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05/19/2023
The probability flow ODE is provably fast
We provide the first polynomial-time convergence guarantees for the prob...
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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 ...
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04/05/2023
Query lower bounds for log-concave sampling
Log-concave sampling has witnessed remarkable algorithmic advances in re...
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02/20/2023
Faster high-accuracy log-concave sampling via algorithmic warm starts
Understanding the complexity of sampling from a strongly log-concave and...
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02/16/2023
Improved Discretization Analysis for Underdamped Langevin Monte Carlo
Underdamped Langevin Monte Carlo (ULMC) is an algorithm used to sample f...
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12/14/2022
Learning threshold neurons via the "edge of stability"
Existing analyses of neural network training often operate under the unr...
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10/05/2022
Fisher information lower bounds for sampling
We prove two lower bounds for the complexity of non-log-concave sampling...
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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...
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05/31/2022
Variational inference via Wasserstein gradient flows
Along with Markov chain Monte Carlo (MCMC) methods, variational inferenc...
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03/09/2022
An entropic generalization of Caffarelli's contraction theorem via covariance inequalities
The optimal transport map between the standard Gaussian measure and an α...
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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...
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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...
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12/23/2021
Analysis of Langevin Monte Carlo from Poincaré to Log-Sobolev
Classically, the continuous-time Langevin diffusion converges exponentia...
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12/21/2021
The entropic barrier is n-self-concordant
For any convex body K ⊆ℝ^n, S. Bubeck and R. Eldan introduced the entrop...
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09/17/2021
Gaussian discrepancy: a probabilistic relaxation of vector balancing
We introduce a novel relaxation of combinatorial discrepancy called Gaus...
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06/16/2021
Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent
We study first-order optimization algorithms for computing the barycente...
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05/29/2021
Rejection sampling from shape-constrained distributions in sublinear time
We consider the task of generating exact samples from a target distribut...
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05/29/2021
The query complexity of sampling from strongly log-concave distributions in one dimension
We establish the first tight lower bound of Ω(loglogκ) on the query comp...
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12/23/2020
Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm
Conventional wisdom in the sampling literature, backed by a popular diff...
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10/30/2020
Efficient constrained sampling via the mirror-Langevin algorithm
We propose a new discretization of the mirror-Langevin diffusion and giv...
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10/22/2020
Fast and Smooth Interpolation on Wasserstein Space
We propose a new method for smoothly interpolating probability measures ...
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06/03/2020
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence
Stein Variational Gradient Descent (SVGD), a popular sampling algorithm,...
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05/19/2020
Exponential ergodicity of mirror-Langevin diffusions
Motivated by the problem of sampling from ill-conditioned log-concave di...
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01/06/2020
Gradient descent algorithms for Bures-Wasserstein barycenters
We study first order methods to compute the barycenter of a probability ...
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06/18/2018
