Journal Club
A cumulative seminar archive for in-semester paper discussions on uncertainty quantification, scientific machine learning, and related statistical methodology.
TBA
TBA
An Introduction to PINNs and Their Convergence
Open SessionTBA
Tabular Foundation Models - From Tree-based Methods to TabPFN
Open SessionFrom GBDTs to TabPFN: how deep learning finally started to competing on tabular data through in-context-learning foundation models, and where this new paradigm still breaks.
Marginal Tail-Adaptive Normalizing Flows
Open SessionTBA
Uncertainty Quantification in Vision Transformer
Open SessionLikelihood-guided Regularization in Attention Based Models.
Introduction to Flow Matching
Open SessionFlow matching is a framework for generative modeling that learns a vector field transporting a simple reference distribution to a complex target via an ODE.