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.
Algorithm improves SLR efficiency in financial narratives.
Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact. The goal of this survey article is to give a coherent and comprehensive review of the literature around the construction and use of Normalizing Flows for distribution lear…
This review assesses deep-learning methods for complex sequential data.
Survey analyzes economic research on cryptocurrencies using hybrid methods.
This paper reviews incompatibilities of comonotonic risk measures.
LR-Robot automates SLRs with AI, expert oversight, and multidimensional analysis.
This review introduces graph kernels for chemoinformatics.
This paper reviews digital transformation research from 2011-2024, focusing on corporate finance.
This paper reviews spatial and spatiotemporal volatility models.
This review examines deep learning in financial fraud detection over 5 years.
This is a review article for Encyclopedia of Complexity and System Science, to be published by Springer http://refworks.springer.com/complexity/. The paper reviews statistical models for money, wealth, and income distributions developed in the econophysics literature since late 1990s.
LR-Robot accelerates SLRs by combining expert oversight and AI, revealing trends and patterns in financial research.
Comprehensive review of robust portfolio selection models.
The article reviews how to set stochastic volatility model parameters.
Systematic review of ML explainability in process mining.
The rise of non-linear and interactive media such as video games has increased the need for automatic movement animation generation. In this survey, we review and analyze different aspects of building automatic movement generation systems using machine learning techniques and motion capture data. We cover topics such a…
This paper reviews bank performance determinants, highlighting future research areas.
Neural networks have been used as a nonparametric method for option pricing and hedging since the early 1990s. Far over a hundred papers have been published on this topic. This note intends to provide a comprehensive review. Papers are compared in terms of input features, output variables, benchmark models, performance…
This study analyzes EU ETS literature trends using bibliometric methods.
The literature on volatility modelling and option pricing is a large and diverse area due to its importance and applications. This paper provides a review of the most significant volatility models and option pricing methods, beginning with constant volatility models up to stochastic volatility. We also survey less comm…
Digital Financial Services continue to expand and replace the delivery of traditional banking services to the customers through innovative technologies to meet the growing complex needs and globalization challenges. These diversified digital products help the organizations (service providers) to improve their firm perf…
Recent advances in cryptography promise to enable secure statistical computation on encrypted data, whereby a limited set of operations can be carried out without the need to first decrypt. We review these homomorphic encryption schemes in a manner accessible to statisticians and machine learners, focusing on pertinent…
The paper presents a systematic review of state-of-the-art approaches to identify patient cohorts using electronic health records. It gives a comprehensive overview of the most commonly de-tected phenotypes and its underlying data sets. Special attention is given to preprocessing of in-put data and the different modeli…
This paper reviews early time series classification methods.
Transportation agencies have an opportunity to leverage increasingly-available trajectory datasets to improve their analyses and decision-making processes. However, this data is typically purchased from vendors, which means agencies must understand its potential benefits beforehand in order to properly assess its value…
This review analyzes recent advances in solving index tracking problems.
In this paper we tackle the issue of clustering trajectories of geolocalized observations. Using clustering technics based on the choice of a distance between the observations, we first provide a comprehensive review of the different distances used in the literature to compare trajectories. Then based on the limitation…
Abstract reviews algorithms for multi-index models, focusing on polynomial-time methods and their limitations.
There have been rapid developments in model-based clustering of graphs, also known as block modelling, over the last ten years or so. We review different approaches and extensions proposed for different aspects in this area, such as the type of the graph, the clustering approach, the inference approach, and whether the…
Deep learning models improve financial price forecasting accuracy.
Machine vision is critical to robotics due to a wide range of applications which rely on input from visual sensors such as autonomous mobile robots and smart production systems. To create the smart homes and systems of tomorrow, an overview about current challenges in the research field would be of use to identify furt…
Paper reviews intrinsic motivations and their role in open-ended learning.
Survey on uncertainty in ML and DL, covering sources, quantification, and decision-making.
Abstract reviews mathematical fairness in machine learning.
This study reviews text-based stock market analysis methods.
In this paper, we aimed at reviewing present literature on employing nonlinear analysis in combination with machine learning methods, in depression detection or prediction task. We are focusing on an affordable data-driven approach, applicable for everyday clinical practice, and in particular, those based on electroenc…
This review classifies electricity price models for risk management.
Automatic summarization of natural language is a current topic in computer science research and industry, studied for decades because of its usefulness across multiple domains. For example, summarization is necessary to create reviews such as this one. Research and applications have achieved some success in extractive …
Survey on statistical theories of neural networks, focusing on approximation, training dynamics, and generative models.
This review summarizes five Lasso optimization algorithms.
We review coupled -structures, also known in the literature as restricted half-flat structures, in relation to supersymmetry. In particular, we study special classes of examples admitting such structures and the behaviour of flows of -structures with respect to the coupled condition.
The paper reviews recent statistical methods for financial markets, focusing on jumps, volatility, and microstructure noise.
A neuroscience method to understanding the brain is to find and study the preferred stimuli that highly activate an individual cell or groups of cells. Recent advances in machine learning enable a family of methods to synthesize preferred stimuli that cause a neuron in an artificial or biological brain to fire strongly…
Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies h…
The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical inference method in applied work. However, MCMC algorithms tend to be computationally demanding, and are particularly slow for large datasets…
MPNNs generalize poorly, study reviews current research.
Pattern analysis often requires a pre-processing stage for extracting or selecting features in order to help the classification, prediction, or clustering stage discriminate or represent the data in a better way. The reason for this requirement is that the raw data are complex and difficult to process without extractin…
This review analyzes deep learning methods for electricity price forecasting across different markets.