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papers

Publications (13)

cs.DS2020

Faster p-norm minimizing flows, via smoothed q-norm problems

Deeksha Adil, Sushant Sachdeva

We present faster high-accuracy algorithms for computing $ℓ_{p}$ -norm minimizing flows. On a graph with $m$ edges, our algorithm can compute a $(1 + 1/ poly (m))$ -approximate u…

math.OC2021

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, $ℓ_{p}$ regression, and logistic regression. Central to our appr…

cs.DS2020

Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression

Deeksha Adil, Richard Peng, Sushant Sachdeva

Linear regression in $ℓ_{p}$ -norm is a canonical optimization problem that arises in several applications, including sparse recovery, semi-supervised learning, and signal processi…

math.OC2026

Convex optimization with $p$ -norm oracles

Deeksha Adil, Brian Bullins, Arun Jambulapati +1

In recent years, there have been significant advances in efficiently solving $ℓ_{s}$ -regression using linear system solvers and $ℓ_{2}$ -regression [Adil-Kyng-Peng-Sachdeva, J. AC…

cs.LG2025

Efficient and Provable Algorithms for Covariate Shift

Deeksha Adil, JarosÅaw BÅasiok

Covariate shift, a widely used assumption in tackling {\it distributional shift} (when training and test distributions differ), focuses on scenarios where the distribution of the l…

math.OC2022

Optimal Methods for Higher-Order Smooth Monotone Variational Inequalities