[CMU AI Seminar] Apr 6 at 12pm (Zoom) -- Elan Rosenfeld (CMU MLD) -- The Risks of Invariant Risk Minimization -- AI Seminar sponsored by Fortive

[Shaojie Bai]

Reminder: This talk is happening now! Please join us at
https://cmu.zoom.us/j/96139997861?pwd=ZlMrUUZaWXY0Sm9mai9ZdjE4QXNDQT09.

On Mon, Apr 5, 2021 at 1:26 PM Shaojie Bai <shaojieb at andrew.cmu.edu> wrote:

> Dear all,
>
> *NOTE/UPDATE*: This seminar is tomorrow at *1pm* (due to a faculty job
> talk at 12pm).
>
> Just a reminder that the CMU AI Seminar
> <http://www.cs.cmu.edu/~aiseminar/> is tomorrow *1pm-2pm*:
> https://cmu.zoom.us/j/96139997861?pwd=ZlMrUUZaWXY0Sm9mai9ZdjE4QXNDQT09.
>
> Elan Rosenfeld (CMU MLD) will be talking about his latest work on
> invariant risk minimization (IRM).
>
> Thanks,
> Shaojie
>
> On Tue, Mar 30, 2021 at 4:14 PM Shaojie Bai <shaojieb at andrew.cmu.edu>
> wrote:
>
>> Dear all,
>>
>> We look forward to seeing you *next Tuesday (4/6)* from *1**2:00-1:00 PM
>> (U.S. Eastern time)* for the next talk of our *CMU AI seminar*,
>> sponsored by Fortive <https://careers.fortive.com/>.
>>
>> To learn more about the seminar series or see the future schedule, please
>> visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.
>> <http://www.cs.cmu.edu/~aiseminar/>
>>
>> On 4/6, *Elan Rosenfeld* (CMU MLD) will be giving a talk on "*The Risks
>> of Invariant Risk Minimization*."
>>
>> *Title*: The Risks of Invariant Risk Minimization
>>
>> *Talk Abstract*: Invariant feature learning has become a popular
>> alternative to Empirical Risk Minimization as practitioners recognize the
>> need to ignore features which may be misleading at test time in order to
>> improve out-of-distribution generalization. Early results in this area
>> leverage variation across environments to provably identify the features
>> which are directly causal with respect to the target variable. More recent
>> work attempts to use this technique for deep learning, frequently with no
>> formal guarantees of an algorithm's ability to uncover the correct
>> features. Most notably, the seminal work introducing Invariant Risk
>> Minimization gave a loose bound for the linear setting and no results at
>> all for non-linearity; despite this, a large number of variations have been
>> suggested. In this talk, I'll introduce a formal latent variable model
>> which encodes the primary assumptions made by these works. I'll then give
>> the first characterization of the optimal solution to the IRM objective,
>> deriving the exact number of environments needed for the solution to
>> generalize in the linear case. Finally, I'll present the first analysis of
>> IRM when the observed data is a non-linear function of the latent
>> variables: in particular, we show that IRM can fail catastrophically when
>> the test distribution is even moderately different from the training
>> distribution - this is exactly the problem that IRM was intended to solve.
>> These results easily generalize to all recent variations on IRM,
>> demonstrating that these works on invariant feature learning fundamentally
>> do not improve over standard ERM. This talk is based on work with Pradeep
>> Ravikumar and Andrej Risteski, to appear at ICLR 2021.
>>
>> *Speaker Bio*: Elan Rosenfeld is a PhD student in the Machine Learning
>> Department at CMU, advised by Andrej Risteski and Pradeep Ravikumar. He is
>> interested in theoretical foundations of machine learning, with a
>> particular focus on robust learning, representation learning and
>> out-of-distribution generalization. Elan completed his undergraduate
>> degrees in Computer Science and Statistics & Machine Learning at CMU, where
>> his senior thesis on human-usable password schemas was advised by Manuel
>> Blum and Santosh Vempala.
>>
>> *Zoom Link*:
>> https://cmu.zoom.us/j/96139997861?pwd=ZlMrUUZaWXY0Sm9mai9ZdjE4QXNDQT09
>>
>>
>> Thanks,
>> Shaojie Bai (MLD)
>>
>
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