Past Events

9:00 AM - 10:00 AM Central (CDT; UTC -5) Access: Seminar will be delivered live on Wednesday, March 31, 2021 at 9:00 AM Central (CDT; UTC -5) via the following link: Zoom A Zoom account is required in order to access the seminar. Zoom is accessible to UT faculty, staff, and students with support from ITS. Otherwise, you can sign up for a free personal account on the Zoom website.

9:00 AM - 10:00 AM Central (CDT; UTC -5)   Access: Seminar will be delivered live on Friday, March 26, 2021 PM Central (CDT; UTC -5) via the following link: Zoom link A Zoom account is required in order to access the seminar. Zoom is accessible to UT faculty, staff, and students with support from ITS. Otherwise, you can sign up for a free personal account on the Zoom website(link is external).

9:00AM - 10:00AM U.S. Central Time (CST / UTC -6) Seminar will be delivered live via Zoom on Friday, March 12 at the date and time above. The Zoom conferencing system is accessible to UT faculty, staff, and students with support from ITS. Otherwise, you can sign up for a free account on the Zoom website.

Join this virtual event to learn the behind-the-scenes stories of e-tattoo research and development.

Graph Neural Networks (GNNs) have become a popular tool for learning representations of graph-structured inputs, with applications in computational chemistry, recommendation, pharmacy, reasoning, and many other areas.

Seminar will be delivered live via Zoom on Thursday, November 5, 2020 at 11:00AM - 12:00PM U.S. Central Time (CST / UTC -6). The Zoom conferencing system is accessible to UT faculty, staff, and students with support from ITS. Otherwise, you can sign up for a free account on the Zoom website.

Seminar will be delivered live via Zoom on Friday, October 30, 2020 at 9:00AM - 10:00AM U.S. Central Time (CDT / UTC -5). The Zoom conferencing system is accessible to UT faculty, staff, and students with support from ITS. Otherwise, you can sign up for a free account on the Zoom website.

Machine learning as a service (MLaaS) has emerged as a paradigm allowing clients to outsource machine learning computations to the cloud. However, MLaaS raises immediate security concerns, specifically relating to the integrity (or correctness) of computations performed by an untrusted cloud, and the privacy of the client’s data. In this talk, I discuss frameworks we built on cryptographic tools that can be used for secure deep learning based inference on an untrusted cloud: CryptoNAS (building models for private inference) and SafetyNets (addressing correctness).

The focus of our work is to obtain finite-sample and/or finite-time convergence bounds of various model-free Reinforcement Learning (RL) algorithms. Many RL algorithms are special cases of Stochastic Approximation (SA), which is a popular approach for solving fixed point equations when the information is corrupted by noise. We first obtain finite-sample bounds for general SA using a generalized Moreau envelope as a smooth potential/ Lyapunov function.

Overparameterized neural networks have proved to be remarkably successful in many complex tasks such as image classification and deep reinforcement learning. In this talk, we will consider the role of explicit regularization in training overparameterized neural networks. Specifically, we consider ReLU networks and show that the landscape of commonly used regularized loss functions have the property that every local minimum has good memorization and regularization performance. Joint work with Shiyu Liang and Ruoyu Sun. Time: 11:00 AM – 12:00 PM Central (CDT; UTC -5)