[AI Seminar] AI Seminar sponsored by Fortive on Jan 28 (NSH 3305) -- Han Zhao -- Costs and Benefits of Invariant Representation Learning

[Aayush Bansal]

Han Zhao will be giving a seminar on  "Costs and Benefits of Invariant
Representation Learning" from *12:00 - 01:00 PM* in Newell Simon Hall (NSH)
3305.

CMU AI Seminar is sponsored by Fortive. Lunch will be served.

Following are the details of the talk:

*Title: *Costs and Benefits of Invariant Representation Learning

*Abstract: *The success of supervised machine learning in recent years
crucially hinges on the availability of large-scale and unbiased data.
However, it is often time-consuming and expensive to collect such data.
Recent advances in deep learning focus on learning invariant
representations that have found abundant applications in both domain
adaptation and algorithmic fairness. However, it is not clear what price we
have to pay in terms of task utility for such universal representations. In
this talk, I will discuss my recent work on understanding and learning
invariant representations.

In the first part, I will focus on understanding the costs of existing
invariant representations by characterizing a fundamental tradeoff between
invariance and utility. In particular, I will use domain adaptation as an
example to both theoretically and empirically show such tradeoff in
achieving small joint generalization error. This result also implies that
when the base rates differ, any fair algorithm has to make a large error on
at least one of the groups.

In the second part of the talk, I will focus on designing learning
algorithms to escape the existing tradeoff and to utilize the benefits of
invariant representations. I will show how the algorithm can be used to
ensure equalized treatment of individuals between groups, and what
additional problem structure that permits efficient domain adaptation
through learning invariant representations.

*Bio*: Han Zhao is a final-year PhD student at the Machine Learning
Department, Carnegie Mellon University. At CMU, he works with Prof. Geoff
Gordon. Before coming to CMU, he obtained his BEng degree from the Computer
Science Department at Tsinghua University and MMath from the University of
Waterloo. He has a broad interest in both the theoretical and applied side
of machine learning. In particular, he works on invariant representation
learning, probabilistic reasoning with Sum-Product Networks, transfer and
multitask learning, and computational social choice. More details are here:
https://www.cs.cmu.edu/~hzhao1/

To learn more about the seminar series, please visit the website
<http://www.cs.cmu.edu/~aiseminar/>.

-- 
Aayush Bansal
http://www.cs.cmu.edu/~aayushb/
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