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Tag: sampling

  • TFTF: Training-Free Targeted Flow for Conditional Sampling

    TFTF: Training-Free Targeted Flow for Conditional Sampling arXiv:2602.12932v1 Announce Type: new Abstract: We propose a training-free conditional sampling method for flow matching models based on importance sampling. Because a na”ive application of importance sampling suffers from weight degeneracy in high-dimensional settings, we modify and incorporate a resampling technique in sequential Monte Carlo (SMC) during intermediate…

  • Fast and Robust Likelihood-Guided Diffusion Posterior Sampling with Amortized Variational Inference

    Fast and Robust Likelihood-Guided Diffusion Posterior Sampling with Amortized Variational Inference arXiv:2602.07102v1 Announce Type: new Abstract: Zero-shot diffusion posterior sampling offers a flexible framework for inverse problems by accommodating arbitrary degradation operators at test time, but incurs high computational cost due to repeated likelihood-guided updates. In contrast, previous amortized diffusion approaches enable fast inference by…

  • Provable Diffusion Posterior Sampling for Bayesian Inversion

    Provable Diffusion Posterior Sampling for Bayesian Inversion arXiv:2512.08022v1 Announce Type: new Abstract: This paper proposes a novel diffusion-based posterior sampling method within a plug-and-play (PnP) framework. Our approach constructs a probability transport from an easy-to-sample terminal distribution to the target posterior, using a warm-start strategy to initialize the particles. To approximate the posterior score, we…

  • DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants

    DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants arXiv:2512.00252v1 Announce Type: new Abstract: Data assimilation (DA) is a cornerstone of scientific and engineering applications, combining model forecasts with sparse and noisy observations to estimate latent system states. Classical DA methods, such as the ensemble Kalman filter, rely on Gaussian approximations and heuristic tuning (e.g.,…

  • Robust Sampling for Active Statistical Inference

    Robust Sampling for Active Statistical Inference arXiv:2511.08991v1 Announce Type: new Abstract: Active statistical inference is a new method for inference with AI-assisted data collection. Given a budget on the number of labeled data points that can be collected and assuming access to an AI predictive model, the basic idea is to improve estimation accuracy by…

  • Functional Adjoint Sampler: Scalable Sampling on Infinite Dimensional Spaces

    Functional Adjoint Sampler: Scalable Sampling on Infinite Dimensional Spaces arXiv:2511.06239v1 Announce Type: new Abstract: Learning-based methods for sampling from the Gibbs distribution in finite-dimensional spaces have progressed quickly, yet theory and algorithmic design for infinite-dimensional function spaces remain limited. This gap persists despite their strong potential for sampling the paths of conditional diffusion processes, enabling…

  • Self-adaptive weighting and sampling for physics-informed neural networks

    Self-adaptive weighting and sampling for physics-informed neural networks arXiv:2511.05452v1 Announce Type: new Abstract: Physics-informed deep learning has emerged as a promising framework for solving partial differential equations (PDEs). Nevertheless, training these models on complex problems remains challenging, often leading to limited accuracy and efficiency. In this work, we introduce a hybrid adaptive sampling and weighting…

  • A new class of Markov random fields enabling lightweight sampling

    A new class of Markov random fields enabling lightweight sampling arXiv:2511.02373v1 Announce Type: new Abstract: This work addresses the problem of efficient sampling of Markov random fields (MRF). The sampling of Potts or Ising MRF is most often based on Gibbs sampling, and is thus computationally expensive. We consider in this work how to circumvent…

  • On Thompson Sampling and Bilateral Uncertainty in Additive Bayesian Optimization

    On Thompson Sampling and Bilateral Uncertainty in Additive Bayesian Optimization arXiv:2510.11792v1 Announce Type: new Abstract: In Bayesian Optimization (BO), additive assumptions can mitigate the twin difficulties of modeling and searching a complex function in high dimension. However, common acquisition functions, like the Additive Lower Confidence Bound, ignore pairwise covariances between dimensions, which we’ll call textit{bilateral…

