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Found 8 papers, 0 papers with code
Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising Diffusion
It first describes a framework, Diffusion Monte Carlo (DMC), based on the simulation of a denoising diffusion process with its score function approximated by a generic Monte Carlo estimator.
High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance
We derive high-dimensional scaling limits and fluctuations for the online least-squares Stochastic Gradient Descent (SGD) algorithm by taking the properties of the data generating model explicitly into consideration.
Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality
We do so by establishing upper and lower bounds for Langevin diffusions and LMC under weak Poincar\'e inequalities that are satisfied by a large class of densities including polynomially-decaying heavy-tailed densities (i. e., Cauchy-type).
Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling
We analyze the complexity of sampling from a class of heavy-tailed distributions by discretizing a natural class of It\^o diffusions associated with weighted Poincar\'e inequalities.
Regularized Stein Variational Gradient Flow
In this work, we propose the Regularized Stein Variational Gradient Flow which interpolates between the Stein Variational Gradient Flow and the Wasserstein Gradient Flow.
Heavy-tailed Sampling via Transformed Unadjusted Langevin Algorithm
We analyze the oracle complexity of sampling from polynomially decaying heavy-tailed target densities based on running the Unadjusted Langevin Algorithm on certain transformed versions of the target density.
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method
The randomized midpoint method, proposed by [SL19], has emerged as an optimal discretization procedure for simulating the continuous time Langevin diffusions.
Mutual-Supervised Feature Modulation Network for Occluded Pedestrian Detection
These two branches are trained in a mutual-supervised way with full body annotations and visible body annotations, respectively.
