Smoothed Analysis.
CoRR
Tsz Chiu Kwok, Lap Chi Lau, Yin Tat Lee, Akshay Ramachandran