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Events for March 24, 2020

  • Seminar will be exclusively online (no in-room presentation) - CS Colloquium: Simon S. Du (Princeton University) - Foundations of Learning Systems with (Deep) Function Approximators

    Tue, Mar 24, 2020 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Simon S. Du, Princeton University

    Talk Title: Foundations of Learning Systems with (Deep) Function Approximators

    Series: CS Colloquium

    Abstract: Function approximators, such as deep neural networks, play a crucial role in building learning systems that make predictions and decisions. In this talk, I will discuss my work on understanding, designing, and applying function approximators.

    First, I will focus on understanding deep neural networks. The main result is that the over-parameterized neural network is equivalent to a new kernel, Neural Tangent Kernel. This equivalence implies two surprising phenomena: 1) the simple algorithm gradient descent provably finds the global optimum of the highly non-convex empirical risk, and 2) the learned neural network generalizes well despite being highly over-parameterized. Furthermore, this equivalence helps us design a new class of function approximators: we transform (fully-connected and graph) neural networks to (fully-connected and graph) Neural Tangent Kernels, which achieve superior performance on standard benchmarks.

    In the second part of the talk, I will focus on applying function approximators to decision-making, aka reinforcement learning, problems. In sharp contrast to the (simpler) supervised prediction problems, solving reinforcement learning problems requires an exponential number of samples, even if one applies function approximators. I will then discuss what additional structures that permit statistically efficient algorithms.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Simon S. Du is a postdoc at the Institute for Advanced Study of Princeton, hosted by Sanjeev Arora. He completed his Ph.D. in Machine Learning at Carnegie Mellon University, where he was co-advised by Aarti Singh and Barnabás Póczos. Previously, he studied EECS and EMS at UC Berkeley. He has also spent time at Simons Institute and research labs of Facebook, Google, and Microsoft. His research interests are broadly in machine learning, with a focus on the foundations of deep learning and reinforcement learning.

    Host: Haipeng Luo

    Location: Seminar will be exclusively online (no in-room presentation)

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

  • ECE Seminar: Reliability, Equity, and Reproducibility in Modern Machine Learning

    Tue, Mar 24, 2020 @ 11:00 AM - 12:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars

    Speaker: Yaniv Romano, Postdoctoral Scholar, Dept of Statistics, Stanford University

    Talk Title: Reliability, Equity, and Reproducibility in Modern Machine Learning

    Abstract: Modern machine learning algorithms have achieved remarkable performance in a myriad of applications, and are increasingly used to make impactful decisions in the hiring process, criminal sentencing, healthcare diagnostics and even to make new scientific discoveries. The use of data-driven algorithms in high-stakes applications is exciting yet alarming: these methods are extremely complex, often brittle, notoriously hard to analyze and interpret. Naturally, concerns have raised about the reliability, fairness, and reproducibility of the output of such algorithms. This talk introduces statistical tools that can be wrapped around any "black-box" algorithm to provide valid inferential results while taking advantage of their impressive performance. We present novel developments in conformal prediction and quantile regression, which rigorously guarantee the reliability of complex predictive models, and show how these methodologies can be used to treat individuals equitably. Next, we focus on reproducibility and introduce an operational selective inference tool that builds upon the knockoff framework and leverages recent progress in deep generative models. This methodology allows for reliable identification of a subset of important features that is likely to explain a phenomenon under-study in a challenging setting where the data distribution is unknown, e.g., mutations that are truly linked to changes in drug resistance.

    Biography: Yaniv Romano is a postdoctoral scholar in the Department of Statistics at Stanford University, advised by Prof. Emmanuel Candes. He earned his Ph.D. and M.Sc. degrees in 2017 from the Department of Electrical Engineering at the Technion-”Israel Institute of Technology, under the supervision of Prof. Michael Elad. Before that, in 2012, Yaniv received his B.Sc. from the same department. His research spans the theory and practice of selective inference, sparse approximation, machine learning, data science, and signal and image processing. His goal is to advance the theory and practice of modern machine learning, as well as to develop statistical tools that can be wrapped around any data-driven algorithm to provide valid inferential results. Yaniv is also interested in image recovery problems: the super-resolution technology he invented together with Dr. Peyman Milanfar is being used in Google's flagship products, increasing the quality of billions of images and bringing significant bandwidth savings. In 2017, he constructed with Prof. Michael Elad a MOOC on the theory and practice of sparse representations, under the edX platform. Yaniv is a recipient of the 2015 Zeff Fellowship, the 2017 Andrew and Erna Finci Viterbi Fellowship, the 2017 Irwin and Joan Jacobs Fellowship, the 2018-2020 Zuckerman Postdoctoral Fellowship, the 2018-2020 ISEF Postdoctoral Fellowship, the 2018-2020 Viterbi Fellowship for nurturing future faculty members, Technion, and the 2019-2020 Koret Postdoctoral Scholarship, Stanford University. Yaniv was awarded the 2020 SIAG/IS Early Career Prize.

    Host: Salman Avestimehr, avestime@usc.edu

    Webcast: https://usc.zoom.us/j/782728120

    WebCast Link: https://usc.zoom.us/j/782728120

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

  • Undergraduate Admission Virtual Information Session

    Tue, Mar 24, 2020 @ 02:00 PM - 03:00 PM

    Viterbi School of Engineering Undergraduate Admission

    Workshops & Infosessions

    Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for prospective first-year students and their family members to learn more about the USC Viterbi undergraduate experience.Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life.Guests will be able to ask questions and engage in further discussion toward the end of the session.

    Please register here!

    Audiences: Everyone Is Invited

    Contact: Viterbi Admission

  • **CANCELED** SE 651 - Epstein Seminar

    Tue, Mar 24, 2020 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars

    Speaker: Dr. Adam Elmachtoub, Assistant Professor, Columbia University

    Talk Title: TBD

    Host: Dr. Phebe Vayanos

    Location: Ethel Percy Andrus Gerontology Center (GER) - 206

    Audiences: Everyone Is Invited

    Contact: Grace Owh