arXiv research

Study Type $C$ skein modules using $Sp(2n)$ webs and construct transparent elements.

Study $G_2$ skein algebra elements using Kuperberg webs and threading operations.

Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that …

A recent paper of Arnold, Falk, and Winther [Bull AMS, 47 (2010)] showed that a large class of mixed finite element methods can be formulated naturally on Hilbert complexes, where using a Galerkin-like approach, one solves a variational problem on a finite-dimensional subcomplex. In a seemingly unrelated research direc…

Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and di…

Machine-learning of atomic-scale properties amounts to extracting correlations between structure, composition and the quantity that one wants to predict. Representing the input structure in a way that best reflects such correlations makes it possible to improve the accuracy of the model for a given amount of reference …

Corporate transparency reduces investors' disposition effect by increasing confidence in holding profitable and losing stocks.

Kernelized PCovR reveals structure-property relations in chemistry and materials.

Bringing transparency to black-box decision making systems (DMS) has been a topic of increasing research interest in recent years. Traditional active and passive approaches to make these systems transparent are often limited by scalability and/or feasibility issues. In this paper, we propose a new notion of black-box D…

Let $M$ be a closed orientable surface of negative curvature. A connection is said to be transparent if its parallel transport along closed geodesics is the identity. We describe all transparent SU(2)-connections and we show that they can be built up from suitable Bäcklund transformations.

Machine learning algorithms aim at minimizing the number of false decisions and increasing the accuracy of predictions. However, the high predictive power of advanced algorithms comes at the costs of transparency. State-of-the-art methods, such as neural networks and ensemble methods, often result in highly complex mod…

We use elementary methods to compute the L2-dimension of the eigenspaces of the Markov operator on the lamplighter group and of generalizations of this operator on other groups. In particular, we give a transparent explanation of the spectral measure of the Markov operator on the lamplighter group found by Grigorchuk-Z…

The paper addresses monotonicity in machine learning models for fairness and accountability.

The aim of this research is to give a simple framework to evaluate/quantize the "transparency" of a firm. We assume that the process of the firm value is only observable once in a while but is strongly correlated with the stock price which is observable and tradable. This hybrid type structure make the transparency "ob…

Enhances machine learning performance predictions with transparency.

New models improve machine learning accuracy and transparency in finance.

This paper explores using NFTs for patents, offering a framework and addressing challenges.

Paper tackles transparency and auditability of machine learning in credit scoring.

Hides the complexity of neural networks, making them more transparent.

Let $(M,g)$ be a closed oriented negatively curved surface. A unitary connection on a Hermitian vector bundle over $M$ is said to be transparent if its parallel transport along the closed geodesics of $g$ is the identity. We study the space of such connections modulo gauge and we prove a classification result in terms …

We present the "Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles" (ABOUT ML) project as an initiative to operationalize ML transparency and work towards a standard ML documentation practice. We make the case for the project's relevance and effectiveness in consolidating dispa…

By Torelli topology the author understands aspects of the topology of surfaces (potentially) relevant to the study of Torelli groups. The present paper is devoted to a new approach to the results of W. Vautaw about Dehn multi-twists in Torelli groups and abelian subgroups of Torelli groups. The new proofs are more tran…

Develops transparent global models consistent with local explanations.

Autonomous AI systems will be entering human society in the near future to provide services and work alongside humans. For those systems to be accepted and trusted, the users should be able to understand the reasoning process of the system, i.e. the system should be transparent. System transparency enables humans to fo…

Stablecoin liquidity was affected by the SVB collapse, with USDC's transparency leading to market reactions.

Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems, e.g., image classification, natural language processing or human action recognition. Although these methods perform impressively well, they have a significant disadvantage, the lack of transpar…

AutoML enhances credit decisions with XAI for better transparency.

Interpole learns transparent decision-making policies from data.

Paper uses deep learning to make predictions transparently.

Develops a transparent surrogate model for complex data.

New method learns interpretable concepts from user feedback for high-dimensional data.

This paper emphasizes model transparency and interpretation in insurance.

AI agents improve forecast combination but require transparency.

AIMM-X monitors markets for suspicious behavior using transparent scoring.

Coding collaborations link crypto returns, revealing systemic transparency.

In 2018, at the World Economic Forum in Davos it was presented a new countries' economic performance metric named the Inclusive Development Index (IDI) composed of 12 indicators. The new metric implies that countries might need to realize structural reforms for improving both economic expansion and social inclusion per…

The world of cryptocurrency is not transparent enough though it was established for innate transparent tracking of capital flows. The most contributing factor is the violation of securities laws and scam in Initial Coin Offering (ICO) which is used to raise capital through crowdfunding. There is a lack of proper regula…

Let $M$ be a closed orientable Riemannian surface. Consider an SO(3)-connection $A$ and a Higgs field $Φ:M\to so(3)$. The pair $(A,Φ)$ naturally induces a cocycle over the geodesic flow of $M$. We classify (up to gauge transformations) cohomologically trivial pairs $(A,Φ)$ with finite Fourier series in terms of a suita…

As artificial intelligence plays an increasingly important role in our society, there are ethical and moral obligations for both businesses and researchers to ensure that their machine learning models are designed, deployed, and maintained responsibly. These models need to be rigorously audited for fairness, robustness…

Proposes PRMs for interpreting financial risk concept drift.

A new approach clusters data first, then embeds each cluster, improving transparency.

TRUST improves tree models' accuracy while maintaining interpretability.

New framework replicates private equity performance using AI and liquid strategies.

survex explains machine learning survival models, improving model transparency.

This study improves mid-cap equity performance with a data-driven, market-neutral approach.

Recent studies have shown that information disclosed on social network sites (such as Facebook) can be used to predict personal characteristics with surprisingly high accuracy. In this paper we examine a method to give online users transparency into why certain inferences are made about them by statistical models, and …

We provide a new approach to training neural models to exhibit transparency in a well-defined, functional manner. Our approach naturally operates over structured data and tailors the predictor, functionally, towards a chosen family of (local) witnesses. The estimation problem is setup as a co-operative game between an …

Hybrid model uses LLM to build transparent Bayesian networks for trading decisions.