2 citations · 4 across the 10 of their papers we have counts for
5 papers · 1 filter
Contrastive Entropy Bounds for Density and Conditional Density Decomposition
Bo Hu, Jose C. Principe
This paper studies the interpretability of neural network features from a Bayesian Gaussian view, where optimizing a cost is reaching a probabilistic bound; learning a model approx…
A Simple and Effective Method for Uncertainty Quantification and OOD Detection
Yaxin Ma, Benjamin Colburn, Jose C. Principe
Bayesian neural networks and deep ensemble methods have been proposed for uncertainty quantification; however, they are computationally intensive and require large storage. By util…
ELEMENT: Episodic and Lifelong Exploration via Maximum Entropy
Hongming Li, Zhao Yang, Shujian Yu +3
Reinforcement learning agents depend on reward signals whose density is rarely under the designer's control, and when such signals are absent, an agent must generate its own drive…
Cauchy-Schwarz Divergence Information Bottleneck for Regression
Shujian Yu, Xi Yu, Sigurd Løkse +2
The information bottleneck (IB) approach is popular to improve the generalization, robustness and explainability of deep neural networks. Essentially, it aims to find a minimum suf…
An Alternate View on Optimal Filtering in an RKHS
Benjamin Colburn, Jose C. Principe, Luis G. Sanchez Giraldo
Kernel Adaptive Filtering (KAF) are mathematically principled methods which search for a function in a Reproducing Kernel Hilbert Space. While they work well for tasks such as time…