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.
This paper improves autoregressive model training by focusing on test metrics, not just likelihood.
Paper addresses incorrectness of nearest neighbor in ranking models.
CPMetric learns distances between structured preferences using deep neural networks.
This paper presents performance analysis of hybrid model comprise of concordance and Genetic Programming (GP) to forecast financial market with some existing models. This scheme can be used for in depth analysis of stock market. Different measures of concordances such as Kendalls Tau, Ginis Mean Difference, Spearmans R…
Two new methods improve coherence modeling without complex machine translation.
We extend the recently introduced theory of Lovasz-Bregman (LB) divergences (Iyer & Bilmes 2012) in several ways. We show that they represent a distortion between a "score" and an "ordering", thus providing a new view of rank aggregation and order based clustering with interesting connections to web ranking. We show ho…
New findings challenge the importance of forecast accuracy in battery storage optimization, highlighting the role of rank correlation instead.
Crypto crashes show no consistent early warning signal, suggesting they are abrupt shocks rather than critical transitions.