Bayesian Methods and Mixture Models

Neueste Paper

• Scalable estimation of VARMA models

  Daniel Paulin, Victor Elvira · 7. August 2026

  Vector autoregressive moving-average (VARMA) models have long been considered impractical beyond moderate dimensions: the likelihood is non-convex, the parametrization is identified only up to equivalence, and every evaluation costs a pass over the entire series. Yet their moving-average term captur…

• Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data

  Nina van Gerwen, Dimitris Rizopoulos, Manon Hillegers, Loes Keijsers, Sten Willemsen · 7. August 2026

  The experience sampling method (ESM) is a longitudinal research design where participants report their thoughts, emotional states and behaviours multiple times a day. Our work is motivated by such data collected by the GrowIt! app, which was released to investigate daily emotions among adolescents d…

• Differentially Private Nonparametric Modal Learning with Applications to Regression and Clustering

  Arkajyoti Bhattacharjee, Arnab Auddy · 3. August 2026

  Density modes provide a localized and interpretable summary of multimodal distributions, but their estimation under rigorous differential privacy constraints remains largely unexplored. We study differentially private recovery of density modes for multivariate distributions under local smoothness, c…

• Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance

  Pei-Hsuan Hsia, Lars H. Heyen, Arvid Weyrauch, Markus Goetz, Achim Streit, Sebastian Krumscheid, Charlotte Debus · 29. Juli 2026

  In Bayesian neural networks (BNNs), variational inference is a widely adopted framework for modeling uncertainty in a distributional way, with the evidence lower bound (ELBO) serving as the standard objective function. Several distributions contribute to the ELBO loss, such as the prior, approximate…

• New methods to compute the generalized chi-square distribution

  Abhranil Das · 30. Juni 2026

  We present four new mathematical methods, two exact and two approximate, along with open-source software, to compute the cdf, pdf and inverse cdf of the generalized chi-square distribution. Some methods are geared for speed, while others are designed to be accurate far into the tails, using which we…

• Detection and Evaluation of Clusters within Sequential Data

  Alexander Van Werde, Albert Senen-Cerda, Gianluca Kosmella, Jaron Sanders · 23. Juni 2026

  Sequential data is ubiquitous -- it is routinely gathered to gain insights into complex processes such as behavioral, biological, or physical processes. Challengingly, such data not only has dependencies within the observed sequences, but the observations are also often high-dimensional, sparse, and…

• Variance-Tilted Diffusion Models for Diverse Sampling

  Iskander Azangulov, Leo Zhang, Kianoosh Ashouritaklimi · 23. Juni 2026

  Diffusion models are typically sampled independently, even when the downstream objective is to obtain a diverse set of candidates. We introduce a variance-weighted batch distribution that favours collections of samples with large empirical spread after a prescribed linear feature map. The target is …

• Bayesian Model Averaging under Predictor Redundancy via Density-Ratio Posterior Compression

  Hanqing Li, Xuewen Lu, Yuting Chen · 23. Juni 2026

  Bayesian model averaging in support-indexed regression induces a posterior distribution over active predictor supports. Under predictor redundancy, posterior mass can spread across many nearly interchangeable supports, making exact-support summaries unstable or hard to interpret even when prediction…

• Variational Consensus Monte Carlo for Bayesian Mixture

  Julie Fendler, Francesca L. Crowe, Tom Marshall, Sylvia Richardson, Paul D. W. Kirk · 19. Juni 2026

  Motivated by the privacy, sensitivity and sharing limitations of health data, we present a comprehensive pipeline for inference of Bayesian mixture models within a federated learning setting, i.e. when data cannot be fully shared or pooled across compute nodes. We adopt a Consensus Monte Carlo (CMC)…

• Information Gap and Feasibility-Aware Inference in Binomial Logistic Mixtures

  Yuta Hayashida, Shonosuke Sugasawa · 16. Juni 2026

  This paper studies the information gap between mixture detection and label recovery in binomial logistic mixtures. Standard likelihood-based criteria such as the Bayesian information criterion (BIC) can detect the presence of two components, but this does not guarantee that the corresponding labels …

• A nonparametric two-sample test using a parametric integral probability metric

  Yuha Park, Yongdai Kim · 16. Juni 2026

  Detecting distributional differences between two independent samples is a fundamental problem in statistics and machine learning. Nonparametric two-sample testing provides a principled framework for determining whether two samples are drawn from the same underlying distribution, without assuming any…

• Projected random forests and conformal prediction of circular data

  Paulo C. Marques F., Rinaldo Artes, Helton Graziadei · 11. Juni 2026

  We apply conformal prediction techniques to regression problems with circular responses, producing prediction sets with adaptive arc length and finite-sample coverage guarantees for any circular predictive model under the assumption of data exchangeability. Leveraging the high performance of existin…

• Flash-GMM: A Memory-Efficient Kernel for Scalable Soft Clustering

  Gal Bloch, Ariel Gera, Matan Orbach, Ohad Eytan, Assaf Toledo · 10. Juni 2026

  We present \textbf{Flash-GMM}, a fused Triton kernel for efficient computation of Gaussian Mixture Models (GMMs) over large-scale data in a single GPU pass. By eliminating the need to materialize the full responsibility matrix in GPU memory, Flash-GMM achieves a \textbf{20$\times$} speedup over exis…

• ClusBench: The Clustering Benchmark Data Resource You've All Been Waiting For (?)

