A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Develops framework for valuing and assessing risk of renewable PPAs.
Deep hedging strategies for Green PPAs in electricity markets reduce risk.
New method corrects correlation bias in feature importance.
This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) generalizes PCA by modeling the directions of maximal variance by means of curves, instead of straight lines. Co…
Paper develops framework for valuing and assessing credit risk in renewable PPAs.
Develops a semi-static strategy for hedging renewable PPAs, separating price and volume risks.
This paper introduces a new unsupervised method for dimensionality reduction via regression (DRR). The algorithm belongs to the family of invertible transforms that generalize Principal Component Analysis (PCA) by using curvilinear instead of linear features. DRR identifies the nonlinear features through multivariate r…
Introduces PPMM algorithm for nonconvex robust regression problems.
New model clusters graphs using Gromov-Wasserstein discrepancy.
This work includes all the technical details of the Sequential Principal Curves Analysis (SPCA) in a single document. SPCA is an unsupervised nonlinear and invertible feature extraction technique. The identified curvilinear features can be interpreted as a set of nonlinear sensors: the response of each sensor is the pr…