A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
We introduce a new sampling method for large language models that balances diversity and parallelism.
New method uses higher-order Langevin dynamics for efficient parallel sampling.
Parallel sampling for smooth distributions with fast convergence.
Communication costs, resulting from synchronization requirements during learning, can greatly slow down many parallel machine learning algorithms. In this paper, we present a parallel Markov chain Monte Carlo (MCMC) algorithm in which subsets of data are processed independently, with very little communication. First, w…
New method accelerates Parallel Tempering using neural samplers.
Clapping reduces memory usage in distributed optimization by reusing data samples.
We design and analyse variations of the classical Thompson sampling (TS) procedure for Bayesian optimisation (BO) in settings where function evaluations are expensive, but can be performed in parallel. Our theoretical analysis shows that a direct application of the sequential Thompson sampling algorithm in either synch…
This study compares parallel SMC and MCMC for Bayesian deep learning, showing SMC parallel is faster.
Parallelizes active learning for Bayesian inference using Nested Sampler.
Enhances gradient-based discrete samplers with parallel tempering for multimodal distributions.
We present Acquisition Thompson Sampling (ATS), a novel technique for batch Bayesian Optimization (BO) based on the idea of sampling multiple acquisition functions from a stochastic process. We define this process through the dependency of the acquisition functions on a set of model hyper-parameters. ATS is conceptuall…
ESS improves MCMC efficiency for correlated & multimodal distributions.
New adaptive temperature selection improves parallel tempering efficiency.
A new method combines MCMC results to avoid failures in parallel computing.
An infinite parallel tempering bouncy particle sampler improves sampling efficiency for multimodal distributions.
New algorithm speeds up diffusion model sampling 4-14 times.
Massively parallel RWS improves inference in complex models.
Embarrassingly (communication-free) parallel Markov chain Monte Carlo (MCMC) methods are commonly used in learning graphical models. However, MCMC cannot be directly applied in learning topic models because of the quasi-ergodicity problem caused by multimodal distribution of topics. In this paper, we develop an embarra…
Two parallel samplers enhance image quality in limited denoising steps.
New sampling method improves efficiency for diffusion models.
Chemical space is so large that brute force searches for new interesting molecules are infeasible. High-throughput virtual screening via computer cluster simulations can speed up the discovery process by collecting very large amounts of data in parallel, e.g., up to hundreds or thousands of parallel measurements. Bayes…
We consider parallel asynchronous Markov Chain Monte Carlo (MCMC) sampling for problems where we can leverage (stochastic) gradients to define continuous dynamics which explore the target distribution. We outline a solution strategy for this setting based on stochastic gradient Hamiltonian Monte Carlo sampling (SGHMC) …
EP-GFlowNets parallelize GFlowNet training for large-scale Bayesian inference.
In this paper we consider planar polygons with parallel opposite sides. This type of polygons can be regarded as discretizations of closed convex planar curves by taking tangent lines at samples with pairwise parallel tangents. For this class of polygons, we define discrete versions of the area evolute, central symmetr…
DreamPropeller accelerates text-to-3D generation by 4.7x with minimal loss in quality.
Paper improves financial trading models using GPU parallelism.
Parallelizes feedforward computation using nonlinear equation solving.
Improved lower bound for parallel tempering's mixing time.
In AI research and industry, machine learning is the most widely used tool. One of the most important machine learning algorithms is Gradient Boosting Decision Tree, i.e. GBDT whose training process needs considerable computational resources and time. To shorten GBDT training time, many works tried to apply GBDT on Par…
We propose a new sampler that integrates the protocol of parallel tempering with the Nosé-Hoover (NH) dynamics. The proposed method can efficiently draw representative samples from complex posterior distributions with multiple isolated modes in the presence of noise arising from stochastic gradient. It potentially faci…
We describe an embarrassingly parallel, anytime Monte Carlo method for likelihood-free models. The algorithm starts with the view that the stochasticity of the pseudo-samples generated by the simulator can be controlled externally by a vector of random numbers u, in such a way that the outcome, knowing u, is determinis…
PHS optimizes hyperparameters in parallel for expensive computations.
Jeffreys Flow improves robustness of Boltzmann generators for rare event sampling.
Parallel Bayesian optimization tackles noisy multi-objective problems.
The Graph Convolutional Network (GCN) model and its variants are powerful graph embedding tools for facilitating classification and clustering on graphs. However, a major challenge is to reduce the complexity of layered GCNs and make them parallelizable and scalable on very large graphs -- state-of the art techniques a…
Alternative sampling method for autoregressive models using Langevin dynamics.
In this paper, we propose a new framework for designing fast parallel algorithms for fundamental statistical subset selection tasks that include feature selection and experimental design. Such tasks are known to be weakly submodular and are amenable to optimization via the standard greedy algorithm. Despite its desirab…
Efficient event generation for collider phenomenology using parallel Langevin sampling and learned Stein diagnostics.
The complex and computationally expensive nature of landscape evolution models pose significant challenges in the inference and optimisation of unknown parameters. Bayesian inference provides a methodology for estimation and uncertainty quantification of unknown model parameters. In our previous work, we developed para…
Motivated by a sampling problem basic to computational statistical inference, we develop a nearly optimal algorithm for a fundamental problem in spectral graph theory and numerical analysis. Given an SDDM matrix , and a constant , our algorithm gives efficient access to a…
Parallelizes autoregressive generation using VSSM.
A new sampler for complex discrete distributions efficiently updates all variables in parallel.
New method speeds up diffusion models inference to sub-linear time.
Bayesian optimization has become a popular method for high-throughput computing, like the design of computer experiments or hyperparameter tuning of expensive models, where sample efficiency is mandatory. In these applications, distributed and scalable architectures are a necessity. However, Bayesian optimization is mo…
Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling are finding widespread use in applied statistics and machine learning. These often lead to difficult computational problems, which are increasingly being solved on parallel and distributed systems such as compute clusters. Recent work has proposed running i…
Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In order to foster better exploration of the state space, specially in high-dimensional applications, s…
A new algorithm speeds up elliptical slice sampling for truncated multivariate normals.
ULA estimates covariance of log-concave distributions efficiently.