NS-NTK

This repository is the official code release for an academic paper titled "Deep Learning under Continuous Distribution Shift: The Non-Stationary NTK and Spectral Tracking SDE for Quantitative Finance." The paper appears to have been posted in May 2026, though it is only linked as a local PDF and is not publicly accessible through this README.

The subject matter sits at the intersection of machine learning theory and financial markets. The core idea concerns how neural networks behave when the data they were trained on keeps changing over time, which is a common challenge in finance where market conditions shift continuously. The repository name "NS-NTK" refers to the Non-Stationary Neural Tangent Kernel, a mathematical framework for analyzing how neural networks learn.

According to the README, the code includes several components: PVR and EAT optimizers (training algorithms), Exponential Aggregation samplers (a method for combining data or model outputs), and Temporal Transformers (a type of neural network designed for time-series data). No explanation of how to install or run any of these is provided in the README.

The documentation is very sparse. Beyond the component list and a reference table image, there are no setup instructions, usage examples, or explanations of what the tools actually do in practice. Anyone who wants to use this code would need to read the linked paper for context, and that paper is not publicly hosted through this repository. This is a research code release aimed at other machine learning researchers, not a general-use library.