Gaussian Mixture Models

Follow this topic Ask about this topic

Recent momentum

-43%

4 papers in the last 28 days · 0.1% of indexed attention

Twelve weeks of publication activity for this topic as it is defined today.

Weekly history

Recent digests

What was published in this topic, kept on the site without email delivery.

Period ending 2026-09-21

2 new papers

A weekly snapshot of new work published in Gaussian Mixture Models.

Period ending 2026-09-14

1 new paper

A weekly snapshot of new work published in Gaussian Mixture Models.

Period ending 2026-09-07

2 new papers

A weekly snapshot of new work published in Gaussian Mixture Models.

72 papers

Latest in Gaussian Mixture Models

Browse all topics →

Federated Soft Clustering via Generalized Total Variation Minimization
Sep 16, 2026Shamsiiat Abdurakhmanova, Alexander JungImage Clustering Gaussian Mixture Models
Certified Uncertainty Propagation in One-Shot Federated Bayesian Models via Posterior Event Transport
Sep 14, 2026Mahyar Mohammadi, Mohammad Hossein Badiei, Abolfazl Yaghmaei +1Bayesian Neural Networks Finite-Sample Certificates
SGD in Multiclass Logistic Regression: Sequential Learning and Scaling Laws
Sep 7, 2026Konstantinos Christopher Tsiolis, Denny Wu, Christos Thrampoulidis +1Multiclass Classification Scaling Laws
Geometric Ceilings on Time-Frequency Masking for Single-Channel Separation
Sep 3, 2026Maxime BaeldeJoint Channel Estimation Time-Frequency
Foundations of Independent Component Analysis
Aug 13, 2026Patrick ForréIndependent Component Analysis Identifiability
Gromov-Wasserstein Quantization and Clustering: Structure, Rates, and Algorithms
Aug 11, 2026Florian Beier, Stephan EcksteinGromov--Wasserstein K-Means
Dynamic Distribution-Aware Uncertainty Tracking in Vision-Language Representation Learning
Aug 10, 2026Ao Zhou, Zhiwei Jiang, Zifeng Cheng +4Vision-Language Gaussian Mixture Models
Mixture of Geodesic Factor Analyzers on Riemannian Homogeneous Spaces
Aug 7, 2026Hengchao Chen, Yuanyao Tan, Chao Huang +2Gaussian Mixture Models Riemannian Manifolds
Minimax Optimal Early-Stopped Gradient Descent for Gaussian Mixture Classification
Aug 6, 2026Alex Buna, Shirley Xiaoqi Liu, Patrick RebeschiniEmpirical Risk Minimization Gaussian Mixture Models
Variational Bounds for Perceptron Learning from Structured Data
Aug 5, 2026Francesco Camilli, Pierluigi Contucci, Federica Gerace +1Variational Empirical Risk Minimization
Lloyd's $K$ -Means Clustering Algorithm Is Frank-Wolfe in Disguise
Jul 28, 2026Michael Pokojovy, J. Marcus Jobe, Simon Lacoste-JulienK-Means Gaussian Mixture Models
RayOcc: Occlusion-Aware Ray Occupancy Estimation via Gaussian Mixture Intensity
Jul 20, 2026Junho Kim, Seongwon Lee3D Semantic Occupancy Prediction Occupancy
Robust Chance-Constrained Optimization using a Continuous Parameter Space Wasserstein-2 Ambiguity Set of Gaussian Mixtures
Jul 19, 2026Shibshankar Dey, Sanjay MehrotraRobust Optimization Gaussian Mixture Models
Dimension-Calibrated Unexplained Mass: An Interpretable Drift Statistic for Contamination Monitoring in Data Streams
Jul 18, 2026Behnam AsadiContamination Gaussian Mixture Models
Spectral Concentration and Recovery in Sparse High-Dimensional Random Geometric Graphs
Jul 15, 2026Manuel Fernandez, Yizhe ZhuExponential Random Graph Models Latent Cluster Geometry
Heavy-Tailed Flow Matching via Random Clocks
Jul 15, 2026Zhouhao Yang, Yezhen Wang, Kenji Kawaguchi +2Conditional Flow Matching Long-Tailed Distribution
UD-ASD: A Unified Diffusion Model for Anomalous Sound Detection
Jul 14, 2026Pengxiang Gao, Yu Qiu, Yanzhi SongMel-Frequency Cepstral Coefficient Ad
Gaussian Mixture Modeling for Event-Aware Visual Allocation in Long Video Understanding
Jul 14, 2026Yifan Lu, Ziqi Zhang, Chunfeng Yuan +3Video Understanding Longvideobench
High-Dimensional Procrustes Matching via Tree Counts
Jul 9, 2026Xiaochun Niu, Tselil Schramm, Jiaming XuHigh-Dimensional Data Feature Matching
Mixture-of-Gaussians-Guided Schedule Design for Brownian Bridge Diffusion Models
Jul 3, 2026Ron Levi, Michael EladSchrödinger Bridges Image Restoration
Regularized Variational and Spectral Log-Density-Ratio Estimation in the Gaussian Location Model
