3 papers
cs.LG2026
Data-Driven Graph Filters via Adaptive Spectral Shaping
Dylan Sandfelder, Mihai Cucuringu, Xiaowen Dong
We introduce Adaptive Spectral Shaping, a data-driven framework for graph filtering that learns a reusable baseline spectral kernel and modulates it with a small set of Gaussian fa…
cs.LG2025
On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective
Ning Zhang, Henry Kenlay, Li Zhang +2
Graph convolutional neural networks (GCNNs) have emerged as powerful tools for analyzing graph-structured data, achieving remarkable success across diverse applications. However, t…
q-fin.PM2025
Tactical Asset Allocation with Macroeconomic Regime Detection
Daniel Cunha Oliveira, Dylan Sandfelder, André Fujita +2
This paper extends the tactical asset allocation literature by incorporating regime modeling using techniques from machine learning. We propose a novel model that classifies curren…