Statistical Learning
Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation
While extensive research on uncertainty estimation has been conducted with unimodal data, uncertainty estimation for multimodal data remains a challenge. Neural processes (NPs) have been demonstrated to be an effective uncertainty estimation method for unimodal data by providing the reliability of Gaussian processes with efficient and powerful DNNs.
A Probability Contrastive Learning Framework for 3D Molecular Representation Learning
It has inspired various applications in molecular property prediction and drug design.
The Price of Implicit Bias in Adversarially Robust Generalization
We study the implicit bias of optimization in robust empirical risk minimization (robust ERM) and its connection with robust generalization.
SwiFT: Swin 4D fMRI Transformer Peter Y ongho Kim
Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience.
Riemannian Projection-free Online Learning
In Euclidean space, OCO boasts a robust theoretical foundation and numerous real-world applications, such as online load balancing (Molinaro, 2017), optimal control (Li et al., 2019), revenue maximization (Lin et al., 2019), and portfolio management (Jรฉzรฉquel et al., 2022).