  David P. Hofmeyr · 10. Juni 2026

  Although some very common test beds exist for assessing the performance of clustering methods, large scale benchmarking is typically limited to relatively simplistic simulation set-ups. Here we describe the production and curation of close to 3000 synthetic data sets, derived from more than 200 publ…

• Latent Guided Sampling for Combinatorial Optimization

  Sobihan Surendran (LPSM), Adeline Fermanian (LPSM), Sylvain Le Corff (LPSM) · 10. Juni 2026

  Combinatorial Optimization problems are widespread in domains such as logistics, manufacturing, and drug discovery, yet their NP-hard nature makes them computationally challenging. Recent Neural Combinatorial Optimization (NCO) methods leverage deep learning to learn policies for constructing soluti…

• Identifiability and Estimation for Unlabeled Finite Mixtures under Marginal Independence

  Takafumi Kanamori, Yushi Hirose, Shohei Yamamoto · 9. Juni 2026

  We study component recovery and mixing-matrix estimation from unlabeled finite mixtures whose observable distributions share the same latent components but have unknown mixing weights. The main identifying signal is marginal independence: each component is assumed to be independent on at least one c…

• A Broader View of Thompson Sampling

  Yanlin Qu, Hongseok Namkoong, Assaf Zeevi · 28. Mai 2026

  Thompson Sampling is one of the most widely used and studied bandit algorithms, known for its simple structure, low regret performance, and solid theoretical guarantees. Yet, in stark contrast to most other families of bandit algorithms, the exact mechanism through which posterior sampling (as intro…

• Missing Pattern Recognized Diffusion Imputation Model for Missing Not At Random

  Gyuwon Sim, Sumin Lee, Heesun Bae, Byeonghu Na, Doyun Kwon, Ju-Hee Hwang, Jae-Young Lim, Il-Chul Moon · 26. Mai 2026

  Missing data frequently arises across diverse domains, including time-series and image domains. In the real world, missing occurrences often depend on the unobservable values themselves, which are referred to as Missing Not at Random (MNAR). In this work, we introduce the Missing Pattern Recognized …

• Mean-Shift PCA by Knockoff Mean

  Mengda Li, Zeng Li, Jianfeng Yao · 26. Mai 2026

  Removing noise is difficult, but adding noise is easy. In this work, we show how to eliminate mean-shift noisy components from PCA by deliberately introducing knockoff mean-shift perturbation. Standard PCA is highly sensitive to shifts in the sample mean: a small fraction of samples from a shifted d…

• How does Bayesian Sampling help Membership Inference Attacks?

  Zhenlong Liu, Wenyu Jiang, Feng Zhou, Hongxin Wei · 26. Mai 2026

  Membership Inference Attacks (MIAs) aim to estimate whether a specific data point was used in the training of a given model. Existing state-of-the-art attacks typically rely on training multiple reference models to approximate the conditional score distribution for individual data points, which lead…

• Corrected Integrated Laplace Approximation for Bayesian Inference in Latent Gaussian Models

  Jinlin Lai, Charles C. Margossian, Daniel R. Sheldon · 21. Mai 2026

  Latent Gaussian models (LGMs) are a popular class of Bayesian hierarchical models that include Gaussian processes, as well as certain spatial models and mixed-effect models. Efficient Bayesian inference of LGMs often requires marginalizing out the latent variables. For LGMs with a non-Gaussian likel…

• Scalable Subset Selection in Linear Mixed Models

  Ryan Thompson, Matt P. Wand, Joanna J. J. Wang · 15. Mai 2026

  Linear mixed models (LMMs), which incorporate fixed and random effects, are key tools for analyzing heterogeneous data, such as in personalized medicine. Nowadays, this type of data is increasingly wide, sometimes containing thousands of candidate predictors, necessitating sparsity for prediction an…

• Test-Time Compositional Generalization in Diffusion Models via Concept Discovery

  Zekun Wang, Anant Gupta, Tianyi Zhu, Christopher J. MacLellan · 11. Mai 2026

  Compositional generalization requires models to produce novel configurations from familiar parts. In diffusion models, prior compositional generation methods typically assume that the relevant concepts or conditioning signals are already available. We instead ask whether a pretrained diffusion model…

• Diffusion model for SU(N) gauge theories

  Javad Komijani, Marina K. Marinkovic, Lara Turgut · 8. Mai 2026

  Implicit score matching provides a computationally efficient approach for training diffusion models and generating high-quality samples from complex distributions. In this work, we develop a score-matching framework for SU(N) lattice gauge theories, which can be extended to other Lie groups. We appl…

• Super-Level-Set Regression: Conditional Quantiles via Volume Minimization

  Sacha Braun, Michael I. Jordan, Francis Bach · 8. Mai 2026

  Constructing minimum-volume prediction regions that satisfy conditional coverage is a fundamental challenge in multivariate regression. Standard approaches rely on explicitly estimating the full conditional density and subsequently thresholding it. This two-step plug-in process is notoriously diffic…