3 accepted papers
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…
Detection of anomalies is a common and important problem, especially when anomalies are rare and labels are difficult to acquire. Here we sequentially detect outliers in the outputs of an unknown function, which have been distorted by noise. We model the sequence of outputs by using Yule-Simon proce…
Causal discovery with time series data remains a challenging yet increasingly important task across many scientific domains. Convergent cross mapping (CCM) and related methods have been proposed to study time series that are generated by dynamical systems, where traditional approaches like Granger c…