[Lisa_seminaires] Fwd: [Lisa_teatalk] [Tea Talk] Nicolas Le Roux (Google) May 25 2018 10:30 AM AA3195

xavier bouthillier xavier.bouthillier at gmail.com
Ven 25 Mai 10:43:08 EDT 2018


He are the slides from Nicolas' talk.

---------- Forwarded message ----------
From: Nicolas Le Roux <nlr at google.com>
Date: Fri, May 25, 2018 at 10:32 AM
Subject: Re: [Lisa_seminaires] [Lisa_teatalk] [Tea Talk] Nicolas Le Roux
(Google) May 25 2018 10:30 AM AA3195
To: xavier.bouthillier at gmail.com




On Fri, 25 May 2018 at 10:16, xavier bouthillier <
xavier.bouthillier at gmail.com> wrote:

> Salut Nicolas,
>
> Pour le streaming tu peux te connecter à l'adresse suivante:
> https://bluejeans.com/809027115/webrtc
> On va faire un share-screen pour partager tes slides en temps réel. Je
> vais seulement streamer, je n'enregistrerai pas.
>
> Le local 3195 est ouvert, tu peux venir t'installer quand tu veux.
>
> Xavier
>
> On Tue, May 22, 2018 at 12:45 PM, Nicolas Le Roux <nlr at google.com> wrote:
>
>> For those of you who attended my talk at the Workshop on learning theory,
>> this one will fill in some of the gaps I mentioned.
>>
>> On Tue, 22 May 2018 at 12:17, Michael Noukhovitch <mnoukhov at gmail.com>
>> wrote:
>>
>>> This week we will have two tea talks (morning and afternoon) on Friday!
>>> Wow!
>>>
>>> The first talk will be *Nicolas Le* *Roux * on *May 25 2018* at *10:30
>>> AM* in room *AA3195* (note the room change, we'll most likely be in
>>> 3195 for the summer).
>>>
>>> Streaming is, as always, at this link: https://bluejeans.com/
>>> 809027115/webrtc but watch it live because there will be no recordings!
>>>
>>> Don't waver on going to this talk, it is sure to be invariantly
>>> excellent!
>>> Michael
>>>
>>> *TITLE* An exploration of variance reduction techniques in stochastic
>>> optimization
>>>
>>> *KEYWORDS *deep learning theory, optimization
>>>
>>> *ABSTRACT*
>>> I will present recent and ongoing work on reducing the variance in
>>> stochastic optimization techniques to speed-up and simplify the resulting
>>> algorithms. In particular, stochastic gradient methods can suffer from high
>>> variance, limiting their convergence speed. While variance reduction
>>> techniques exist in the finite case, they are rarer in the online case. We
>>> demonstrate how an increasing momentum offers variance reduction in the
>>> online case, at the expense of bias, and how that bias can be countered by
>>> an extrapolation step. The resulting algorithm differs from iterate
>>> averaging in only a factor, but, in the context of the minimization of a
>>> quadratic function, this difference is enough to lead to the first
>>> algorithm converging both linearly in the noiseless and sublinearly in the
>>> homoscedastic noise case when using a constant stepsize.
>>>
>>> *BIO*
>>> *Nicolas Le Roux got an MSc in Applied Maths from Ecole Centrale Paris
>>> and an MSc in Maths, Learning and Vision from ENS Cachan. He got his PhD in
>>> 2008 from University of Montreal where he worked with Yoshua Bengio on
>>> neural networks in general and their optimisation in particular. He then
>>> moved to Microsoft Research Cambridge to work on generative models of
>>> images with John Winn. In 2010, he joined Inria in Francis Bach's team to
>>> work on large-scale convex optimisation. From 2012 to 2017, he created and
>>> managed the research team at Criteo in Paris. He joined Google Brain
>>> Montreal in 2017 where he now works on large-scale optimization and
>>> reinforcement learning.*
>>>
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>>
>>
>> --
>> Nicolas Le Roux
>>
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>

-- 
Nicolas Le Roux
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