14 citations · 18 across the 6 of their papers we have counts for
10 papers
Balancing Gradient and Hessian Queries in Non-Convex Optimization
Deeksha Adil, Brian Bullins, Aaron Sidford +1
We develop optimization methods which offer new trade-offs between the number of gradient and Hessian computations needed to compute the critical point of a non-convex function. We…
Convex optimization with -norm oracles
Deeksha Adil, Brian Bullins, Arun Jambulapati +1
In recent years, there have been significant advances in efficiently solving -regression using linear system solvers and -regression [Adil-Kyng-Peng-Sachdeva, J. AC…
Acceleration Meets Inverse Maintenance: Faster -Regression
Deeksha Adil, Shunhua Jiang, Rasmus Kyng
We propose a randomized multiplicative weight update (MWU) algorithm for regression that runs in time when…
Fast Algorithms for -Regression
Deeksha Adil, Rasmus Kyng, Richard Peng +1
The -norm regression problem is a classic problem in optimization with wide ranging applications in machine learning and theoretical computer science. The goal is to comput…
Optimal Methods for Higher-Order Smooth Monotone Variational Inequalities
Deeksha Adil, Brian Bullins, Arun Jambulapati +1
In this work, we present new simple and optimal algorithms for solving the variational inequality (VI) problem for -order smooth, monotone operators -- a problem that gener…
Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization
Deeksha Adil, Brian Bullins, Sushant Sachdeva
We provide several algorithms for constrained optimization of a large class of convex problems, including softmax, regression, and logistic regression. Central to our appr…