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.
Scoping review finds EEG key in MCI research, identifying ERP/EEG, QEEG, and machine learning.
Scoping review of EO-ML methods for causal inference in poverty geography.
Scoping review and benchmarking of synthetic EHR data generation methods.
This review explores ChatGPT in accounting and finance.
SaML guides ML models to avoid survey biases.
An increase in the use of smartphones has laid to the use of the internet and social media platforms. The most commonly used social media platforms are Twitter, Facebook, WhatsApp and Instagram. People are sharing their personal experiences, reviews, feedbacks on the web. The information which is available on the web i…
Following a review of metric, ultrametric and generalized ultrametric, we review their application in data analysis. We show how they allow us to explore both geometry and topology of information, starting with measured data. Some themes are then developed based on the use of metric, ultrametric and generalized ultrame…
Machine learning boosts physics research, especially at high energy experiments.
Negative dependence improves machine learning performance.
This survey introduces synthetic timelike Ricci curvature bounds in Lorentzian spaces.
Deep learning enhances financial asset management through new models and data sources.
The paper reviews recent statistical methods for financial markets, focusing on jumps, volatility, and microstructure noise.
Survey on uncertainty in ML and DL, covering sources, quantification, and decision-making.
Tool uses text mining to define innovative tech fields from abstracts.
Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews the main challenges in sampling from the parameter posterior of a neural network via MCMC. Such challenges culminate to lack of convergence to the parameter posterior. Nevertheless, thi…
The increased adoption of Electronic Health Records(EHRs) has brought changes to the way the patient care is carried out. The rich heterogeneous and temporal data space stored in EHRs can be leveraged by machine learning models to capture the underlying information and make clinically relevant predictions. This can be …
Many machine learning models, such as logistic regression~(LR) and support vector machine~(SVM), can be formulated as composite optimization problems. Recently, many distributed stochastic optimization~(DSO) methods have been proposed to solve the large-scale composite optimization problems, which have shown better per…
Unified geometry for relativity and beyond.
Review of methods enabling causal predictions under hypothetical interventions.
SCOPE iteratively optimizes sparsity-constrained problems without tuning hyperparameters.
Convolutional neural network improves assertion detection in multi-label clinical text.
Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a sco…
A standard model of (conditional) heteroscedasticity, i.e., the phenomenon that the variance of a process changes over time, is the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, which is especially important for economics and finance. GARCH models are typically estimated by the Quasi-Maximum …
SCOPE-FE improves feature engineering efficiency for high-dimensional datasets.
Study identifies methods for handling sparse data in web settings with contextual bandits.
This work reviews and tests risk allocation strategies in finance, highlighting Shapley allocation's advantages.
Study finds carbon emissions affect stock value, but not bought emissions.
Two new estimators reduce costs and improve accuracy for EHR outcome prediction.
We propose a method for estimation in high-dimensional linear models with nominal categorical data. Our estimator, called SCOPE, fuses levels together by making their corresponding coefficients exactly equal. This is achieved using the minimax concave penalty on differences between the order statistics of the coefficie…
Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates---active learning. However, standard \emph{global-scope error} metrics for model quality are not predictive of discovery performance, and can be misleading. We introduce the notion of \emph{Pareto shell-…
The coeffective differential complex on a symplectic manifold is extended both in length and in scope, unifying the constructions of various other authors.
New method smooths optimization for sparse regularization.
Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to (i) very restricted model classes where exact or approximate probabilistic inference were feasible, and (ii) small or medium-sized data sets which fit …
Study reduces emissions in portfolios with error-prone emissions data.
Surrogate models help predict complex systems with less computational cost.
Important advances have been made using convolutional neural network (CNN) approaches to solve complicated problems in areas that rely on grid structured data such as image processing and object classification. Recently, research on graph convolutional neural networks (GCNN) has increased dramatically as researchers tr…
Clarifies the scope of 'reproducibility' in AI and ML.
The small-ball method was introduced as a way of obtaining a high probability, isomorphic lower bound on the quadratic empirical process, under weak assumptions on the indexing class. The key assumption was that class members satisfy a uniform small-ball estimate: that for given const…
In probabilistic approaches to classification and information extraction, one typically builds a statistical model of words under the assumption that future data will exhibit the same regularities as the training data. In many data sets, however, there are scope-limited features whose predictive power is only applicabl…
Defines Learning Analytics' foundational structure and scope.
SCOPE estimator improves covariance and precision matrix estimation.
Experiment shows author rankings can improve peer review scores.
Our study employs sentiment analysis to evaluate the compatibility of Amazon.com reviews with their corresponding ratings. Sentiment analysis is the task of identifying and classifying the sentiment expressed in a piece of text as being positive or negative. On e-commerce websites such as Amazon.com, consumers can subm…
Online reviews provided by consumers are a valuable asset for e-Commerce platforms, influencing potential consumers in making purchasing decisions. However, these reviews are of varying quality, with the useful ones buried deep within a heap of non-informative reviews. In this work, we attempt to automatically identify…
Peer review is the foundation of scientific publication, and the task of reviewing has long been seen as a cornerstone of professional service. However, the massive growth in the field of machine learning has put this community benefit under stress, threatening both the sustainability of an effective review process and…
Mathematical general relativity reviewed.
Neural Information Processing Systems (NIPS) is a top-tier annual conference in machine learning. The 2016 edition of the conference comprised more than 2,400 paper submissions, 3,000 reviewers, and 8,000 attendees. This represents a growth of nearly 40% in terms of submissions, 96% in terms of reviewers, and over 100%…
Deep learning complements OR/MS for decision-making under uncertainty.