D-12: Machine Learning for Data Distributions (invited) |
| Session Type: Virtual |
| Time: Tuesday, November 02, 09:45 - 11:15 |
| Location: Room 3 |
| Virtual Session: Attend on Virtual Platform |
| Session Chair: Tom Goldstein, University of Maryland, USA |
| D-12.1: Generalizing from easy to hard data distributions with Deep Thinking Systems |
| Avi Schwarzschild; University of Maryland |
| Arjun Gupta; University of Maryland |
| Micah Goldblum; University of Maryland |
| Tom Goldstein; University of Maryland |
| D-12.2: Gaussian equivalence theorem for shallow neural networks with non-random weights |
| Galen Reeves; Duke University |
| D-12.3: Differential Private Data Summarization for Sampling and Estimation |
| Anshumali Shrivastava; Rice University |
| D-12.4: Generative Modeling by Estimating Gradients of the Data Distribution |
| Stefano Ermon; Stanford University |