Paper proposes MDER model to extract ML methods and datasets from papers.
problem Lack of focus on methods and datasets in literature analysis.
method Proposes MDER model for entity recognition of methods and datasets.
result Demonstrates preliminary performance of MDER model.
Algorithm improves SLR efficiency in financial narratives.
problem Fragmented understanding of financial narratives.
method NLP, clustering, interpretability tools.
result Unified narrative modeling approaches needed.
Survey analyzes economic research on cryptocurrencies using hybrid methods.
problem Lack of comprehensive literature review in cryptocurrencies economic research.
method Dual analysis combining bibliometric and close literature review.
result Updated state of cryptocurrency economic research literature.
LR-Robot automates SLRs with AI, expert oversight, and multidimensional analysis.
problem Efficient but contextually limited outputs from existing SLR frameworks.
method Human-in-the-loop process, structured knowledge sources, retrieval-augmented generation.
result Empirical demonstration of AI-driven literature synthesis in option pricing.
Study analyzes OR/MS literature using topic models.
problem Understanding the content and evolution of OR/MS literature.
method Developed a topic model using Latent Dirichlet Allocation (LDA).
result Identified temporal dynamics and unique scopes of journals.
This research uses BERT for financial sentiment analysis and LSTM for stock return prediction.
problem Traditional sentiment analysis in finance is limited; this research aims to improve it.
method BERT for sentiment analysis and LSTM for stock return prediction.
result Significant enhancement of BERT in financial sentiment analysis and improved stock return predictability.
Adaptive PCA algorithms for changing environments.
problem Static adversarial regret is not suitable for changing environments.
method Online adaptive algorithms for PCA and variance minimization with sub-linear adaptive regret guarantees.
result The proposed algorithms adapt to changing environments.
TDA improves cryptocurrency portfolio management.
problem Traditional methods fail to manage cryptocurrencies effectively.
method Topological Data Analysis (TDA) for identifying investment opportunities.
result TDA-based portfolio management outperforms traditional methods.
Paper proves existence of Lévy term structure models.
problem Existence proof for Lévy term structure models.
method Proof of existence and uniqueness for Heath-Jarrow-Morton type equation.
result Full proof of existence and uniqueness of Lévy term structure models.
This review assesses deep-learning methods for complex sequential data.
problem Lack of robustness and transparency in deep-learning frameworks for irregular sequential data.
method Systematic literature review of existing algorithms.
result Recurrent neural networks dominate in performance evaluation of deep-learning frameworks.
This study reviews text-based stock market analysis methods.
problem Insufficient analysis of unstructured textual data in stock market predictions.
method Reviews existing literature, covers data types, representation techniques, and analysis methods.
result Identifies open problems and suggests future research directions.
An analysis of the concept of orientation used in electrodynamics is presented. At least two different versions are encountered in the literature. Both are clearly identified and comparisons are made.
ACA identifies and explains anomalies in data.
problem Explaining anomalies in non-supervised data analysis.
method Abnormal Component Analysis (ACA) using data depth.
result ACA provides a linear explanation for anomalies.
This study compares 16 embedding-based link prediction methods for knowledge graphs.
problem KG incompleteness and missing facts prediction.
method Embedding-based link prediction methods compared.
result Effective and efficient comparison of 16 embedding-based LP methods.
Study on spinor field equation on spheres, focusing on blow-up analysis.
problem Spinorial Yamabe problem on spheres.
method Variational methods, blow-up analysis.
result Blow-up profile for the spinorial Yamabe type equation on Sm. New method improves conditional covariance estimation using targeted groups of assets.
problem Improving conditional covariance estimation in financial time series.
method Introduces targeting in BEKK and DCC models for financial time series analysis.
result Encouraging results from empirical case study, especially with fewer assets.
Analyzes deep neural networks training errors with SGD and random init.
problem Lack of rigorous understanding of deep learning algorithms.
method Mathematical analysis of deep learning with SGD and random init.
result First full error analysis for deep learning with SGD and random init.
