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Events for March 09, 2020

  • CS Colloquium: Lili Su (MIT) - Learning with Distributed Systems: Adversary-Resilience and Neural Networks

    Mon, Mar 09, 2020 @ 11:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Lili Su, MIT

    Talk Title: Learning with Distributed Systems: Adversary-Resilience and Neural Networks

    Series: CS Colloquium

    Abstract: In this talk, I will first talk about how to secure Federated Learning (FL) against adversarial faults.
    FL is a new distributed learning paradigm proposed by Google. The goal of FL is to enable the cloud (i.e., the learner) to train a model without collecting the training data from users' mobile devices. Compared with traditional learning, FL suffers serious security issues and several practical constraints call for new security strategies. Towards quantitative and systematic insights into the impacts of those security issues, we formulated and studied the problem of Byzantine-resilient Federated Learning. We proposed two robust learning rules that secure gradient descent against Byzantine faults. The estimation error achieved under our more recently proposed rule is order-optimal in the minimax sense.

    Then, I will briefly talk about our recent results on neural networks, including both biological and artificial neural networks. Notably, our results on the artificial neural networks (i.e., training over-parameterized 2-layer neural networks) improved the state-of-the-art. In particular, we showed that nearly-linear network over-parameterization is sufficient for the global convergence of gradient descent.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Lili Su is a postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT, hosted by Professor Nancy Lynch. She received a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2017, supervised by Professor Nitin H. Vaidya. Her research intersects distributed systems, learning, security, and brain computing. She was the runner-up for the Best Student Paper Award at DISC 2016, and she received the 2015 Best Student Paper Award at SSS 2015. She received UIUC's Sundaram Seshu International Student Fellowship for 2016, and was invited to participate in Rising Stars in EECS (2018). She has served on TPC for several conferences including ICDCS and ICDCN.

    Host: Leana Golubchik

    Location: Ronald Tutor Hall of Engineering (RTH) - 109

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

  • PhD Defense - Johnathan Mell

    Mon, Mar 09, 2020 @ 11:00 AM - 01:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Candidate: Johnathan Mell
    Date: Monday, March 9th, 2020
    Time: 11:00 AM - 1:00 PM
    Location: SAL 213
    Committee: Dr. Jonathan Gratch (Chair), Dr. Nate Fast, Dr. Sven Koenig

    Title: A Framework for Research in Human-Agent Negotiation


    Increasingly, automated agents are interacting with humans in highly social interactions. Many of these interactions can be characterized as negotiation tasks. There has been broad research in negotiation techniques between humans (in business literatures, e.g.), as well a great deal of work in creating optimal agents that negotiate with each other. However, the creation of effective socially-aware agents requires fundamental basic research on human-agent negotiation. Furthermore, this line of enquiry requires highly customizable, fully-interactive systems that are capable of enabling and implementing human-agent interaction. Previous attempts that rely on hypothetical situations or one-shot studies are insufficient in capturing truly social behavior.

    This dissertation showcases my invention and development of the Interactive Arbitration Guide Online (IAGO) platform, which enables rigorous human-agent research. IAGO has been designed from the ground up to embody core principles gleaned from the rich body of research on how people actually negotiate. I demonstrate several examples of how IAGO has already yielded fundamental contributions towards our understanding of human-agent negotiation. I also demonstrate how IAGO has contributed to a community of practice by allowing researchers across the world to easily develop and investigate novel algorithms. Finally, I discuss future plans to use this framework to explore how humans and machines can establish enduring and profitable relationships through repeated negotiations.

    Location: Henry Salvatori Computer Science Center (SAL) - 213

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

  • Spring 2020 Joint CSC@USC/CommNetS-MHI Seminar Series

    Mon, Mar 09, 2020 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars

    Speaker: Bruno Ribeiro, Purdue University

    Talk Title: Unearthing the relationship between graph neural networks and matrix factorization

    Abstract: Graph tasks are ubiquitous, with applications ranging from recommendation systems, to language understanding, to automation with environmental awareness and molecular synthesis. A fundamental challenge in applying machine learning to these tasks has been encoding (representing) the graph structure in a way that ML models can easily exploit the relational information in the graph, including node and edge features. Until recently, this encoding has been performed by factor models (a.k.a. matrix factorization embeddings), which arguably originated in 1904 with Spearman's common factors. Recently, however, graph neural networks have introduced a new powerful way to encode graphs for machine learning models. In my talk, I will describe these two approaches and then introduce a unifying mathematical framework using group theory and causality that connects them. Using this novel framework, I will introduce new practical guidelines to generating and using node embeddings and graph representations, which fixes significant shortcomings of the standard operating procedures used today.

    Biography: Bruno Ribeiro is an Assistant Professor in the Department of Computer Science at Purdue University. He obtained his Ph.D. at the University of Massachusetts Amherst and did his postdoctoral studies at Carnegie Mellon University from 2013-2015. His research interests are in representation learning and data mining, with a focus on sampling and modeling relational and temporal data. He received an NSF CAREER award in 2020 and the ACM SIGMETRICS best paper award in 2016.

    Host: Prof. Antonio Ortega, aortega@usc.edu

    More Info: http://csc.usc.edu/seminars/2020Spring/ribeiro.html

    More Information: 200309_Bruno Ribeiro_CSC Seminar.pdf

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

    Audiences: Everyone Is Invited

    Contact: Brienne Moore

    Event Link: http://csc.usc.edu/seminars/2020Spring/ribeiro.html

  • Intro to Computer Vison with OpenCV AAAI x USC Makers

    Mon, Mar 09, 2020 @ 06:30 PM - 07:30 PM

    Viterbi School of Engineering Student Organizations

    Workshops & Infosessions

    AAAI Workshop in Collaboration with USC Makers:
    Intro to Computer Vision with OpenCV

    Computer vision is one of the hottest areas of AI research in 2020, in large part due to its massive potential to solve problems in industry. From self-driving cars and automated grocery stores to radiology and agriculture, computer vision is shaking up every corner of the industrial world. Plus, computer vision projects are super fun!

    In this workshop, you will learn the ropes of OpenCV in Python, a powerful computer vision library originally developed by Intel that is now free and open-source. OpenCV has the benefit of pre-trained machine learning models for both still images and video, allowing you to power your next project with powerful and optimized computer vision AI.


    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

    Audiences: Everyone Is Invited

    Contact: USC AAAI