Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space
Event details
| Date | 06.03.2024 |
| Hour | 13:15 › 14:15 |
| Speaker | Aram-Alexandre Pooladian (NYU) |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
We develop a theory of finite-dimensional polyhedral subsets over the Wasserstein space and optimization of functionals over them via first-order methods. Our main application is to the problem of mean-field variational inference, which seeks to approximate a distribution, called the posterior, by the closest product measure in the sense of Kullback--Leibler divergence, called the mean-field approximation. We propose a novel optimization procedure for computing the mean-field approximation, where we are able to provide concrete algorithmic guarantees under the standard assumption that the posterior is strongly log-concave and log-smooth. Joint work with Yiheng Jiang and Sinho Chewi.
Practical information
- Informed public
- Free
Organizer
- Lénaïc Chizat