780 citations · 1.8k across the 76 of their papers we have counts for
8 papers · 1 filter
The Functional Wiener Filter
Benjamin Colburn, Luis G. Sanchez Giraldo, Jose C. Principe
This paper presents a close form solution in Reproducing Kernel Hilbert Space (RKHS) for the famed Wiener filter, which we called the functional Wiener filter(FWF). Instead of usin…
Adapting the Exploration Rate for Value-of-Information-Based Reinforcement Learning
Isaac J. Sledge, Jose C. Principe
In this paper, we consider the problem of adjusting the exploration rate when using value-of-information-based exploration. We do this by converting the value-of-information optimi…
The Normalized Cross Density Functional: A Framework to Quantify Statistical Dependence for Random Processes
Bo Hu, Jose C. Principe
This paper presents a novel approach to measuring statistical dependence between two random processes (r.p.) using a positive-definite function called the Normalized Cross Density…
Robust Dependence Measure using RKHS based Uncertainty Moments and Optimal Transport
Rishabh Singh, Jose C. Principe
Reliable measurement of dependence between variables is essential in many applications of statistics and machine learning. Current approaches for dependence estimation, especially…
Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHS
Rishabh Singh, Jose C. Principe
Deep learning models for semantic segmentation are prone to poor performance in real-world applications due to the highly challenging nature of the task. Model uncertainty quantifi…
Principle of Relevant Information for Graph Sparsification
Shujian Yu, Francesco Alesiani, Wenzhe Yin +2
Graph sparsification aims to reduce the number of edges of a graph while maintaining its structural properties. In this paper, we propose the first general and effective informatio…
Hierarchical Linear Dynamical System for Representing Notes from Recorded Audio
Leila Kalantari, Jose Principe, Kathryn E. Sieving
We seek to develop simultaneous segmentation and classification of notes from audio recordings in presence of outliers. The selected architecture for modeling time series is hierar…
Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing
Hongming Li, Shujian Yu, Jose C. Principe
We develop a new neural network based independent component analysis (ICA) method by directly minimizing the dependence amongst all extracted components. Using the matrix-based R{é…