Re: [AIRG] Robust regression via hard thresholding

[Aubrey Barnard <barnard@xxxxxxxxxxx>]

AIRG,

I just noticed that the NIPS and arXiv links are for different papers, 
one a predecessor of the other. I apologize for misleading anyone.

1. Robust Regression via Hard Thresholding
NIPS 2015
https://papers.nips.cc/paper/6010-robust-regression-via-hard-thresholding
https://arxiv.org/abs/1506.02428

2. Consistent Robust Regression
NIPS 2017
https://papers.nips.cc/paper/6806-consistent-robust-regression
(I didn't find a version on arXiv.)

It's only the second paper that has the benefits that Xiaomin and I 
mentioned, so read the second paper. (But don't worry if you have 
already read the first paper as I suggested.)

Aubrey


On 4/17/19 10:44 AM, Aubrey Barnard wrote:
> 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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