Dejan Slepcev — accepted AI-conference papers

2022

Accurate Quantization of Measures via Interacting Particle-based Optimization

ICML 2022spotlight

Approximating a target probability distribution can be cast as an optimization problem where the objective functional measures the dissimilarity to the target. This optimization can be addressed by approximating Wasserstein and related gradient flows. In practice, these are simulated by interacting…

Cited by 19 Source ↗PDF ↗Scholar ↗

2020

Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates

Jeff Calder, Brendan Cook, Matthew Thorpe, Dejan Slepcev

ICML 2020poster

We propose a new framework, called Poisson learning, for graph based semi-supervised learning at very low label rates. Poisson learning is motivated by the need to address the degeneracy of Laplacian semi-supervised learning in this regime. The method replaces the assignment of label values at train…

Cited by 118 Source ↗PDF ↗code ↗Scholar ↗