no code implementations • 5 Oct 2022 • Sinho Chewi, Patrik Gerber, Holden Lee, Chen Lu
We prove two lower bounds for the complexity of non-log-concave sampling within the framework of Balasubramanian et al. (2022), who introduced the use of Fisher information (FI) bounds as a notion of approximate first-order stationarity in sampling.
no code implementations • NeurIPS 2021 • Jason M. Altschuler, Sinho Chewi, Patrik Gerber, Austin J. Stromme
We study first-order optimization algorithms for computing the barycenter of Gaussian distributions with respect to the optimal transport metric.
no code implementations • 29 May 2021 • Sinho Chewi, Patrik Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet
We consider the task of generating exact samples from a target distribution, known up to normalization, over a finite alphabet.
no code implementations • 29 May 2021 • Sinho Chewi, Patrik Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet
We establish the first tight lower bound of $\Omega(\log\log\kappa)$ on the query complexity of sampling from the class of strongly log-concave and log-smooth distributions with condition number $\kappa$ in one dimension.
