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X-WR-CALNAME:Ideal made real: machine learning with limited data and interp
retable outputs
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UID:2021-05-10-david-alvarez-melis@cml.ics.uci.edu
DTSTAMP:20210510T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20210510T130000
DTEND;TZID=America/Los_Angeles:20210510T140000
SUMMARY:[CML Seminar] David Alvarez-Melis: Ideal made real: machine learnin
g with limited data and interpretable outputs
LOCATION:Online (live stream)
DESCRIPTION:David Alvarez-Melis\, Postdoctoral Researcher\, Microsoft Resea
rch New England\n\nTitle: Ideal made real: machine learning with limited d
ata and interpretable outputs\n\nAbstract: Success stories in machine lear
ning tend to be concentrated on ideal scenarios where clean labeled data a
re abundant and constraints are rare. But machine learning in practice is
rarely so pristine. In this talk we explore how to reconcile these along t
wo axes: learning with scarce or heterogeneous data\, and making complex m
odels interpretable. First\, I present approaches for amplifying datasets
based on the theory of Optimal Transport\, with applications in machine tr
anslation\, transfer learning\, and dataset shaping. Second\, I present wo
rk on designing methods to extract explanations from complex models and a
novel framework for interpretable machine learning inspired by the study o
f human explanation in the social sciences.\n\nhttps://cml.ics.uci.edu/sem
inars/2021-05-10-david-alvarez-melis
X-ALT-DESC;FMTTYPE=text/html:David Alvarez-Melis\, Postd
octoral Researcher\, Microsoft Research New England
Title: I
deal made real: machine learning with limited data and interpretable outpu
ts
Abstract: Success stories in machine learning tend to be
concentrated on ideal scenarios where clean labeled data are abundant and
constraints are rare. But machine learning in practice is rarely so pristi
ne. In this talk we explore how to reconcile these along two axes: learnin
g with scarce or heterogeneous data\, and making complex models interpreta
ble. First\, I present approaches for amplifying datasets based on the the
ory of Optimal Transport\, with applications in machine translation\, tran
sfer learning\, and dataset shaping. Second\, I present work on designing
methods to extract explanations from complex models and a novel framework
for interpretable machine learning inspired by the study of human explanat
ion in the social sciences.
https://cml.ics.uci.edu/seminars/2021-0
5-10-david-alvarez-melis
URL:https://cml.ics.uci.edu/seminars/2021-05-10-david-alvarez-melis
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