Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more In My Bag Say More Trending Now Mashable Selects Creator Playbook Back to School Good Connection: Uplifting stories for a digital age Switch Off Mashable Voices Safety Net All Series Elisha Sauers writes about space for Mashable, taking deep dives into NASA's moon and Mars missions, chatting up astronauts and history-making discoverers, and jetting above the clouds . Through 17 years of reporting, she's covered a variety of topics, including health, business, and government, with a penchant for public records requests. She previously worked for in Norfolk, Virginia, and in Annapolis, Maryland. Her work has earned numerous state awards, including the Virginia Press Association's top honor, Best in Show, and national recognition for narrative storytelling. For each year she has covered space, Sauers has won National Headliner Awards, including first place for her Sex in Space series.

For additional motivation, it is reasonable to consider Massart noise to be a more realistic model of real-life noise (even when benign) when compared to the RCN model, as it allows for some amount of non-uniformity. This made Definition 1 a possibly tractable way to relax the noise assumption, without running intotheaforementioned computational barriers foragnostic learning.

We give the first polynomial-time algorithm for robust regression in the listdecodable setting where an adversary can corrupt a greater than1/2 fraction ofexamples.

On the positive side, we give anα = 1.01-approximate proper learner that uses O(1/( 2γ2)) samples (which is optimal) and runs in timepoly(d/) 2 O(1/γ

Given 2 (0,12] and 2 (0,1), letT bethesamplesetgiventothe learner.

In the first model, we are givenn i.i.d.

In the first model, we are givenn i.i.d.

Insuch situations, weallowthealgorithm toreturn asmall listofcandidates such that one of the candidates is close to the true parameter.

Inthisbasicsetting, linear regression is well-understood.

Robust learning is a critical field that seeks to develop efficient algorithms that can recover an underlying model despite possibly malicious corruptions in the data. In recent decades, being able to deal with corrupted measurements has become of crucial importance.