[Priberam ML Seminars] Priberam Machine Learning Lunch Seminars (T11) - 9 - "Preference Modeling with Context-Dependent Salient Features", Laura Balzano (U. Michigan)
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Rúben Cardoso
Jun 23, 2020, 1:27:22 PM6/23/20
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Hello all,
Hope you are all safe and healthy, the Priberam Machine Learning Seminars will continue to take place remotely via zoom on Tuesdays.
Next Tuesday, June 30th,
Laura Balzano, an associate professor in Electrical Engineering and Computer Science at the University of Michigan will present her work "Preference Modeling with Context-Dependent Salient Features" at
14:30h (zoom link: https://zoom.us/j/89697809357
). Please notice that the time is different from the usual.
You can register for this event and keep watch on future seminars below:
Food will not be provided but feel free to eat at the same time :) Please note that the seminar is limited to 100 people
and this will work on a 1st come 1st served basis. So please try to be on time if you wish to attend.
Priberam is hiring!
If you are interested in working with us please consult the available positions at priberam.com/careers.
PRIBERAM SEMINARS
-- Zoom
89697809357__________________________________________________
Priberam Machine Learning Lunch Seminar
Speaker: Laura Balzano (U. Michigan)
Venue: https://zoom.us/j/89697809357
Date: Tuesday, June 30th, 2020
Time: 14:30
Title:
Preference
Modeling with Context-Dependent Salient Features
Abstract:
This talk considers the preference modeling problem
and addresses the fact that pairwise comparison data often reflects irrational choice, e.g. intransitivity. Our key observation is that two items compared in isolation from other items may be compared based on only a salient subset of features. Formalizing
this idea, I will introduce our proposal for a “salient feature preference model” and discuss sample complexity results for learning the parameters of our model and the underlying ranking with maximum likelihood estimation. I will also provide empirical results
that support our theoretical bounds, illustrate how our model explains systematic intransitivity, and show in this setting that our model is able to recover both pairwise comparisons and rankings for unseen pairs or items. Finally I will share results on two
data sets: the UT Zappos50K data set and comparison data about the compactness of legislative districts in the US. This is joint work with Amanda Bower at the University of Michigan, accepted to ICML.
Short Bio:
Laura
Balzano is an associate professor in Electrical Engineering and Computer Science at the University of Michigan, and a member of the Institute for Advanced Study for the special year on Optimization, Statistics, and Theoretical Machine Learning. She is a recipient
of the NSF Career Award, a Fulbright fellowship, ARO Young Investigator Award, AFOSR Young Investigator Award, and faculty fellowships from Intel and 3M. Laura received a BS from Rice University, MS from UCLA, and PhD from the University of Wisconsin, all
in Electrical and Computer Engineering. Her main research focus is on modeling with big, messy data — highly incomplete or corrupted data, uncalibrated data, and heterogeneous data — and its applications in machine learning, environmental monitoring, and computer
vision. Her expertise is in statistical signal processing, matrix factorization, and optimization.
Eventbrite:
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