革新知能統合研究センター

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 Jan. to Feb. 2023.

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 48th Seminar】

Date and Time: January 11th 4:00 pm – 5:00 pm(JST)

Venue: Zoom webinar

Language: English

Speaker: Liyuan Xu (Gatsby Computational Neuroscience Unit)
Title: Learning Deep Feature in Causal Inference with Unobserved Confounder
Short Abstract
In order to build a trust-worthy machine learning model, it is essential to model the causal relationships between the action, or treatments, on the outcome. This is challenging when there exist unobserved confounders, which affect both treatments and the outcome and cause bias in the estimation. In this talk, we will introduce two causal methods to deal with this; One is instrumental variable regression, which uses the variable that affects the outcome only through the treatment. The other is proxy causal learning, which uses proxies, the structured side information for the confounders. We propose to learn neural network features in these methods by alternatively solving the regression problems, which avoids the need for sampling or density estimation. We also point out that these methods can be applied to the problems in reinforcement learning, such as offline-policy evaluations and confounded bandit problems.

Bio:
Liyuan Xu is a Ph.D. student at Gatsby Computational Neuroscience Unit and supervised by Prof. Arthur Gretton. He is interested in machine learning problems related to decision-making, specifically, multi-armed bandit, causal inference, and reinforcement learning. Liyuan was a part-time assistance support worker at RIKEN AIP and received his Bachelor’s and Master’s degrees from the University of Tokyo.

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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.