Jose C. Principe | Sohan Seth

Latest

Quantized mixture kernel least mean square
Learning dependence from samples
Mixture kernel least mean square
Adaptive Inverse Control of Neural Spatiotemporal Spike Patterns With a Reproducing Kernel Hilbert Space (RKHS) Framework
Kernel Methods on Spike Train Space for Neuroscience: A Tutorial
Strictly Positive-Definite Spike Train Kernels for Point-Process Divergences
Conditional Association
Online efficient learning with quantized KLMS and L1 regularization
Analyzing dependence structure of the human brain in response to visual stimuli
Functional Connectivity Dynamics Among Cortical Neurons: A Dependence Analysis
An adaptive decoder from spike trains to micro-stimulation using kernel least-mean-squares (KLMS)
A Unified Framework for Quadratic Measures of Independence
Evaluating dependence in spike train metric spaces
A metric approach toward point process divergence
Estimation of symmetric chi-square divergence for point processes
A test of independence based on a generalized correlation function
Variable Selection: A Statistical Dependence Perspective
A conditional independence perspective of variable selection
A Test of Granger Non-causality Based on Nonparametric Conditional Independence
A conditional distribution function based approach to design nonparametric tests of independence and conditional independence
Estimation of density ratio and its application to design a measure of dependence
On speeding up computation in information theoretic learning
A new nonparametric measure of conditional independence
Compressed signal reconstruction using the correntropy induced metric
Signal Processing with Echo State Networks in the Complex Domain