Image de couverture de livre

A Unified Theory of Exact Inference and Learning in Exponential Family Latent Variable Models

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Sokoloski, Sacha
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics
par: Sokoloski, Sacha
Publié: (2015)

Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering
par: Sokoloski, Sacha, et autres
Publié: (2022)

Conditional Finite Mixtures of Poisson Distributions for Context-Dependent Neural Correlations
par: Sokoloski, Sacha, et autres
Publié: (2019)

Exponential Family Attention
par: Wibisono, Kevin Christian, et autres
Publié: (2025)

Noise-Calibrated Inference from Differentially Private Sufficient Statistics in Exponential Families
par: Asiaee, Amir, et autres
Publié: (2026)

A Latent Causal Inference Framework for Ordinal Variables
par: Scauda, Martina, et autres
Publié: (2025)

Diffusion Models for Inverse Problems in the Exponential Family
par: Micheli, Alessandro, et autres
Publié: (2025)

Transfer Learning for Latent Variable Network Models
par: Jalan, Akhil, et autres
Publié: (2024)

High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching
par: Williams, Daniel J., et autres
Publié: (2024)

Graph Laplacian Learning with Exponential Family Noise
par: Shi, Changhao, et autres
Publié: (2023)

Likelihood Based Inference in Fully and Partially Observed Exponential Family Graphical Models with Intractable Normalizing Constants
par: Chen, Yujie, et autres
Publié: (2024)

Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix
par: Margossian, Charles C., et autres
Publié: (2024)

Causal Inference with Latent Variables: Recent Advances and Future Prospectives
par: Zhu, Yaochen, et autres
Publié: (2024)

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective
par: Chen, Tianhua
Publié: (2025)

Safe Control using Learned Safety Filters and Adaptive Conformal Inference
par: Huriot, Sacha, et autres
Publié: (2026)

Identifiable Markov Switching Models with Instantaneous Effects and Exponential Families
par: Hulsman, Roel, et autres
Publié: (2026)

Bayesian Active Learning for Discrete Latent Variable Models
par: Jha, Aditi, et autres
Publié: (2022)

From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables
par: Li, Zongyu
Publié: (2026)

Variational Inference, Entropy, and Orthogonality: A Unified Theory of Mixture-of-Experts
par: Su, Ye, et autres
Publié: (2026)

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold
par: Liu, Song, et autres
Publié: (2025)

Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families
par: Schwienhorst, Benedikt Lütke, et autres
Publié: (2023)

Exponential Quantum Communication Advantage in Distributed Inference and Learning
par: Gilboa, Dar, et autres
Publié: (2023)

Deep Latent Variable Modeling of Physiological Signals
par: Vo, Khuong
Publié: (2024)

HiVAE: Hierarchical Latent Variables for Scalable Theory of Mind
par: Doering, Nigel, et autres
Publié: (2026)

Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures
par: Lin, Wu, et autres
Publié: (2019)

Inverse Optimization Latent Variable Models for Learning Costs Applied to Route Problems
par: Lahoud, Alan A., et autres
Publié: (2025)

Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm
par: Cuin, James, et autres
Publié: (2025)

Adaptive Robust Learning using Latent Bernoulli Variables
par: Karakulev, Aleksandr, et autres
Publié: (2023)

Latent-Variable Learning of SPDEs via Wiener Chaos
par: Zeng, Sebastian, et autres
Publié: (2026)

Preventing Model Collapse in Gaussian Process Latent Variable Models
par: Li, Ying, et autres
Publié: (2024)

Accurate Coresets for Latent Variable Models and Regularized Regression
par: Ranjan, Sanskar, et autres
Publié: (2024)

Weighted-Sum of Gaussian Process Latent Variable Models
par: Odgers, James, et autres
Publié: (2024)

Identifying Metric Structures of Deep Latent Variable Models
par: Syrota, Stas, et autres
Publié: (2025)

Particle Dynamics for Latent-Variable Energy-Based Models
par: Tang, Shiqin, et autres
Publié: (2025)

Latent Variable Modeling for Robust Causal Effect Estimation
par: Morimura, Tetsuro, et autres
Publié: (2025)

Exponential Family Variational Flow Matching for Tabular Data Generation
par: Guzmán-Cordero, Andrés, et autres
Publié: (2025)

The Unified Balance Theory of Second-Moment Exponential Scaling Optimizers in Visual Tasks
par: Zhang, Gongyue, et autres
Publié: (2024)

Learning Discrete Latent Variable Structures with Tensor Rank Conditions
par: Chen, Zhengming, et autres
Publié: (2024)

Achieving Approximate Symmetry Is Exponentially Easier than Exact Symmetry
par: Tahmasebi, Behrooz, et autres
Publié: (2025)

Variational Inference for Latent Variable Models in High Dimensions
par: Zhong, Chenyang, et autres
Publié: (2025)