Re: [AIRG] Robust regression via hard thresholding

[Aubrey Barnard <barnard@xxxxxxxxxxx>]

AIRG,

Today Xiaomin Zhang will be talking to us about robust regression. This 
particular approach achieves a consistent estimator in the face of 
adversarial data corruption. Moreover, a variant based on gradient 
descent is fast and scalable. (They claim 20x speedup compared to the 
best L1 solver.)

4pm, CS 3310

https://papers.nips.cc/paper/6806-consistent-robust-regression
https://arxiv.org/abs/1506.02428

(While I normally prefer the canonical version from the publisher, in 
this case I think the arXiv version is easier to read.)

Aubrey


On 4/15/19 11:08 PM, XIAOMIN ZHANG via AIRG wrote:
> Hi AIRG,
> 
> I will talk about robust regression this Wednesday, which does 
> much better than standard regression at handling noisy data, corrupted 
> data, or outliers. Robust regression via convex methods may not be 
> consistent. However, non-convex methods can be consistent. I will focus 
> on hard thresholding for robust regression.
> 
> References are the following papers:
> 1. https://arxiv.org/pdf/1506.02428.pdf
> 2. https://papers.nips.cc/paper/6806-consistent-robust-regression.pdf
> 
> See you at 4pm Wednesday, CS 4310.,
> Xiaomin
> 
> 
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