Dino Sejdinović

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages

2015 · arXiv (Cornell University)

We propose an efficient nonparametric strategy for learning a message operator in expectation propagation (EP), which takes as input the set of incoming messages to a factor node, and produces an outgoing message as output. …

Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families

2024 · Proceedings of the AAAI Conference on Artificial Intelligence

We introduce squared neural Poisson point processes (SNEPPPs) by parameterising the intensity function by the squared norm of a two layer neural network. When the hidden layer is fixed and the second layer has a …

Credal Two-Sample Tests of Epistemic Uncertainty

2024 · arXiv (Cornell University)

We introduce credal two-sample testing, a new hypothesis testing framework for comparing credal sets -- convex sets of probability measures where each element captures aleatoric uncertainty and the set itself represents epistemic uncertainty that arises …