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.
Defines data science as a natural ecosystem with challenges and missions.
Evaluates six ETSC algorithms on various datasets.
Building and expanding on principles of statistics, machine learning, and scientific inquiry, we propose the predictability, computability, and stability (PCS) framework for veridical data science. Our framework, comprised of both a workflow and documentation, aims to provide responsible, reliable, reproducible, and tr…
A simpler metric for latent space geometry.
In this paper, we show how simple logistic growth that was studied intensively during the last 200 years in many domains of science could be extended in a rather simple way and with these extensions is capable to produce a collection of behaviors widely observed in an enormous number of real-life systems in Economics, …
Provides a compendium of data sources for various applications.
PHOTONAI simplifies machine learning model development in Python.
Why do nations produce scientific research? This is a fundamental problem in the field of social studies of science. The paper confronts this question here by showing vital determinants of science to explain the sources of social power and wealth creation by nations. Firstly, this study suggests a new general definitio…
Survey on AI math foundations, focusing on neural networks.
Enhances insurance loss models using InsurTech data and machine learning.
The ubiquitous availability of wearable sensors is responsible for driving the Internet-of-Things but is also making an impact on sport sciences and precision medicine. While human activity recognition from smartphone data or other types of inertial measurement units (IMU) has evolved to one of the most prominent daily…
A four-pronged approach to dealing with Social Science Phenomenon is outlined. This methodology is applied to Financial Services, Economic Growth and Well-Being. The four prongs are like the four directions for an army general looking for victory. Just like the four directions, we need to be aware that there is a degre…
We use mass-transportation as a tool to compare surfaces (2-manifolds). In particular, we determine the "similarity" of two given surfaces by solving a mass-transportation problem between their conformal densities. This mass transportation problem differs from the standard case in that we require the solution to be inv…
Federated learning approach for binary matrix factorization.
This paper reviews radar-based nowcasting techniques for short-term weather predictions.
Experimental life sciences like biology or chemistry have seen in the recent decades an explosion of the data available from experiments. Laboratory instruments become more and more complex and report hundreds or thousands measurements for a single experiment and therefore the statistical methods face challenging tasks…
In life sciences, the experts generally use empirical knowledge to recode variables, choose interactions and perform selection by classical approach. The aim of this work is to perform automatic learning algorithm for variables selection which can lead to know if experts can be help in they decision or simply replaced …
Deep learning improves community detection in graph datasets.
The paper examines higher moments in insurance, focusing on coskewness and its impact on actuarial quantities.
This report aims to improve trust in AI by explaining machine learning models.
SLdisco uses supervised learning to discover causal models from observational data.
We determine how an individual can use life insurance to meet a bequest goal. We assume that the individual's consumption is met by an income, such as a pension, life annuity, or Social Security. Then, we consider the wealth that the individual wants to devote towards heirs (separate from any wealth related to the afor…
The paper revisits and applies FTAP to life insurance and annuities pricing.
GRETEL unifies GCE evaluation across various settings.
The study examines how different interpolation methods affect the decomposition of life insurance surplus.
Study efficient resource allocation for detecting extreme values.
One of the fundamental tasks of science is to find explainable relationships between observed phenomena. One approach to this task that has received attention in recent years is based on probabilistic graphical modelling with sparsity constraints on model structures. In this paper, we describe two new approaches to Bay…
Novel approach models life events using causal discovery and survival analysis.
Bayesian MS-VAR model for pricing equity-linked life insurance products.
In this paper, we study a stochastic optimal control problem with stochastic volatility. We prove the sufficient and necessary maximum principle for the proposed problem. Then we apply the results to solve an investment, consumption and life insurance problem with stochastic volatility, that is, we consider a wage earn…
Optimizes capital structure for life insurance companies with surplus participation.
Investigates optimal life insurance and annuity decisions in inflationary economies.
Two-dimensional transition rates improve life insurance reserve calculations.
Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the fiel…
Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs dat…
Proposes isotonic regression for calibrating Deep Cox models' survival probabilities.
Graph machine learning lacks a balanced theory, focusing on expressive power and optimization.
Study large deviations in life insurance portfolios without identical distributions.
This paper explores how machine learning can improve life insurance risk assessment.
BAxUS optimizes high-dimensional functions adaptively, avoiding performance degradation and failure.
LIFE framework improves model accuracy and interpretability.
MVRSM optimizes expensive functions with mixed variables, outperforming state-of-the-art methods.
Disagreement is an essential element of science and life in general. The language of probabilities and statistics is often used to describe disagreements quantitatively. In practice, however, we want much more than that. We want disagreements to be resolved. This leaves us with a substantial knowledge gap which is ofte…
Factor analysis provides linear factors that describe relationships between individual variables of a data set. We extend this classical formulation into linear factors that describe relationships between groups of variables, where each group represents either a set of related variables or a data set. The model also na…
Investigates timing and asset allocation for life insurance in uncertain financial planning.
New formulas estimate life insurance benefits with less computation.
Persistence diagrams, the most common descriptors of Topological Data Analysis, encode topological properties of data and have already proved pivotal in many different applications of data science. However, since the (metric) space of persistence diagrams is not Hilbert, they end up being difficult inputs for most Mach…
We determine the optimal strategies for purchasing term life insurance and for investing in a risky financial market in order to maximize the probability of reaching a bequest goal while consuming from an investment account. We extend Bayraktar and Young (2015) by allowing the individual to purchase term life insurance…