5 papers
Diffusion-Based Posterior Sampling: A Feynman-Kac Analysis of Bias and Stability
Matias G. Delgadino, Sebastien Motsch, Advait Parulekar +2
Diffusion-based posterior samplers use pretrained diffusion priors to sample from measurement- or reward-conditioned posteriors, and are widely used for inverse problems. Yet their…
EmDT: Embedding Diffusion Transformer for Tabular Data Generation in Fraud Detection
En-Ya Kuo, Sebastien Motsch
Imbalanced datasets pose a difficulty in fraud detection, as classifiers are often biased toward the majority class and perform poorly on rare fraudulent transactions. Synthetic da…
Wealth exchange under ceiling and flooring constraints: a modified Bennati-Dragulescu-Yakovenko model
Fei Cao, Sebastien Motsch, Wendy Garcia Umbarita
We investigate the classical Bennati-Dragulescu-Yakovenko (BDY) dollar exchange model introduced in \cite{dragulescu_statistical_2000} where the effects of wealth ceiling and wealt…
Generative diffusion models from a PDE perspective
Fei Cao, Kimball Johnston, Thomas Laurent +2
Diffusion models have become the de facto framework for generating new datasets. The core of these models lies in the ability to reverse a diffusion process in time. The goal of th…
Doppelgänger Model: Emergence of polarization in opinion dynamics
Vince Campo, Sebastien Motsch, Dylan Weber
Over the past decade, contrary to the early popular expectation that large-scale discourse in online communities would foster greater consensus, the large-scale structure of online…