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.
Paper improves volatility forecasting for new issues and spin-offs.
Proposes a new approach to regression learning that addresses overfitting and underfitting.
As more researchers have become aware of and passionate about algorithmic fairness, there has been an explosion in papers laying out new metrics, suggesting algorithms to address issues, and calling attention to issues in existing applications of machine learning. This research has greatly expanded our understanding of…
New framework tackles deep learning issues like local traps and miscalibration.
The adaptive moment estimation algorithm Adam (Kingma and Ba) is a popular optimizer in the training of deep neural networks. However, Reddi et al. have recently shown that the convergence proof of Adam is problematic and proposed a variant of Adam called AMSGrad as a fix. In this paper, we show that the convergence pr…
New active learning method uses combinatorial coverage to improve data transfer and reduce bias.
New issue found in value-based reinforcement learning for stochastic environments.
A new graph neural network tackles oversmoothing and generalization issues.
Notions of "fair classification" that have arisen in computer science generally revolve around equalizing certain statistics across protected groups. This approach has been criticized as ignoring societal issues, including how errors can hurt certain groups disproportionately. We pose a modification of one of the fairn…
PCA is a classical statistical technique whose simplicity and maturity has seen it find widespread use as an anomaly detection technique. However, it is limited in this regard by being sensitive to gross perturbations of the input, and by seeking a linear subspace that captures normal behaviour. The first issue has bee…
Green bond leaks impact equity markets, altering investor reactions.
GraphFL tackles semi-supervised node classification on graphs using federated learning.
New GAN formulation addresses mode collapse issue.
New method enhances hotspot prediction in IC designs.
Although there are millions of transgender people in the world, a lack of information exists about their health issues. This issue has consequences for the medical field, which only has a nascent understanding of how to identify and meet this population's health-related needs. Social media sites like Twitter provide ne…
New deep learning method preserves orientation in shape matching.
Federated learning poses new statistical and systems challenges in training machine learning models over distributed networks of devices. In this work, we show that multi-task learning is naturally suited to handle the statistical challenges of this setting, and propose a novel systems-aware optimization method, MOCHA,…
Based on graphic lambda calculus, we propose a program for a new model of asynchronous distributed computing, inspired from Hewitt Actor Model, as well as several investigation paths, concerning how one may graft lambda calculus and knot diagrammatics.
A new method estimates the number of clusters on spherical data.
We address feature interpretation and reproducibility issues in dense nets, proposing a modified loss function.
How to model and encode the semantics of human-written text and select the type of neural network to process it are not settled issues in sentiment analysis. Accuracy and transferability are critical issues in machine learning in general. These properties are closely related to the loss estimates for the trained model.…
In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages t…
Learning from many real-world datasets is limited by a problem called the class imbalance problem. A dataset is imbalanced when one class (the majority class) has significantly more samples than the other class (the minority class). Such datasets cause typical machine learning algorithms to perform poorly on the classi…
Every year at the United Nations, member states deliver statements during the General Debate discussing major issues in world politics. These speeches provide invaluable information on governments' perspectives and preferences on a wide range of issues, but have largely been overlooked in the study of international pol…
Graph Neural Networks (GNNs) have been emerging as a promising method for relational representation including recommender systems. However, various challenging issues of social graphs hinder the practical usage of GNNs for social recommendation, such as their complex noisy connections and high heterogeneity. The oversm…
The implementation of the Own Risk and Solvency Assessment is a critical issue raised by Pillar II of Solvency II framework. In particular the Overall Solvency Needs calculation left the Insurance companies to define an optimal entity-specific solvency constraint on a multi-year time horizon. In a life insurance societ…
Machine learning improves RNA secondary structure prediction.
New method offsets DML's error-compounding issue and provides more stable causal parameter estimates.
A new differentiable divergence for time series comparison.
New method compresses large sample data for faster discriminant analysis.
The paper proves existence and growth estimates for inverse mean curvature flow and related -Laplacian Green kernel decay.
This paper surveys recent progress on issues related to the Bartnik quasi-local mass . In addition, we formulate a number of new problems and conjectures regarding foundational properties of the mass . This work is dedicated with pleasure to Robert Bartnik in honor of his 60th birthday.
New approach to deeper graph neural networks to avoid performance degradation.
This study analyzes public debts and deficits between European countries. The statistical evidence here seems in general to reveal that sovereign debts and government deficits of countries within European Monetary Unification-in average- are getting worse than countries outside European Monetary Unification, in particu…
Proposes ESCFR to estimate treatment effects from biased data.
We use bifurcation theory to determine the existence of infinitely many new examples of triply periodic minimal surfaces in . These new examples form branches issuing from the H-family, the rPD-family, the tP-family, and the tD-family, that converge to some degenerate embedding of the families. As to nonde…
New approach to optimal income tax theory tackles inequity issues.
New method to handle credit portfolio model uncertainties.
A new framework ensures model safety by retaining old model capabilities while improving new tasks.
We develop the first basic Operational Risk perspective on key risk management issues associated with the development of new forms of electronic currency in the real economy. In particular, we focus on understanding the development of new risks types and the evolution of current risk types as new components of financia…
This foreword discusses the contributions of Bolyai, Gauss, and Lobachevsky to non-Euclidean geometry.
Investigates numerical issues in GP interpolation parameter estimation.
This paper tackles ranking-based performance normalization for optimization algorithms.
New method extracts aleatoric and epistemic uncertainties from regression-based neural networks.
New method tackles convergence issues in approximating FBSDEs.
Score-based methods fail with isolated components and incorrect mixing proportions.
New results on splitting tangles and spatial graphs.
learn2learn simplifies meta-learning research by providing a library and standardized interfaces.