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Events for November 14, 2019
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PhD Thesis Proposal - Michael Tsang
Thu, Nov 14, 2019 @ 10:00 AM - 11:30 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Interpretable Machine Learning Models via Feature Interaction Discovery
Date/Time: Thursday, November 14th 10-11:30am
Location: SAL 322
Candidate: Michael Tsang
Committee: Prof. Yan Liu (adviser), Prof. Joseph Lim, Prof. Maja Mataric, Prof. Emily Putnam Hornstein, Prof. Xiang Ren
The impact of machine learning prediction models has created a growing need for us to understand why they make their predictions. The interpretation of these models is important to reveal their fundamental behavior, to obtain scientific insights into data, and to help us trust automatic predictions. In this thesis proposal, we advance these directions via the problem of feature interaction discovery. We develop a way to interpret the feature interactions in feedforward neural networks by tracing their learned weights. We follow-up on this method and develop a way of learning transparent neural networks. Lastly, we investigate applications of this work on interpreting black-box models beyond feedforward neural networks, such as image/text classifiers and recommender systems. Throughout this presentation, we will explain the physical meaning and practical importance of our feature interaction interpretations.Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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Theory Lunch
Thu, Nov 14, 2019 @ 12:15 PM - 02:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Mengxiao Zhang, CS PhD Student
Talk Title: Gradient Descent Provably Optimizes Over-Parameterized Neural Networks
Abstract: This talk is on the paper "Gradient Descent Provably Optimizes Over-Parameterized Neural Networks," which is about how techniques like gradient descent have zero training loss even for objective functions that are non-convex and non-smooth.
Host: Shaddin Dughmi
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Cherie Carter
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CS Colloquium: Bryan Perozzi (Google AI) - Machine Learning on Graphs
Thu, Nov 14, 2019 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Bryan Perozzi, Google AI
Talk Title: Machine Learning on Graphs
Series: Computer Science Colloquium
Abstract: Machine Learning on Graphs (also known as Relational Learning, or Graph-Based Machine Learning) is a branch of ML which focuses on problems where the data items (nodes) contain discrete relationships (edges) between themselves (usually in addition to traditional real-valued feature vectors). The structure of these links between unlabelled data items can be leveraged for both semi-supervised learning and unsupervised learning algorithms.
In this talk, I will provide an overview of the area, and some recent results from our team in clustering and representation learning. When appropriate, I will try to motivate our research with examples of real world problems.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Bryan Perozzi is a Senior Research Scientist in Google AI's Algorithms and Optimization group, where he routinely analyzes some of the world's largest (and perhaps most interesting) graphs. Bryan's research focuses on developing techniques for learning expressive representations of relational data with neural networks. These scalable algorithms are useful for prediction tasks (classification/regression), pattern discovery, and anomaly detection in large networked data sets.
Bryan is an author of 20+ peer-reviewed papers at leading conferences in machine learning and data mining (such as ICML, NeurIPS, KDD, and WWW). His doctoral work on learning network representations was awarded the 2017 KDD Dissertation Award. Bryan received his Ph.D. in Computer Science from Stony Brook University in 2016, and his M.S. from the Johns Hopkins University in 2011.
Host: Sami Abu-El-Haija
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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Viterbi Impact Program: Reflection Session #2
Thu, Nov 14, 2019 @ 03:30 PM - 04:30 PM
Viterbi School of Engineering Student Organizations
Workshops & Infosessions
Viterbi Impact Program participants are invited to come together, connect with others in the program, and reflect/make meaning from their experiences volunteering.
Location: Ronald Tutor Hall of Engineering (RTH) - 211
Audiences: Everyone Is Invited
Contact: Viterbi Undergraduate Programs
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Sonny Astani Civil and Environmental Engineering Seminar
Thu, Nov 14, 2019 @ 04:00 PM - 05:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. Michael Kleeman, Ph.D., University of California, Davis
Talk Title: Long-term exposure modeling for ultrafine particulate matter
Abstract: See attached
Host: Dr. George Ban-Weiss
More Information: M. Kleeman_Abstract 11-14-2019.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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Women in Engineering Meets Women in Industry
Thu, Nov 14, 2019 @ 06:00 PM - 08:00 PM
USC Viterbi School of Engineering, Viterbi School of Engineering Student Organizations
Workshops & Infosessions
Women in Engineering (WIE) Meets Women in Industry (WII) is a program designed to provide current Viterbi women the opportunity to meet with professional women in the field and gain insight on the unique challenges female engineers face. Through an engaging panel of Viterbi alumnae from varying backgrounds, participants can gain insight on the experience of transitioning from student life to professional careers, challenges women face in the workplace, and successes they-have achieved.
We are excited to have 9 alumnae on the panel from LA Sanitation and Environment (LASAN), Boeing, Lockheed Martin, Northrop Grumman, The Aerospace Corporation, MOOG, the Viterbi School of Engineering, and more.
This promises to be an amazing event with great conversations and mentorship from engineering women eager to meet you and share their experiences and wisdom! Please join us.
RSVP at http://bit.ly/wiemeetswii19
Dinner will be provided!Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Monica De Los Santos
