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.
New method selects FMM components via variational Bayes.
Enhances swaption modeling with rough stochastic volatility.
FMM fails to accurately determine the number of components even with consistent posterior.
Derives PDEs for pricing RFR derivatives under a new FMM model.
New algorithm improves on existing methods for solving transport problems.
This paper improves SABR/LMM for better practical use in global banks.
This work studies the class of algorithms for learning with side-information that emerge by extending generative models with embedded context-related variables. Using finite mixture models (FMM) as the prototypical Bayesian network, we show that maximum-likelihood estimation (MLE) of parameters through expectation-maxi…
We present a general method for fitting finite mixture models (FMM). Learning in a mixture model consists of finding the most likely cluster assignment for each data-point, as well as finding the parameters of the clusters themselves. In many mixture models, this is difficult with current learning methods, where the mo…
We consider fast kernel summations in high dimensions: given a large set of points in dimensions (with ) and a pair-potential function (the {\em kernel} function), we compute a weighted sum of all pairwise kernel interactions for each point in the set. Direct summation is equivalent to a (dense) matrix-vec…