Bayesian Methods and Mixture Models

Últimos artículos

• Clustering Approaches for Mixed-Type Data: A Comparative Study

  Badih Ghattas, Alvaro Sanchez San-Benito · 26 de noviembre de 2025

  Clustering is widely used in unsupervised learning to find homogeneous groups of observations within a dataset. However, clustering mixed-type data remains a challenge, as few existing approaches are suited for this task. This study presents the state-of-the-art of these approaches and compares them…

• Non-equilibrium Annealed Adjoint Sampler

  Jaemoo Choi, Yongxin Chen, Molei Tao, Guan-Horng Liu · 26 de noviembre de 2025

  Recently, there has been significant progress in learning-based diffusion samplers, which aim to sample from a given unnormalized density. Many of these approaches formulate the sampling task as a stochastic optimal control (SOC) problem using a canonical uninformative reference process, which limit…

• Classification EM-PCA for clustering and embedding

  Zineddine Tighidet, Lazhar Labiod, Mohamed Nadif · 25 de noviembre de 2025

  The mixture model is undoubtedly one of the greatest contributions to clustering. For continuous data, Gaussian models are often used and the Expectation-Maximization (EM) algorithm is particularly suitable for estimating parameters from which clustering is inferred. If these models are particularly…

• Variational Inference with Mixtures of Isotropic Gaussians

  Marguerite Petit-Talamon, Marc Lambert, Anna Korba · 18 de noviembre de 2025

  Variational inference (VI) is a popular approach in Bayesian inference, that looks for the best approximation of the posterior distribution within a parametric family, minimizing a loss that is typically the (reverse) Kullback-Leibler (KL) divergence. In this paper, we focus on the following paramet…

• Federated Variational Inference for Bayesian Mixture Models

  Jackie Rao, Francesca L. Crowe, Tom Marshall, Sylvia Richardson, Paul D. W. Kirk · 13 de noviembre de 2025

  We present a federated learning approach for Bayesian model-based clustering of large-scale binary and categorical datasets. We introduce a principled 'divide and conquer' inference procedure using variational inference with local merge and delete moves within batches of the data in parallel, follow…

• Stochastic Mean-Shift Clustering

  Itshak Lapidot, Yann Sepulcre, Tom Trigano · 13 de noviembre de 2025

  We present a stochastic version of the mean-shift clustering algorithm. In this stochastic version a randomly chosen sequence of data points move according to partial gradient ascent steps of the objective function. Theoretical results illustrating the convergence of the proposed approach, and its r…

• Parsimonious Gaussian mixture models with piecewise-constant eigenvalue profiles

  Tom Szwagier, Pierre-Alexandre Mattei, Charles Bouveyron, Xavier Pennec · 10 de noviembre de 2025

  Gaussian mixture models (GMMs) are ubiquitous in statistical learning, particularly for unsupervised problems. While full GMMs suffer from the overparameterization of their covariance matrices in high-dimensional spaces, spherical GMMs (with isotropic covariance matrices) certainly lack flexibility …

• Multivariate Bernoulli Hoeffding Decomposition: From Theory to Sensitivity Analysis

  Baptiste Ferrere (EDF R\&D PRISME, IMT, SINCLAIR AI Lab), Nicolas Bousquet (EDF R\&D PRISME, SINCLAIR AI Lab, LPSM), Fabrice Gamboa (IMT, ANITI), Jean-Michel Loubes (IMT, ANITI), Joseph Mur\'e (EDF R\&D PRISME) · 6 de noviembre de 2025

  Understanding the behavior of predictive models with random inputs can be achieved through functional decompositions into sub-models that capture interpretable effects of input groups. Building on recent advances in uncertainty quantification, the existence and uniqueness of a generalized Hoeffding …

• A Unified Framework for Variable Selection in Model-Based Clustering with Missing Not at Random

  Binh H. Ho, Long Nguyen Chi, TrungTin Nguyen, Binh T. Nguyen, Van Ha Hoang, Christopher Drovandi · 5 de noviembre de 2025

  Model-based clustering integrated with variable selection is a powerful tool for uncovering latent structures within complex data. However, its effectiveness is often hindered by challenges such as identifying relevant variables that define heterogeneous subgroups and handling data that are missing …

• Overspecified Mixture Discriminant Analysis: Exponential Convergence, Statistical Guarantees, and Remote Sensing Applications

  Arman Bolatov, Alan Legg, Igor Melnykov, Amantay Nurlanuly, Maxat Tezekbayev, Zhenisbek Assylbekov · 3 de noviembre de 2025

  This study explores the classification error of Mixture Discriminant Analysis (MDA) in scenarios where the number of mixture components exceeds those present in the actual data distribution, a condition known as overspecification. We use a two-component Gaussian mixture model within each class to fi…

• VIKING: Deep variational inference with stochastic projections

  Samuel G. Fadel, Hrittik Roy, Nicholas Kr\"amer, Yevgen Zainchkovskyy, Stas Syrota, Alejandro Valverde Mahou, Carl Henrik Ek, S{\o}ren Hauberg · 29 de octubre de 2025

  Variational mean field approximations tend to struggle with contemporary overparametrized deep neural networks. Where a Bayesian treatment is usually associated with high-quality predictions and uncertainties, the practical reality has been the opposite, with unstable training, poor predictive power…

• Transformers can do Bayesian Clustering

  Prajit Bhaskaran, Tom Viering · 29 de octubre de 2025

  Bayesian clustering accounts for uncertainty but is computationally demanding at scale. Furthermore, real-world datasets often contain missing values, and simple imputation ignores the associated uncertainty, resulting in suboptimal results. We present Cluster-PFN, a Transformer-based model that ext…

• Diffusion Generative Modeling on Lie Group Representations

  Marco Bertolini, Tuan Le, Djork-Arn\'e Clevert · 28 de octubre de 2025

  We introduce a novel class of score-based diffusion processes that operate directly in the representation space of Lie groups. Leveraging the framework of Generalized Score Matching, we derive a class of Langevin dynamics that decomposes as a direct sum of Lie algebra representations, enabling the m…

• MMbeddings: Parameter-Efficient, Low-Overfitting Probabilistic Embeddings Inspired by Nonlinear Mixed Models

  Giora Simchoni, Saharon Rosset · 28 de octubre de 2025

  We present MMbeddings, a probabilistic embedding approach that reinterprets categorical embeddings through the lens of nonlinear mixed models, effectively bridging classical statistical theory with modern deep learning. By treating embeddings as latent random effects within a variational autoencoder…

• Input Adaptive Bayesian Model Averaging

  Yuli Slavutsky, Sebastian Salazar, David M. Blei · 28 de octubre de 2025

  This paper studies prediction with multiple candidate models, where the goal is to combine their outputs. This task is especially challenging in heterogeneous settings, where different models may be better suited to different inputs. We propose input adaptive Bayesian Model Averaging (IA-BMA), a Bay…