Uncertainty
On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis
Probabilistic principal component analysis (PPCA) is currently one of the most used statistical tools to reduce the ambient dimension of the data.
A Compositional Atlas for Algebraic Circuits
The key feature of circuits is that they enable one to precisely characterize tractability conditions (structural properties of the circuit) under which a given inference query can be computed exactly and efficiently. One can then enforce these circuit properties when compiling or learning a model to enable tractable inference.
VISA: Variational Inference with Sequential Sample-Average Approximations
We perform experiments on high-dimensional Gaussians, Lotka-V olterra dynamics, and a Pickover attractor.
Partial Transportability for Domain Generalization
Our results are corroborated with experiments.
Diffusion Twigs with Loop Guidance for Conditional Graph Generation
We introduce a novel score-based diffusion framework named Twigs that incorporates multiple co-evolving flows for enriching conditional generation tasks. Specifically, a central or trunk diffusion process is associated with a primary variable (e.g., graph structure), and additional offshoot or stem processes are dedicated
Improving Generalization in Federated Learning with Model-Data Mutual Information Regularization: A Posterior Inference Approach
Theoretical analysis provides a generalization bound for FL w.r.t. the