Jul 2, 2026Francis BachDensity Ratio Estimation Gaussian Mixture Models
eXact-Prior Variational Autoencoder (X-VAE): Learning Data-Adaptive Gaussian Mixture Priors for Latent Distributions
Jun 30, 2026Qijun Chen, Shaofan LiVariational Autoencoder Generative Priors
Multi-Contact Force Estimation for Continuum Robots via Gaussian-Parameterized Factor Graphs
Jun 28, 2026Aditya Prakash, Panagiotis TsiotrasGround Reaction Forces Contact Forces
Fast algorithms for learning a Gaussian under halfspace truncation with optimal sample complexity
Jun 25, 2026Haitong Liu, Deepak Narayanan Sridharan, David Steurer +1Optimal Sample Complexity Gaussian Mixture Models
Rethinking Training & Inference for Forecasting: Linking Winner-Take-All back to GMMs
Jun 24, 2026Qiyuan Wu, Katie Z Luo, Bharath Hariharan +2Trajectory Prediction Llm-Based Forecasting Methods
From Point Estimates to Distributions: GMM Pooling for MIL in Preterm Birth Prediction
Jun 22, 2026Hussain Alasmawi, Numan Saeed, Soha Said +1Multiple Instance Learning Gaussian Mixture Models
Global Convergence of Gradient Descent for Score Matching in Gaussian Mixtures via Reverse Fisher Divergence
Jun 18, 2026Alexander TyurinGaussian Mixture Models Gradient Descent
S-JEPA : Soft Clustering Anchors for Self-Supervised Speech Representation Learning
Jun 17, 2026Georgios Ioannides, Adrian Kieback, Judah Goldfeder +5Sphere-Jepa Representation Learning
Transductive Zero-Shot Audio Classification with Audio-Language Models
Jun 15, 2026Jingwen Zhou, Mingzhe WangSpeech Separation Transductive Active Labeling
Flash-GMM: A Memory-Efficient Kernel for Scalable Soft Clustering
Jun 9, 2026Gal Bloch, Ariel Gera, Matan Orbach +2Gaussian Mixture Models Image Clustering
SSL-GMMVC: Interpretable Voice Conversion via Locally Linear GMM Transforms in Self-Supervised Representation Space
Jun 9, 2026Tomoya Tanabu, Hiroshi Nishijima, Daisuke Saito +1Self-Supervised Speech Models Self-Supervised Representations
Beyond Self-Attention: Sub-Quadratic Vision Transformers for Fast Image Captioning
Jun 7, 2026Chiradeep Ghosh, Dakshina Ranjan KiskuSelf-Supervised Vision Transformers Vision Transformer
How abundant are good interpolators?
Jun 4, 2026August Y. Chen, Ahmed El AlaouiOver-Parameterized Neural Networks Empirical Risk Minimization
BPDA-GMM: Bayesian Probabilistic Data Association via Gaussian Mixture Models for Semantic SLAM
Jun 3, 2026Thanh Nguyen Canh, Haolan Zhang, Xiem HoangVan +2Simultaneous Localization And Mapping Gaussian Mixture Models
A Quantitative Approximation Framework for Flow Distillation in Diffusion Models
Jun 2, 2026Weiguo Gao, Ming Li, Lei Shi +1Flow Map Approximation
Local linear convergence of gradient methods for overparameterized Gaussian mixtures
May 29, 2026Jingxing Wang, Vasileios Charisopoulos, Maryam FazelGaussian Mixture Models Over-Parameterized Neural Networks
Convex Basins in Single-Index Model Loss Landscapes: Applications to Robust Recovery under Strong Adversarial Corruption
May 28, 2026Santanu Das, Sagnik Chatterjee, Jatin BatraLoss Landscape Gaussian Mixture Models
Mind the Gap: Mixtures of Gaussians in Approximate Differential Privacy
May 27, 2026Huikang Liu, Aras Selvi, Wolfram WiesemannDifferential Privacy Gaussian Mixture Models
Robust Moment-Based Estimation via Spectral Gradient Reweighting
May 26, 2026Liu Zhang, Amit SingerEstimators Gaussian Mixture Models
Certified Robustness from Approximate Gaussian Mixture Structures in Pretrained Latent Spaces
May 25, 2026Konstantinos Emmanouilidis, Tianjiao Ding, Nghia Nguyen +2Adversarial Transferability Gaussian Mixture Models
Optimizing Multidimensional Scaling in Gini Metric Spaces
May 24, 2026Cassandra Mussard, Stéphane MussardMaximum Mean Discrepancy Gaussian Mixture Models
Lifted Schrödinger Bridges for Gaussian Mixture Endpoints: Projection Gaps and Path-Space Obstructions
May 24, 2026Siddhartha Ganguly, George Rapakoulias, Panagiotis TsiotrasSchrödinger Bridges Gaussian Mixture Models
Reinforcement Learning for Risk Adaptation via Differentiable CVaR Barrier Functions
May 20, 2026Xinyi Wang, Taekyung Kim, Bardh Hoxha +2Obstacle Avoidance Constrained Reinforcement Learning
