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X-WR-CALNAME:Causal Representation Learning: Discovery of the Hidden World
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UID:2024-04-05-kun-zhang@cml.ics.uci.edu
DTSTAMP:20240405T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20240405T150000
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SUMMARY:[CML Seminar] Kun Zhang: Causal Representation Learning: Discovery
of the Hidden World
LOCATION:Donald Bren Hall 2011
DESCRIPTION:Kun Zhang\, Professor and Director\, Center for Integrative AI\
, Mohamed bin Zayed University of Artificial Intelligence\n\nTitle: Causal
Representation Learning: Discovery of the Hidden World\n\nAbstract: Causa
lity is a fundamental notion in science\, engineering\, and even in machin
e learning. Uncovering the causal process behind observed data can natural
ly help answer why and how questions\, inform optimal decisions\, and achi
eve adaptive prediction. In many scenarios\, observed variables (such as i
mage pixels and questionnaire results) are often reflections of the underl
ying causal variables rather than being the causal variables themselves. C
ausal representation learning aims to reveal the underlying high-level hid
den causal variables and their relations. The modularity property of a cau
sal system implies properties of minimal changes and independent changes o
f causal representations\, and in this talk\, we show how such properties
make it possible to recover the underlying causal representations from obs
ervational data with identifiability guarantees. Various problem settings
are considered\, involving i.i.d. data\, temporal data\, or data with dist
ribution shift as input.\n\nhttps://cml.ics.uci.edu/seminars/2024-04-05-ku
n-zhang
X-ALT-DESC;FMTTYPE=text/html:Kun Zhang\, Professor and D
irector\, Center for Integrative AI\, Mohamed bin Zayed University of Arti
ficial Intelligence
Title: Causal Representation Learning: D
iscovery of the Hidden World
Abstract: Causality is a fundam
ental notion in science\, engineering\, and even in machine learning. Unco
vering the causal process behind observed data can naturally help answer w
hy and how questions\, inform optimal decisions\, and achieve adaptive pre
diction. In many scenarios\, observed variables (such as image pixels and
questionnaire results) are often reflections of the underlying causal vari
ables rather than being the causal variables themselves. Causal representa
tion learning aims to reveal the underlying high-level hidden causal varia
bles and their relations. The modularity property of a causal system impli
es properties of minimal changes and independent changes of causal represe
ntations\, and in this talk\, we show how such properties make it possible
to recover the underlying causal representations from observational data
with identifiability guarantees. Various problem settings are considered\,
involving i.i.d. data\, temporal data\, or data with distribution shift a
s input.
https://cml.ics.uci.edu/seminars/2024-04-05-kun-zhang
URL:https://cml.ics.uci.edu/seminars/2024-04-05-kun-zhang
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