activity
20172026
most citedGranger Causality for Compressively Sensed Sparse Signals

3 citations · 10 across the 9 of their papers we have counts for

collaborators

9 papers

stat.ML2026

Dictionary Based Pattern Entropy for Causal Direction Discovery

Harikrishnan N B, Shubham Bhilare, Aditi Kathpalia +1

Discovering causal direction from temporal observational data is particularly challenging for symbolic sequences, where functional models and noise assumptions are often unavailabl…

cs.IT2023

Compression Spectrum: Where Shannon meets Fourier

Aditi Kathpalia, Nithin Nagaraj

Signal processing and Information theory are two disparate fields used for characterizing signals for various scientific and engineering applications. Spectral/Fourier analysis, a…

eess.SP2022★ 3 cited

Granger Causality for Compressively Sensed Sparse Signals

Aditi Kathpalia, Nithin Nagaraj

Compressed sensing is a scheme that allows for sparse signals to be acquired, transmitted and stored using far fewer measurements than done by conventional means employing Nyquist…

cs.LG2022★ 1 cited

Cause-Effect Preservation and Classification using Neurochaos Learning

Harikrishnan N B, Aditi Kathpalia, Nithin Nagaraj

Discovering cause-effect from observational data is an important but challenging problem in science and engineering. In this work, a recently proposed brain inspired learning algor…

cs.LG2021

Learning Generalized Causal Structure in Time-series

Aditi Kathpalia, Keerti P. Charantimath, Nithin Nagaraj

The science of causality explains/determines 'cause-effect' relationship between the entities of a system by providing mathematical tools for the purpose. In spite of all the succe…

stat.ME2019★ 2 cited

Measuring Causality: The Science of Cause and Effect

Aditi Kathpalia, Nithin Nagaraj

Determining and measuring cause-effect relationships is fundamental to most scientific studies of natural phenomena. The notion of causation is distinctly different from correlatio…

cs.LG2019★ 1 cited

ChaosNet: A Chaos based Artificial Neural Network Architecture for Classification

Harikrishnan Nellippallil Balakrishnan, Aditi Kathpalia, Snehanshu Saha +1

Inspired by chaotic firing of neurons in the brain, we propose ChaosNet -- a novel chaos based artificial neural network architecture for classification tasks. ChaosNet is built us…

math.DS2019★ 2 cited

Causal Stability and Synchronization

Aditi Kathpalia, Nithin Nagaraj

Synchronization of chaos arises between coupled dynamical systems and is very well understood as a temporal phenomena which leads the coupled systems to converge or develop a depen…

physics.data-an2017★ 1 cited

Causality Testing: A Data Compression Framework

Aditi Kathpalia, Nithin Nagaraj

Causality testing, the act of determining cause and effect from measurements, is widely used in physics, climatology, neuroscience, econometrics and other disciplines. As a result,…