The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording, and analyzing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods ha…
This paper reviews digital transformation research from 2011-2024, focusing on corporate finance.
problem Lack of systematic review in digital transformation from corporate finance perspective.
method Combines bibliometric and content analysis methods.
result Emerging and rapidly growing focus on digital transformation, particularly in developed countries.
Survey of network analysis limits and optimal methods.
problem Graphon estimation, community detection, and hypothesis testing.
method Review of minimax optimal rates and procedures.
result Optimal algorithms for network analysis.
PCR robust to noisy, missing, and mixed-valued covariates.
problem Handling noisy, missing, and mixed-valued covariates in PCR.
method PCR is equivalent to HSVT pre-processing; establishes robustness and finite-sample analysis.
result PCR robust to noise, equivalent to RSC, and can learn good predictive models.
This paper improves PPCA robustness using t-distributions.
problem Improving robustness of probabilistic PCA.
method Using multivariate t-distributions and a hierarchical model. result Clarified the correct correspondence between the multivariate t-PPCA framework and the hierarchical model. We analyze conditional optimization problems arising in discrete time Principal-Agent problems of delegated portfolio optimization with linear contracts. Applying tools from Conditional Analysis we show that some results known in the literature for very specific instances of the problem carry over to translation invari…
This note introduces a duality principle for nonlinear equations.
problem Maximum principles at infinity for nonlinear equations.
method Ahlfors property and Khas'minskii potentials.
result Unified framework for various maximum principles.
This paper contributes to the literature on international stock market comovements and contagion. The novelty of our approach lies in application of wavelet tools to high-frequency financial market data, which allows us to understand the relationship between stock markets in a time-frequency domain. While major part of…
Develops rMultiNet R package for multilayer network analysis.
problem Analyzing multilayer network data.
method Two frameworks: MMSBM and MMLSM for generating multilayer networks; methods for node and layer embedding; clustering.
result Provides tools for multilayer network analysis.
Paper proposes a new method for exact recovery in robust tensor principal component analysis.
problem Exact recovery of low-rank and sparse components in tensors.
method Proposes a new method based on tensor-tensor product and t-SVD to solve a convex optimization problem.
result Exact recovery achieved in a deterministic fashion without randomness assumptions.
The paper addresses evaluating survival predictions using discrimination measures, finding a robust method to convert distributions to risks.
problem Evaluating survival distribution predictions with discrimination measures is challenging and often leads to unfair comparisons.
method The paper surveys existing methods and recommends summing over the predicted cumulative hazard as the most robust method to convert distributions to risks.
result Summing over the predicted cumulative hazard is the most robust method to convert distribution predictions to risk predictions.
The paper tackles noisy functional data by exploring a multivariate perspective.
problem Noisy functional data measured on discrete points.
method Factor model and principal components analysis.
result The latent signal can be accurately estimated using multivariate methods.
This paper finds that realized kurtosis predicts stock variance better than realized skewness for daily returns.
problem The explanatory power of realized skewness for daily stock returns is limited.
method An extensive empirical analysis of realized skewness and realized kurtosis on daily stock returns and variance.
result Realized kurtosis shows significant forecasting power for stock variance, while realized skewness is less effective for daily returns.
Survey categorizes time series anomaly detection methods.
problem Need for anomaly detection in time series data.
method Process-centric taxonomy of anomaly detection methods.
result Meta-analysis of time series anomaly detection trends.
This paper reviews spatial and spatiotemporal volatility models.
problem Capturing spatial dependence in volatility of spatial and spatiotemporal data.
method Review of time series volatility models and their extensions.
result Comparison and practical recommendations for spatial and spatiotemporal volatility models.
