Fwd: Thesis Defense - June 29, 2020 - Yichong Xu - Learning and Decision Making from Diverse Forms of Information

[Artur Dubrawski]

A reminder about Yichong's defense coming up this Monday at 9am.

All details below, it will be a fun talk so please come and join if you can!

Cheers,
Artur

---------- Forwarded message ---------
From: Diane Stidle <stidle at andrew.cmu.edu>
Date: Mon, Jun 15, 2020 at 1:49 PM
Subject: Thesis Defense - June 29, 2020 - Yichong Xu - Learning and
Decision Making from Diverse Forms of Information
To: ml-seminar at cs.cmu.edu <ML-SEMINAR at cs.cmu.edu>, <jcl at microsoft.com>


*Thesis Defense*

Date: June 29, 2020
Time: 9:00am (EDT)
PhD Candidate: Yichong Xu

Virtual Presentation Link:
https://cmu.zoom.us/j/99909151454

*Title:  *Learning and Decision Making from Diverse Forms of Information

Abstract:
Classical machine learning posits that data are independently and
identically distributed, in a single format usually the same as test data.
In modern applications however, additional information in other formats
might be available freely or at a lower cost. For example, in data
crowdsourcing we can collect preferences over the data points instead of
directly asking the labels of a single data point at a lower cost. In
natural language understanding problems, we might have limited amount of
data in the target domain, but can use a large amount of general domain
data for free.

The main topic of this thesis is to study how to efficiently incorporate
these diverse forms of information into the learning and decision making
process. We study two representative paradigms in this thesis. Firstly, we
study learning and decision making problems with direct labels and
comparisons. Our algorithms can efficiently combine comparisons with direct
labels so that the total learning cost can be greatly reduced. Secondly, we
study multi-task learning problems from multiple domain data, and design
algorithms to transfer the data from a general, abundant domain to the
target domain. We show theoretical guarantees of our algorithms as well as
their statistical minimaxity through information-theoretic limits. On the
practical side, we demonstrate promising experimental results on price
estimation and natural language understanding tasks.

*Thesis Committee:*
Artur Dubrawski, Co-Chair
Aarti Singh, Co-Chair
Sivaraman Balakrishnan
John Langford (Microsoft Research)

-- 
Diane Stidle
Graduate Programs Manager
Machine Learning Department
Carnegie Mellon Universitystidle at andrew.cmu.edu
412-268-1299
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