Robust Recommendation from Noisy Implicit Feedback: A GMM-Weighted Bayes-label Transition Matrix Framework
May 20, 2026Zongyu Li, Xuanyu Liu, Gongce Cao +3Noisy Labels Recommender Systems
A Semantic and Occlusion-Aware GM-PHD Filter
May 20, 2026Jovan Menezes, Mark CampbellParticle Filters Multi-Object Tracking
From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models
May 19, 2026Jianan Yang, Yiran Wang, Shuai Li +3Conventional Physics-Informed Neural Networks Partial Differential Equation Benchmarks
Privacy Policy Enforcement Guardrails for Data-Sensitive Retrieval-Augmented Generation
May 16, 2026Osama Zafar, Alexander Nemecek, Yiqian Zhang +5AI Privacy Risks and Protection Data Leakage
Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures
May 15, 2026Lorenzo Baldassari, Josselin Garnier, Knut Solna +1Langevin Dynamics Diffusion Sampling
PrismQuant: Rate-Distortion-Optimal Vector Quantization for Gaussian-Mixture Sources
May 15, 2026Bumsu Park, Chanho Park, Youngmok Park +1Rate-Distortion Theory Calibration-Free Low-Bit Vector Quantization
A Mixture Autoregressive Image Generative Model on Quadtree Regions for Gaussian Noise Removal via Variational Bayes and Gradient Methods
May 12, 2026Shota Saito, Yuta Nakahara, Kohei Horinouchi +3Image Denoising Autoregressive Image Generation
Sliced Inner Product Gromov-Wasserstein Distances
May 8, 2026Xiaoyun Gong, Gabriel Rioux, Ziv GoldfeldGromov--Wasserstein Slices
When Diffusion Model Can Ignore Dimension: An Entropy-Based Theory
May 8, 2026Ahmad Aghapour, Erhan BayraktarDiffusion Sampling High-Dimensional Data
One Operator for Many Densities: Amortized Approximation of Conditioning by Neural Operators
May 7, 2026Panos Tsimpos, Edoardo Calvello, Ayoub Belhadji +1Conditional Distribution Probabilistic Operator Learning
Fast Gauss-Newton for Multiclass Cross-Entropy
May 7, 2026Mikalai Korbit, Mario ZanonCross-Entropy Losses Multiclass Classification
Gaussian mixture models in Hilbert spaces via kernel methods
May 7, 2026Daniel López-Montero, Antonio Álvarez-López, Marcos MatabuenaKernel Hilbert Spaces Gaussian Mixture Models
A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning
May 5, 2026Dario Filatrella, Ragnar Thobaben, Mikael SkoglundFederated Learning Generalization Bounds
Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection
May 4, 2026Fuyun Wang, Yuanzhi Wang, Xu Guo +6Reconstruction-Based Anomaly Detection Anomaly Detection
Analytic Bridge Diffusions for Controlled Path Generation
May 3, 2026Michael ChertkovSchrödinger Bridges Stochastic Differential Equations
Adaptive Transform Coding for Semantic Compression
Apr 29, 2026Andriy Enttsel, Vincent CorlaySelf-Supervised Vision Transformers Rate-Distortion Theory
Investigation into In-Context Learning Capabilities of Transformers
Apr 28, 2026Rushil Chandrupatla, Leo Bangayan, Sebastian LengIn-Context Learning In-Context

Sep 16, 2026 · stat.MLJ/K move · Enter open · S save

Federated Soft Clustering via Generalized Total Variation Minimization

Shamsiiat Abdurakhmanova, Alexander Jung

Department of Computer Science, Aalto University, Espoo, Finland

We study federated soft clustering over federated learning (FL) networks of devices that each hold a private local dataset and fit a personalized Gaussian mixture model (GMM). Generalized total variation minimization (GTVMin) couples the local maximum likelihood problems through a graph regularizer that penalizes a discrepancy between the models of connected nodes. The choice of discrepancy measure is a key design decision: we compare a squared Euclidean distance between model parameters, which requires component matching, with two measures that compare the local model distributions directly and hence need no matching: a Monte-Carlo approximated Kullback-Leibler (KL) divergence and a closed-form maximum mean discrepancy (MMD). All three resulting GTVMin instances are optimized by synchronous projected gradient updates; for the smooth MMD instance we provide a convergence guarantee to stationary points. We characterize their computational cost and evaluate their robustness to data heterogeneity.

Image Clustering Gaussian Mixture Models Maximum Likelihood

Open paper Ask about it PDF