Motivated by the supersymmetric extension of Liouville theory in the recent physics literature, we couple the standard Liouville functional with a spinor field term. The resulting functional is conformally invariant. We study geometric and analytic aspects of the resulting Euler-Lagrange equations, culminating in a blo…
We study learning latent models with multi-instance weak supervision.
problem Learning latent models with multi-instance weak supervision.
method Formulated as multi-instance Partial Label Learning (multi-instance PLL), proposed a necessary and sufficient condition for learnability, derived Rademacher-style error bounds.
result First theoretical study of multi-instance PLL with unknown transition function, aligns with empirical results but highlights scalability issues.
New algorithm achieves near optimal sample complexity for 1-identification problem.
problem Determining if an arm's mean reward is at least a known threshold with high probability.
method Design of Sequential-Exploration-Exploitation (SEE) algorithm with non-asymptotic analysis.
result Achieves near optimality in sample complexity, matching upper and lower bounds up to a polynomial logarithmic factor.
New framework tackles high-dimensional reliability analysis using surrogate models and active subspaces.
problem High computational cost and curse of dimensionality in reliability analysis of high-dimensional systems.
method Sparse Active Subspace (SAS) algorithm for identifying low-dimensional manifolds and constructing efficient surrogate models.
result Proposed framework significantly improves accuracy and efficiency of reliability analysis compared to existing methods.
Study of common financial data patterns across stocks.
problem Understanding common patterns in financial data.
method Analysis of stock price data from multiple exchanges.
result Identification of various stylized empirical facts in financial data.
Paper analyzes patterns in African exports using network analysis.
problem Limited data on African countries in trade networks.
method Defined two bipartite networks and their minimal spanning trees.
result Emerging patterns reveal important characteristics of African exports.
The paper reviews trajectory data applications for road transportation agencies.
problem Lack of understanding of trajectory data value for transportation agencies.
method Literature review and case study in Maryland.
result Illustrates applications of trajectory data in six areas of road transportation.
The econophysics approach to socio-economic systems is based on the assumption of their complexity. Such assumption inevitably lead to another assumption, namely that underlying interconnections within socio-economic systems, particularly financial markets, are nonlinear, which is shown to be true even in mainstream ec…
Although stochastic approximation learning methods have been widely used in the machine learning literature for over 50 years, formal theoretical analyses of specific machine learning algorithms are less common because stochastic approximation theorems typically possess assumptions which are difficult to communicate an…
Sequential coordinate ascent is more robust in high-dimensional linear regression.
problem Behavior difference between sequential and parallel coordinate ascent in variational inference.
method Comparison of sequential and parallel coordinate ascent algorithms in high-dimensional linear regression.
result Sequential algorithm converges under more relaxed conditions than parallel algorithm.
Model predicts chemical reactions from patent data.
problem Predicting chemical reactions from patent data.
method Neural sequence-to-sequence model trained on 50,000 reactions.
result Model performs comparably with expert systems and overcomes their limitations.
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…
The question of the optimality of Thompson Sampling for solving the stochastic multi-armed bandit problem had been open since 1933. In this paper we answer it positively for the case of Bernoulli rewards by providing the first finite-time analysis that matches the asymptotic rate given in the Lai and Robbins lower boun…
Visuals in scientific papers are used to express complex ideas; this study uses them to identify knowledge domains.
problem Scientific figures are underutilized in literature analysis.
method Encoded scientific figures into visual signatures and used distances between signatures to compare communities of practice.
result Figures can differentiate knowledge domains as effectively as text or citation patterns.
This study analyzes data science vocabulary changes over 13 years.
problem Understanding evolution of data science terms over time.
method Exploratory Data Analysis, Latent Semantic Analysis, Latent Dirichlet Analysis, N-grams Analysis.
result Identified new vocabulary and its incorporation into scientific literature.
Study cosmic structures using Topological Data Analysis and Persistence Energy.
problem Investigate cosmic web evolution in ΛCDM cosmologies. method Apply LITE method to embed persistence diagrams into vector spaces and analyze cosmic structures.
result Discover a correlation between Persistence Energy and redshift values.