research
∙
06/28/2023
Gaussian random field approximation via Stein's method with applications to wide random neural networks
We derive upper bounds on the Wasserstein distance (W_1), with respect t...
share
research
∙
05/19/2023
The probability flow ODE is provably fast
We provide the first polynomial-time convergence guarantees for the prob...
research
∙
04/10/2023
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space
Variational inference (VI) seeks to approximate a target distribution π ...
research
∙
09/22/2022
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
We provide theoretical convergence guarantees for score-based generative...
research
∙
06/02/2022
Federated Learning with a Sampling Algorithm under Isoperimetry
Federated learning uses a set of techniques to efficiently distribute th...
research
∙
02/13/2022
Improved analysis for a proximal algorithm for sampling
We study the proximal sampler of Lee, Shen, and Tian (2021) and obtain n...
research
∙
02/10/2022
Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo
For the task of sampling from a density π∝exp(-V) on ℝ^d, where V is pos...
research
∙
06/06/2021
Complexity Analysis of Stein Variational Gradient Descent Under Talagrand's Inequality T1
We study the complexity of Stein Variational Gradient Descent (SVGD), wh...
research
∙
06/21/2020
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization
We consider the task of decentralized minimization of the sum of smooth ...
research
∙
06/17/2020
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
We study the Stein Variational Gradient Descent (SVGD) algorithm, which ...
research
∙
06/16/2020
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm
We consider the task of sampling with respect to a log concave probabili...
research
∙
04/03/2020
Dualize, Split, Randomize: Fast Nonsmooth Optimization Algorithms
We introduce a new primal-dual algorithm for minimizing the sum of three...
research
∙
02/07/2020
Wasserstein Proximal Gradient
We consider the task of sampling from a log-concave probability distribu...
research
∙
12/20/2019
Distributed Fixed Point Methods with Compressed Iterates
We propose basic and natural assumptions under which iterative optimizat...
research
∙
06/11/2019
Maximum Mean Discrepancy Gradient Flow
We construct a Wasserstein gradient flow of the maximum mean discrepancy...
research
∙
05/28/2019
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates
We propose a new algorithm---Stochastic Proximal Langevin Algorithm (SPL...
research
∙
01/23/2019
A Fully Stochastic Primal-Dual Algorithm
A new stochastic primal-dual algorithm for solving a composite optimizat...
research
∙
04/03/2018
A Constant Step Stochastic Douglas-Rachford Algorithm with Application to Non Separable Regularizations
The Douglas Rachford algorithm is an algorithm that converges to a minim...
research
∙
12/19/2017
