[AI Seminar] AI Seminar sponsored by Apple -- Han Zhao -- April 03

[Adams Wei Yu]

Dear faculty and students,

We look forward to seeing you next Tuesday, April 03, at noon in *NSH 1507* for
AI Seminar sponsored by Apple. To learn more about the seminar series,
please visit the AI Seminar webpage <http://www.cs.cmu.edu/~aiseminar/>.

On Tuesday,  Han Zhao <https://www.cs.cmu.edu/~hzhao1/> will give the
following talk:

Title:  Multiple Source Domain Adaptation with Adversarial Learning

Abstract:

While domain adaptation has been actively researched, most theoretical
results and algorithms focus on the single-source-single-target adaptation
setting. In the first part of the talk, I will discuss new generalization
bounds for domain adaptation when there are multiple source domains with
labeled instances and one target domain with unlabeled instances. The
theory also leads to an efficient learning strategy using adversarial
neural networks: I will show how to interpret it as learning feature
representations that are invariant to the multiple domain shifts while
still being discriminative for the learning task.

In the second part, I will discuss two models for multisource domain
adaptations: the first model optimizes the worst-case bound, while the
second model is a smoothed approximation of the first one and optimizes a
task-adaptive bound. We also demonstrate the effectiveness of both models
by conducting extensive experiments showing superior adaptation performance
on three real-world datasets: sentiment analysis, digit classification, and
vehicle counting.

This talk includes joint work with Shanghang Zhang, Guanhang Wu, Joao
Costeira, Jose Moura and Geoff Gordon.
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