  • Gradient-Guided Furthest Point Sampling for Robust Training Set Selection

    Gradient-Guided Furthest Point Sampling for Robust Training Set Selection arXiv:2510.08906v1 Announce Type: new Abstract: Smart training set selections procedures enable the reduction of data needs and improves predictive robustness in machine learning problems relevant to chemistry. We introduce Gradient Guided Furthest Point Sampling (GGFPS), a simple extension of Furthest Point Sampling (FPS) that leverages molecular…

  • Adaptive randomized pivoting and volume sampling

    Adaptive randomized pivoting and volume sampling arXiv:2510.02513v1 Announce Type: new Abstract: Adaptive randomized pivoting (ARP) is a recently proposed and highly effective algorithm for column subset selection. This paper reinterprets the ARP algorithm by drawing connections to the volume sampling distribution and active learning algorithms for linear regression. As consequences, this paper presents new analysis…

  • One-shot Conditional Sampling: MMD meets Nearest Neighbors

    One-shot Conditional Sampling: MMD meets Nearest Neighbors arXiv:2509.25507v1 Announce Type: new Abstract: How can we generate samples from a conditional distribution that we never fully observe? This question arises across a broad range of applications in both modern machine learning and classical statistics, including image post-processing in computer vision, approximate posterior sampling in simulation-based inference,…

  • Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling

    Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling arXiv:2509.03726v1 Announce Type: new Abstract: Sampling from unnormalized target distributions, e.g. Boltzmann distributions $mu_{text{target}}(x) propto exp(-E(x)/T)$, is fundamental to many scientific applications yet computationally challenging due to complex, high-dimensional energy landscapes. Existing approaches applying modern generative models to Boltzmann distributions either require…

  • TADA: Improved Diffusion Sampling with Training-free Augmented Dynamics

    TADA: Improved Diffusion Sampling with Training-free Augmented Dynamics arXiv:2506.21757v1 Announce Type: new Abstract: Diffusion models have demonstrated exceptional capabilities in generating high-fidelity images but typically suffer from inefficient sampling. Many solver designs and noise scheduling strategies have been proposed to dramatically improve sampling speeds. In this paper, we introduce a new sampling method that is…

  • Sampling conditioned diffusions via Pathspace Projected Monte Carlo

    Sampling conditioned diffusions via Pathspace Projected Monte Carlo arXiv:2506.15743v1 Announce Type: new Abstract: We present an algorithm to sample stochastic differential equations conditioned on rather general constraints, including integral constraints, endpoint constraints, and stochastic integral constraints. The algorithm is a pathspace Metropolis-adjusted manifold sampling scheme, which samples stochastic paths on the submanifold of realizations that…

  • Stable Thompson Sampling: Valid Inference via Variance Inflation

    Stable Thompson Sampling: Valid Inference via Variance Inflation arXiv:2505.23260v1 Announce Type: new Abstract: We consider the problem of statistical inference when the data is collected via a Thompson Sampling-type algorithm. While Thompson Sampling (TS) is known to be both asymptotically optimal and empirically effective, its adaptive sampling scheme poses challenges for constructing confidence intervals for…

  • Deconfounded Warm-Start Thompson Sampling with Applications to Precision Medicine

    Deconfounded Warm-Start Thompson Sampling with Applications to Precision Medicine arXiv:2505.17283v1 Announce Type: new Abstract: Randomized clinical trials often require large patient cohorts before drawing definitive conclusions, yet abundant observational data from parallel studies remains underutilized due to confounding and hidden biases. To bridge this gap, we propose Deconfounded Warm-Start Thompson Sampling (DWTS), a practical approach…

  • Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions

    Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions arXiv:2505.07825v1 Announce Type: new Abstract: We propose a hybrid generative model for efficient sampling of high-dimensional, multimodal probability distributions for Bayesian inference. Traditional Monte Carlo methods, such as the Metropolis-Hastings and Langevin Monte Carlo sampling methods, are effective for sampling from single-mode distributions…

