[The 33rd TrustML Young Scientist Seminar]

Fri, 16 Sep 2022 14:30 - 15:30 JST
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-Passcode 010651 -Time Zone:JST -The seats are available on a first-come-first-served basis. -When the seats are fully booked, we may stop accepting applications. -Simultaneous interpretation will not be available.


The TrustML Young Scientist Seminars (TrustML YSS) started from January 28, 2022.

The TrustML YSS is a video series that features young scientists giving talks and discoveries in relation with Trustworthy Machine Learning.

Timetable for the TrustML YSS online seminars from Sep. to Oct. 2022.

For more information please see the following site.

This network is funded by RIKEN-AIP's subsidy and JST, ACT-X Grant Number JPMJAX21AF, Japan.

【The 33rd Seminar】

Date and Time: September 16th 2:30 pm - 3:30 pm(JST)

Venue: Zoom webinar

Language: English

Speaker: Mahsa Baktashmotlagh (University of Queensland)
Title: Recent Advances in Domain Adaptation and Generalization
Short Abstract
Over the years, a large number of techniques have been proposed to overcome the domain shift, which is a problem of training and test data following different distributions. These techniques can be roughly grouped into two categories: Those that perform domain adaptation and those that tackle domain generalization. The former work under the assumption of observing two domains, source and target (i.e. training and test) and having access to data from both these domains during training, albeit unannotated data in the target case. By contrast, the latter make use of labeled data from multiple source domains during training and aim to generalize to unseen target data from yet another domain at test time. I will talk about our recent works in the area of domain adaptation and domain generalization.

All participants are required to agree with the AIP Seminar Series Code of Conduct.
Please see the URL below.

RIKEN AIP will expect adherence to this code throughout the event. We expect cooperation from all participants to help ensure a safe environment for everybody.

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