# Deeksha Adil: Fast Algorithms for l_p-Regression and Other Problems

Regression in $\ell_p$-norms is a canonical problem in optimization, machine learning, and theoretical computer science. In this talk, I will describe our recent advances in developing fast, high-accuracy algorithms both in theory and practice. Our algorithms are based on a few novel techniques which I will go over briefly and are the fastest available both in theory and practice. Our algorithms for $\ell_p$-regression also imply fast algorithms for the p-norm flow problem and an $m^{1+o(1)}\epsilon^{-1}$ time algorithm for the maximum flow problem on unit capacity graphs, matching the best-known bounds for this problem.