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Special Colloquium (Data Science): Towards a theory of complexity of sampling, inspired by optimization
Special Events| Speaker: | Sinho Chewi, MIT |
| Related Webpage: | https://chewisinho.github.io/ |
| Location: | 1147 MSB |
| Start time: | Fri, Jan 13 2023, 3:10PM |
Sampling is a fundamental and widespread algorithmic primitive that lies at the heart of Bayesian inference and scientific computing, among other disciplines. Recent years have seen a flood of works aimed at laying down the theoretical underpinnings of sampling, in analogy to the fruitful and widely used theory of convex optimization. In this talk, I will discuss some of my work in this area, focusing on new convergence guarantees obtained via a proximal algorithm for sampling, as well as a new framework for studying the complexity of non-log-concave sampling.