arXiv research

Accelerates sampling from Gibbs distributions using ARWP method.

This research accelerates sampling methods using Nesterov's Acceleration.

Accelerates TPP sampling with speculative decoding for faster sequence generation.

Nested Slice Sampling accelerates Nested Sampling for GPU acceleration.

ASVGD accelerates SVGD for efficient sampling.

Variance reduction is a simple and effective technique that accelerates convex (or non-convex) stochastic optimization. Among existing variance reduction methods, SVRG and SAGA adopt unbiased gradient estimators and are the most popular variance reduction methods in recent years. Although various accelerated variants o…

Paper accelerates diffusion models, improving sampling speed.

Stochastic gradient decent~(SGD) and its variants, including some accelerated variants, have become popular for training in machine learning. However, in all existing SGD and its variants, the sample size in each iteration~(epoch) of training is the same as the size of the full training set. In this paper, we propose a…

Accelerated RPCholesky speeds up kernel matrix approximations.

ES improves training efficiency by dynamically selecting data samples.

LA-VDM accelerates VDM using landmarks to improve data analysis.

GPU-accelerated particle methods outperform neural samplers in LFT benchmarks.

New method accelerates diffusion models for broader target distributions.

Accelerates MCMC sampling for large-scale problems using machine learning.

New method uses birth-death process and exploration component to accelerate sampling from multimodal distributions.

ASVGD accelerates SVGD for efficient sampling from Gaussian targets.

Unified method for MMD variance estimation improves accuracy and computational efficiency.

A new method for generating synthetic data using posterior distribution learning accelerates inference.

Paper analyzes and accelerates Langevin Monte Carlo methods using large deviations theory.

We present a framework for Nesterov's accelerated gradient flows in probability space to design efficient mean-field Markov chain Monte Carlo (MCMC) algorithms for Bayesian inverse problems. Here four examples of information metrics are considered, including Fisher-Rao metric, Wasserstein-2 metric, Kalman-Wasserstein m…

Accelerated coordinate descent is widely used in optimization due to its cheap per-iteration cost and scalability to large-scale problems. Up to a primal-dual transformation, it is also the same as accelerated stochastic gradient descent that is one of the central methods used in machine learning. In this paper, we imp…

New method accelerates Bayesian imaging using Langevin sampling.

PNDMs accelerate DDPMs by treating them as differential equations on manifolds.

Timewarp accelerates molecular dynamics by learning to simulate long timescales.

New method speeds up diffusion models without requiring complex assumptions.

Unpaired deep learning reconstructs MRI images from accelerated data.

New algorithms accelerate SVGD convergence using deep unfolding.

GADD accelerates uniform-rate discrete diffusion models by 2 orders of magnitude.

A fundamental problem in Bayesian inference and statistical machine learning is to efficiently sample from multimodal distributions. Due to metastability, multimodal distributions are difficult to sample using standard Markov chain Monte Carlo methods. We propose a new sampling algorithm based on a birth-death mechanis…

Inference of latent feature models in the Bayesian nonparametric setting is generally difficult, especially in high dimensional settings, because it usually requires proposing features from some prior distribution. In special cases, where the integration is tractable, we can sample new feature assignments according to …

New algorithm speeds up diffusion model sampling 4-14 times.

New method accelerates Parallel Tempering using neural samplers.

Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection. Due to the lack of ground truth labels, practitioners often have to build a large number of unsupervised, heterogeneou…

This work accelerates constrained sampling using large deviation principles.

Faster WIND accelerates iterative BOND for LLM alignment.

This paper analyzes speculative decoding, a method to speed up large language model inferences.

Dropout is a simple but efficient regularization technique for achieving better generalization of deep neural networks (DNNs); hence it is widely used in tasks based on DNNs. During training, dropout randomly discards a portion of the neurons to avoid overfitting. This paper presents an enhanced dropout technique, whic…

DreamPropeller accelerates text-to-3D generation by 4.7x with minimal loss in quality.

We propose a novel method to accelerate Lloyd's algorithm for K-Means clustering. Unlike previous acceleration approaches that reduce computational cost per iterations or improve initialization, our approach is focused on reducing the number of iterations required for convergence. This is achieved by treating the assig…

We present a selective sampling method designed to accelerate the training of deep neural networks. To this end, we introduce a novel measurement, the minimal margin score (MMS), which measures the minimal amount of displacement an input should take until its predicted classification is switched. For multi-class linear…

Principal component analysis (PCA) is one of the most powerful tools in machine learning. The simplest method for PCA, the power iteration, requires $\mathcal O(1/Δ)$ full-data passes to recover the principal component of a matrix with eigen-gap $Δ$. Lanczos, a significantly more complex method, achieves an accelerated…

Efficient methods accelerate diffusion model sampling.

HF-opt uses Hamiltonian dynamics to optimize functions, achieving accelerated rates with randomized integration time.

WSD uses a deterministic model to accelerate diffusion-based sampling.

This work sets lower bounds on the number of score queries needed for diffusion sampling.

New study on No-U-Turn Sampler for accelerated mixing in Hamiltonian Monte Carlo.

We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as optimization on the space of probability measures, with Kullback-Leibler (KL) divergence as the objective functional. We show that an underdamped form of the Langevin algorithm performs accelerated gradient descent in this metric. To characterize t…

Improved diffusion model generation speed with speculative sampling.