arXiv Computer Vision

Classification-oriented adaptive sensing via posterior sampling

The paper proposes a classification-oriented adaptive sensing method that uses posterior sampling from diffusion models. It leverages the closed-form posterior covariance of a class-conditional Gaussian mixture model to separate within-class and between-class uncertainty, estimating these terms from diffusion posterior samples via calibrated soft classifier outputs. Experiments on MNIST and CIFAR-10 demonstrate that this approach can achieve better classification accuracy for a given measurement cost compared to reconstruction-oriented methods, while also quantifying the associated reconstruction quality.

arXiv Computer Vision

Sep 21

How Many Posterior Samples? Calibrated Stopping for Adaptive Sensing

The paper investigates how to decide when to stop collecting posterior samples in classification‑oriented adaptive sensing. It shows that a simple threshold‑based plug‑in rule does not guarantee the desired confidence level, and proposes calibrated fixed‑sample and finite‑horizon sequential stopping rules that control the false‑declaration probability. Experiments on MNIST demonstrate that the sequential rule can reduce sensing cost the most, and that a curtailment strategy can save up to 62% of posterior samples while maintaining accuracy.

By Vincent Corlay, Andriy Enttsel

arXiv AI

Jul 22

Provable diffusion-based posterior sampling for linear inverse problems via DDIM

arXiv:2607. 19333v1 Announce Type: cross Abstract: Diffusion-based methods have achieved remarkable empirical success in solving inverse problems.

By Yuchen Jiao, Na Li, Changxiao Cai, Yuxin Chen, Gen Li

diffusion

arXiv Statistics ML

Aug 26

Learning from Uncertainty-dependent Missing Labels for Semi-supervised Classification

arXiv:2608.23960v1 Announce Type: cross Abstract: Missing labels are usually regarded as a source of information loss in classification. We study a semi-supervised setting in which the probability of...

By You-Gan Wang, Jinran Wu, Geoffrey J. McLachlan

benchmarks

arXiv Machine Learning

Jun 24

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning

arXiv:2602. 01477v2 Announce Type: replace-cross Abstract: Evidential Deep Learning (EDL) is a popular framework for uncertainty-aware classification that models predictive uncertainty via Dirichlet distributions parameterized by neural networks.

By Pietro Carlotti, Nevena Gligi\'c, Arya Farahi

safety

arXiv Machine Learning

Jul 21

Bayesian Signal Component Decomposition via Diffusion-within-Gibbs Sampling

arXiv:2602. 10792v2 Announce Type: replace-cross Abstract: In signal processing, the data collected from sensing devices is often a noisy linear superposition of multiple components, and the estimation of components of interest constitutes a crucial pre-processing step.

By Yi Zhang, Rui Guo, Yonina C. Eldar

diffusion

arXiv AI

Jun 30

DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing

arXiv:2511. 17038v4 Announce Type: replace Abstract: From a Bayesian perspective, score-based diffusion solves inverse problems through joint inference, embedding the likelihood with the prior to guide the sampling process.

By Hao Chen, Renzheng Zhang, Scott S. Howard

rag diffusion

arXiv AI

Jun 2

Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications

arXiv:2606. 00078v1 Announce Type: cross Abstract: Numerous modern applications in signal processing and medical imaging necessitate acquiring high-dimensional signals under tight resource constraints.

By Roman Pavelkin, Luis A. Zavala-Mondragon, Christiaan G. A. Viviers, Fons van der Sommen

diffusion computer-vision benchmarks

arXiv Computer Vision

Sep 15

Direct Conditional Transition Sampling for Diffusion Inverse Problems

arXiv:2609.14596v1 Announce Type: new Abstract: Training-free diffusion inverse solvers typically choose between local measurement guidance and costly clean-space posterior updates. Independent poste...

By Qi Yu, Hanlin Wu, Xiaohui Sun

diffusion

arXiv Statistics ML

6d ago

Learning to Replace MCMC in Split-Gibbs Diffusion Posterior Sampling via Deep Unfolding

The paper introduces a learning-based approach to replace the MCMC step in split-Gibbs diffusion posterior sampling. By reformulating both Gibbs updates as Gaussian denoising problems, the method uses ODE diffusion for the prior step with a pretrained denoiser and a lightweight deep-unfolded network for the likelihood step. Experiments on nonlinear phase retrieval show that this alternative reduces likelihood-update cost while maintaining effectiveness compared to MCMC-based split Gibbs.

By Yi Zhang, Rui Guo, Mengchu Xu, Zhaofeng Liu, Yonina C. Eldar

diffusion

arXiv Machine Learning

Sep 23

FREESIA: Covariance-Aware Posterior Transport for Expressive and Scalable Data Assimilation

The paper introduces FREESIA, a training‑free, covariance‑aware posterior transport method for data assimilation that embeds forecast cross‑covariance into a flow‑based transport to recover unobserved states while preserving non‑Gaussian posterior structure. It combines an observation‑adaptive proposal with posterior correction, providing an asymptotically exact approximation of nonlinear posteriors and a Wasserstein error bound. Experiments on Double‑Well, Lorenz‑96, and Kolmogorov flow demonstrate that FREESIA captures complex posterior structures and achieves up to a 56% reduction in RMSE compared to the best baseline in sparse, nonlinear, non‑injective observation scenarios.

By Shiwei Ni, Yangwen Zhang, Hang Qi, Xiaofei Guan, Lili Ju

multimodal

arXiv Machine Learning

Jun 10

Blind denoising diffusion models and the blessings of dimensionality

arXiv:2602. 09639v2 Announce Type: replace Abstract: Denoising diffusion models (DDMs) are state-of-the-art methods for learning densities from data across numerous domains, yet many aspects of the training and sampling pipeline remain poorly understood.

By Zahra Kadkhodaie, Aram-Alexandre Pooladian, Sinho Chewi, Eero Simoncelli

rag diffusion benchmarks

arXiv Machine Learning

Sep 10

Understanding Uncertainty Sampling via Equivalent Loss

arXiv:2307.02719v5 Announce Type: replace Abstract: Uncertainty sampling is a classical active-learning strategy, yet the statistical objective induced by its query rule is often implicit. We introdu...

By Shang Liu, Xiaocheng Li

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