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.
Hierarchical NMF organizes COVID-19 literature into a searchable tree.
Neural networks enhance relation extraction from biomedical literature.
This paper gives a critical account of the minority game literature. The minority game is a simple congestion game: players need to choose between two options, and those who have selected the option chosen by the minority win. The learning model proposed in this literature seems to differ markedly from the learning mod…
LR-Robot automates SLRs with AI, expert oversight, and multidimensional analysis.
Algorithm improves SLR efficiency in financial narratives.
Survey analyzes economic research on cryptocurrencies using hybrid methods.
Discuss ML methods for economists, highlighting better performance in econometrics.
Automated synthesis planning from scientific literature using AI.
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…
Realized moments of higher order computed from intraday returns are introduced in recent years. The literature indicates that realized skewness is an important factor in explaining future asset returns. However, the literature mainly focuses on the whole market and on the monthly or weekly scale. In this paper, we cond…
Paper proposes MDER model to extract ML methods and datasets from papers.
Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domai…
Machine learning algorithms for prediction are increasingly being used in critical decisions affecting human lives. Various fairness formalizations, with no firm consensus yet, are employed to prevent such algorithms from systematically discriminating against people based on certain attributes protected by law. The aim…
New model for fair clustering ensures balanced representation of protected attributes.
Two approaches to directly estimating Riesz representer are shown to be numerically equivalent under certain conditions.
Paper proves existence of Lévy term structure models.
Researchers introduce datasets for cursive Japanese to ML community.
Visuals in scientific papers are used to express complex ideas; this study uses them to identify knowledge domains.
Neural networks reviewed for option pricing and hedging.
The variational framework for learning inducing variables (Titsias, 2009a) has had a large impact on the Gaussian process literature. The framework may be interpreted as minimizing a rigorously defined Kullback-Leibler divergence between the approximating and posterior processes. To our knowledge this connection has th…
The paper critiques current time series classification evaluation methods.
This review assesses deep-learning methods for complex sequential data.
This paper reviews incompatibilities of comonotonic risk measures.
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…
Normalizing Flows model tractable distributions for efficient sampling and evaluation.
We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature. By taking advantage of transfer learning, we are able to efficiently use different data sources that are related to the sam…
We extend Relative Robust Portfolio Optimisation models to allow portfolios to optimise their distance to a set of benchmarks. Portfolio managers are also given the option of computing regret in a way which is more in line with market practices than other approaches suggested in the literature. In addition, they are gi…
In this paper, we propose a two-sector Markovian infectious model, which is an extension of Greenwood's model. The central idea of this model is that the causality of defaults of two sectors is in both direction, which enrich dependence dynamics. The Bayesian Information Criterion is adopted to compare the proposed mod…
We study a market model in which the volatility of the stock may jump at a random time from a fixed value to another fixed value. This model was already described in the literature. We present a new approach to the problem, based on partial derivative equations, which gives a different perspective to the problem. Withi…
In literature there are several studies on the performance of Bayesian network structure learning algorithms. The focus of these studies is almost always the heuristics the learning algorithms are based on, i.e. the maximisation algorithms (in score-based algorithms) or the techniques for learning the dependencies of e…
We present algorithms for the detection of a class of heart arrhythmias with the goal of eventual adoption by practicing cardiologists. In clinical practice, detection is based on a small number of meaningful features extracted from the heartbeat cycle. However, techniques proposed in the literature use high dimensiona…
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…
LR-Robot accelerates SLRs by combining expert oversight and AI, revealing trends and patterns in financial research.
Paper addresses fairness issues in error-prone outcomes.
We prove that "generalized Lie algebroid", a geometric object which appeared recently in the literature, is a misconception.
We study properties of Cartesian products of digital images, using a variety of adjacencies that have appeared in the literature.
Both theoretical and applied economics have a great deal to say about many aspects of the firm, but the literature on the extinctions, or demises, of firms is very sparse. We use a publicly available data base covering some 6 million firms in the US and show that the underlying statistical distribution which characteri…
Efficient inference method for adaptive experiments with tighter confidence sequences.
Survey of trainable activation functions in neural networks.
Sequence learning improves query expansion in information retrieval.
We show the equivalence of several definitions of compact infra-solvmanifolds that appear in various math literatures.
Paper reviews intrinsic motivations and their role in open-ended learning.
Paper explores grafting consistent estimators to improve Random Forest consistency.
The conventional wisdom of mean-variance (MV) portfolio theory asserts that the nature of the relationship between risk and diversification is a decreasing asymptotic function, with the asymptote approximating the level of portfolio systematic risk or undiversifiable risk. This literature assumes that investors hold an…
Unsupervised method constructs knowledge graph from text and code.
Background. Drug-drug interaction (DDI) is a major cause of morbidity and mortality. [...] Biomedical literature mining can aid DDI research by extracting relevant DDI signals from either the published literature or large clinical databases. However, though drug interaction is an ideal area for translational research, …
This review introduces graph kernels for chemoinformatics.
This survey outlines methods to ensure fairness in machine learning.