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[The 30th TrustML Young Scientist Seminar]

Fri, 02 Sep 2022 15:00 - 16:00 JST
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-Passcode 857973 -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.

Description

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.
TrustML YSS

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


【The 30th Seminar】


Date and Time: September 2nd 3:00 pm - 4:00 pm(JST)

Venue: Zoom webinar

Language: English

Speaker: Chirag Agarwal (Adobe)
Title: On the Impact of Estimating Example Difficulty
Short Abstract
In machine learning, a question of great interest is understanding what examples are challenging for a model to classify and what are the advantages of identifying such challenging examples. Identifying atypical examples ensures the safe deployment of models, isolates samples that require further human inspection, and provides interpretability into model behavior. In this talk, we will be discussing i) Variance of Gradients (VoG), a valuable and efficient metric to rank data by difficulty and surface a tractable subset of the most challenging examples for human-in-the-loop auditing, and ii) the utility of such harder subset in a transfer learning setting. In particular, I will be showing how harder subsets are a better measure to estimate the transferability from a source to a target dataset.


All participants are required to agree with the AIP Seminar Series Code of Conduct.
Please see the URL below.
https://aip.riken.jp/event-list/termsofparticipation/?lang=en

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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