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Computational Statistics

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Computational Statistics

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Research Frontiers in Variational Inference with Neural Networks

Integration of deep learning architectures with variational inference frameworks for scalable approximate posterior computation.

Amortized Posterior Learning in High-Dimensional Spaces
Neural Implicit Priors and Variational Collapse
Divergence Geometry of Flow-Based Variational Models
Hierarchical Latent Structure Discovery via Neural Inference
Posterior Calibration and Uncertainty Quantification in Neural Variational Inference
Normalizing Flows as Variational Approximators for Non-Euclidean Geometries
Neural Variational Inference Under Model Misspecification
Scalable Structured Inference Through Neural Factorization
Deep Generative Models and the Variational Information Gap
Bridging Variational Objectives and Neural Autoregressive Density Estimation

All Computational Statistics PhD categories