9 citations · 27 across the 19 of their papers we have counts for
4 papers · 1 filter
Concentration of the Langevin Algorithm's Stationary Distribution
Jason M. Altschuler, Kunal Talwar
A canonical algorithm for log-concave sampling is the Langevin Algorithm, aka the Langevin Diffusion run with some discretization stepsize . This discretization leads the Lan…
Resolving the Mixing Time of the Langevin Algorithm to its Stationary Distribution for Log-Concave Sampling
Jason M. Altschuler, Kunal Talwar
Sampling from a high-dimensional distribution is a fundamental task in statistics, engineering, and the sciences. A canonical approach is the Langevin Algorithm, i.e., the Markov c…
Flows, Scaling, and Entropy Revisited: a Unified Perspective via Optimizing Joint Distributions
Jason M. Altschuler
In this short expository note, we describe a unified algorithmic perspective on several classical problems which have traditionally been studied in different communities. This pers…
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss
Jason M. Altschuler, Kunal Talwar
A central issue in machine learning is how to train models on sensitive user data. Industry has widely adopted a simple algorithm: Stochastic Gradient Descent with noise (a.k.a. St…