Schedule
All times are in EST and pertain to April 29. To get links to all presentations, please login to the ICLR virtual website for this workshop.
| Time | Type | Title |
|---|---|---|
| 08:00 AM - 08:10 AM | Live | Opening Remarks |
| 08:10 AM - 08:30 AM | Live | Summary of Previous Workshops |
| 08:30 AM - 09:00 AM | Foundation Talk | Smita Krishnaswamy |
| 09:00 AM - 09:30 AM | Foundation Talk | Bernadette Stolz |
| 09:30 AM - 10:10 AM | Panel Discussion (live) | Panel D: Bridging Theory and Practice |
| 10:10 AM - 10:30 AM | Invited Talk | Roland Kwitt |
| 10:30 AM - 10:50 AM | Invited Talk | Stefanie Jegelka |
| 10:50 AM - 11:00 AM | Case Study | Stefan Horoi |
| 11:00 AM - 11:40 AM | Panel Discussion (live) | Panel C: Topology-Driven Machine Learning |
| 11:40 AM - 11:45 AM | Spotlight | Neural Approximation of Extended Persistent Homology on Graphs |
| 11:45 AM - 11:50 AM | Spotlight | RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds |
| 11:50 AM - 11:55 AM | Spotlight | Pre-training Molecular Graph Representation with 3D Geometry |
| 11:55 AM - 12:00 PM | Spotlight | Group Symmetry in PAC Learning |
| 12:00 PM - 01:00 PM | Poster session on Gather.Town | Poster Session I |
| 01:40 PM - 01:50 PM | Case Study | Tara Chari |
| 01:50 PM - 02:30 PM | Panel Discussion (live) | Panel A: Data-Driven Manifold Learning |
| 02:30 PM - 02:35 PM | Spotlight | TopTemp: Parsing Precipitate Structure from Temper Topology |
| 02:35 PM - 02:40 PM | Spotlight | A Piece-wise Polynomial Filtering Approach for Graph Neural Networks |
| 02:40 PM - 02:45 PM | Spotlight | Message passing all the way up |
| 02:45 PM - 02:50 PM | Spotlight | Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs |
| 02:50 PM - 03:50 PM | Poster session on Gather.Town | Poster Session II |
| 03:50 PM - 04:00 PM | Case Study | Dmitry Kobak |
| 04:00 PM - 04:50 PM | Panel Discussion (live) | Panel B: Long-Range Graph Representation Learning |
| 04:40 PM - 04:50 PM | Case Study | Jessica Moore |
| 04:50 PM - 05:00 PM | Live | Closing Remarks |
Panel A: Data-Driven Manifold Learning
- Tara Chari, California Institute of Technology
- Claire Donnat, University of Chicago
- Dmitry Kobak, Tübingen University
- Leland McInnes, Tutte Institute for Mathematics and Computing,
Panel B: Long-Range Graph Representation Learning
- Corinna Coupette, Max Planck Institute for Informatics
- Stefanie Jegelka, MIT
- Christopher Morris, Mila – Quebec AI Institute \& McGill University