  • On the Convergence of Irregular Sampling in Reproducing Kernel Hilbert Spaces

    On the Convergence of Irregular Sampling in Reproducing Kernel Hilbert Spaces arXiv:2504.13623v1 Announce Type: new Abstract: We analyse the convergence of sampling algorithms for functions in reproducing kernel Hilbert spaces (RKHS). To this end, we discuss approximation properties of kernel regression under minimalistic assumptions on both the kernel and the input data. We first prove…

  • A Metropolis-Adjusted Langevin Algorithm for Sampling Jeffreys Prior

    A Metropolis-Adjusted Langevin Algorithm for Sampling Jeffreys Prior arXiv:2504.06372v1 Announce Type: cross Abstract: Inference and estimation are fundamental aspects of statistics, system identification and machine learning. For most inference problems, prior knowledge is available on the system to be modeled, and Bayesian analysis is a natural framework to impose such prior information in the form…

  • Denoising guarantees for optimized sampling schemes in compressed sensing

    Denoising guarantees for optimized sampling schemes in compressed sensing arXiv:2504.01046v1 Announce Type: new Abstract: Compressed sensing with subsampled unitary matrices benefits from emph{optimized} sampling schemes, which feature improved theoretical guarantees and empirical performance relative to uniform subsampling. We provide, in a first of its kind in compressed sensing, theoretical guarantees showing that the error caused…

  • Accelerated Stein Variational Gradient Flow

    Accelerated Stein Variational Gradient Flow arXiv:2503.23462v1 Announce Type: new Abstract: Stein variational gradient descent (SVGD) is a kernel-based particle method for sampling from a target distribution, e.g., in generative modeling and Bayesian inference. SVGD does not require estimating the gradient of the log-density, which is called score estimation. In practice, SVGD can be slow compared…

  • Reheated Gradient-based Discrete Sampling for Combinatorial Optimization

    Reheated Gradient-based Discrete Sampling for Combinatorial Optimization arXiv:2503.04047v1 Announce Type: new Abstract: Recently, gradient-based discrete sampling has emerged as a highly efficient, general-purpose solver for various combinatorial optimization (CO) problems, achieving performance comparable to or surpassing the popular data-driven approaches. However, we identify a critical issue in these methods, which we term ”wandering in contours”.…

  • Enhancing Gradient-based Discrete Sampling via Parallel Tempering

    Enhancing Gradient-based Discrete Sampling via Parallel Tempering arXiv:2502.19240v1 Announce Type: new Abstract: While gradient-based discrete samplers are effective in sampling from complex distributions, they are susceptible to getting trapped in local minima, particularly in high-dimensional, multimodal discrete distributions, owing to the discontinuities inherent in these landscapes. To circumvent this issue, we combine parallel tempering, also…

  • Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond

    Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond arXiv:2502.04575v1 Announce Type: new Abstract: Given an unnormalized probability density $piproptomathrm{e}^{-V}$, estimating its normalizing constant $Z=int_{mathbb{R}^d}mathrm{e}^{-V(x)}mathrm{d}x$ or free energy $F=-log Z$ is a crucial problem in Bayesian statistics, statistical mechanics, and machine learning. It is challenging especially in high dimensions…

  • Five Reasons You Cannot Afford Not Knowing Probability Proportional to Size (PPS) Sampling

    Five Reasons You Cannot Afford Not Knowing Probability Proportional to Size (PPS) Sampling Data Science Simple Random Sampling (SRS) works, but if you do not know Probability Proportional to Size Sampling (PPS), you are risking yourself some critical statistical mistakes. Learn why, when, and how you can use PPS Sampling here! Photo by Justin Morgan on Unsplash…

  • Fast, Precise Thompson Sampling for Bayesian Optimization

    Fast, Precise Thompson Sampling for Bayesian Optimization arXiv:2411.17071v1 Announce Type: new Abstract: Thompson sampling (TS) has optimal regret and excellent empirical performance in multi-armed bandit problems. Yet, in Bayesian optimization, TS underperforms popular acquisition functions (e.g., EI, UCB). TS samples arms according to the probability that they are optimal. A recent algorithm, P-Star Sampler (PSS),…