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Guanchao Feng

7 accepted papers

2026

Higher-Order Feature Attribution: Bridging Statistics, Explainable AI, and Topological Signal Processing

ICASSP 2026poster

Feature attributions are post-training analysis methods that assess how various input features of a machine learning model contribute to an output prediction. Their interpretation is straightforward when features act independently, but it becomes less clear when the predictive model involves interac…

Cited by 2SourcePDFScholar
2022

Improving Phase-Rectified Signal Averaging for Fetal Heart Rate Analysis

ICASSP 2022accepted

Low umbilical artery pH is a marker for neonatal acidosis and is associated with an increased risk for neonatal complications. The phase-rectified signal averaging (PRSA) features have demonstrated superior discriminatory or diagnostic ability and good interpretability in many biomedical application…

Cited by 0SourceScholar
2020

Discovering Causalities from Cardiotocography Signals using Improved Convergent Cross Mapping with Gaussian Processes

ICASSP 2020accepted

Convergent cross mapping (CCM) is designed for causal discovery in coupled time series, where Granger causality may not be applicable because of a separability assumption. However, CCM is not robust to observation noise which limits its applicability on signals that are known to be noisy. Moreover,…

Cited by 0SourceScholar
2020

Improving Convergent Cross Mapping for Causal Discovery with Gaussian Processes

ICASSP 2020accepted

Convergent cross mapping (CCM) is designed for causal discovery between coupled time series for which Granger's method for detecting causality is shown to be unreliable. The theoretical foundation of CCM is based on state space reconstruction, and therefore, for the accuracy of its results, the qual…

Cited by 0SourceScholar
2019

Inference about Causality from Cardiotocography Signals Using Gaussian Processes

ICASSP 2019accepted

In this paper, we propose a novel and simple method for discovery of Granger causality from noisy time series using Gaussian processes. More specifically, we adopt the concept of Granger causality, but instead of using autoregressive models for establishing it, we work with Gaussian processes. We sh…

Cited by 0SourceScholar
2019

RF-based Analytics Generated by Tag-to-tag Networks

ICASSP 2019accepted

We have developed a type of RFID tags that can communicate with each other directly if there is an RF signal in their environment to support backscattering. These tags are passive and they can form a tag-to-tag network. Our tags communicate by what we refer to as multiphase probing. With this techni…

Cited by 0SourceScholar