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.
PRESTO maps latent representations across diverse ML models.
Multiverse analysis helps prevent fairness hacking and evaluate model design decisions.
Study a simplified model of multiverse structure with synchronized timelines.
This is a survey of several approaches to the framework for working with infinitesimals and infinite numbers, originally developed by Abraham Robinson in the 1960s, and their constructive engagement with the Cantor-Dedekind postulate and the Intended Interpretation hypothesis. We highlight some applications including (…
C-index varies among software, complicating model comparison.
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programming. By considering the structure of the counterfactual query, one can significantly optimise the inference process. We also consider design c…
This paper defines the pressure metric on the Moduli space of Margulis spacetimes without cusps and shows that it is positive definite on the constant entropy sections. It also demonstrates an identity regarding the variation of the cross-ratios.
In this article we construct the pressure form on the moduli space of higher dimensional Margulis spacetimes without cusps and study its properties. We show that the Margulis spacetimes are infinitesimally determined by their marked Margulis invariant spectrums. We use it to show that the restrictions of the pressure f…
Recent state-of-the-art language models utilize a two-phase training procedure comprised of (i) unsupervised pre-training on unlabeled text, and (ii) fine-tuning for a specific supervised task. More recently, many studies have been focused on trying to improve these models by enhancing the pre-training phase, either vi…
RE enhances DL by learning model behavior, enabling iterative self-improvement.
Efficient private matrix analysis algorithms for recent variants.
This paper introduces compositional data analysis for financial ratios, improving industry-level analysis.
In this dissertation, the main goal is visualisation of financial time series. We expect that visualisation of financial time series will be a useful auxiliary for technical analysis. Firstly, we review the technical analysis methods and test our trading rules, which are built by the essential concepts of technical ana…
Paper combines geometry and time-series analysis for spatiotemporal data.
In this paper the exact linear relation between the leading eigenvectors of the modularity matrix and the singular vectors of an uncentered data matrix is developed. Based on this analysis the concept of a modularity component is defined, and its properties are developed. It is shown that modularity component analysis …
This paper investigates to identify the requirement and the development of machine learning-based mobile big data analysis through discussing the insights of challenges in the mobile big data (MBD). Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the …
Interactive DR framework for comparing datasets.
Combines topological and geometric approaches to data analysis.
Proposes a multivariate regression model for better analysis of multiple datasets.
New method uses topological data analysis to study stock market crashes.
Analyzes stock trends and e-commerce user behavior using Twitter data.
Study analyzes Disney stock market performance using machine learning.
This study analyzes data science vocabulary changes over 13 years.
FinSphere improves stock analysis quality with AI and expert-curated data.
This study examines whether PCA can effectively identify nitrogen pollution sources in rivers.
Paper introduces probabilistic methods to approximate archetypal analysis, reducing complexity.
We present a unifying framework which reduces the construction of probabilistic component analysis techniques to a mere selection of the latent neighbourhood, thus providing an elegant and principled framework for creating novel component analysis models as well as constructing probabilistic equivalents of deterministi…
Deep learning techniques are rapidly advanced recently, and becoming a necessity component for widespread systems. However, the inference process of deep learning is black-box, and not very suitable to safety-critical systems which must exhibit high transparency. In this paper, to address this black-box limitation, we …
In this paper, we explore the effectiveness of dynamic analysis techniques for identifying malware, using Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs), both trained on sequences of API calls. We contrast our results to static analysis using HMMs trained on sequences of opcodes, and show that dyn…
Improved LDA method for better classification and dimensionality reduction.
CDVI improves variational inference for survival analysis by considering censoring mechanisms.
The kernel matrix used in kernel methods encodes all the information required for solving complex nonlinear problems defined on data representations in the input space using simple, but implicitly defined, solutions. Spectral analysis on the kernel matrix defines an explicit nonlinear mapping of the input data represen…
Prototypal analysis is introduced to overcome two shortcomings of archetypal analysis: its sensitivity to outliers and its non-locality, which reduces its applicability as a learning tool. Same as archetypal analysis, prototypal analysis finds prototypes through convex combination of the data points and approximates th…
In this paper, we introduce a Deep Convolutional Analysis Dictionary Model (DeepCAM) by learning convolutional dictionaries instead of unstructured dictionaries as in the case of deep analysis dictionary model introduced in the companion paper. Convolutional dictionaries are more suitable for processing high-dimensiona…
Improved analysis of UCBVI algorithm with better empirical performance.
Reduces survival analysis to common regression tasks.
A new robust scaling approach improves downstream metabolomics analysis.
Paper proposes FinAR-Bench to evaluate LLMs in financial analysis tasks.
Wavelet analysis reveals non-linear dynamics in cryptocurrency prices.
The book explores alternatives to worst-case analysis for algorithm performance.
Alternative wavelet analysis method for financial signals.
These are lecture notes for the course "Analysis and X-ray tomography". The course is a broad overview of various tools in analysis that can be used to study X-ray tomography. The focus is on tools and ideas, not so much on technical details and minimal assumptions. Only very basic functional analysis is assumed as bac…
We investigate the difference between using an penalty versus an constraint in generalized eigenvalue problems, such as principal component analysis and discriminant analysis. Our main finding is that an penalty may fail to provide very sparse solutions; a severe disadvantage for variable sel…
Can textual data be compressed intelligently without losing accuracy in evaluating sentiment? In this study, we propose a novel evolutionary compression algorithm, PARSEC (PARts-of-Speech for sEntiment Compression), which makes use of Parts-of-Speech tags to compress text in a way that sacrifices minimal classification…
This paper analyzes text in financial disclosures to improve financial analysis.
This paper deals with various topics in analysis on hyperbolic spaces. It surveys some recent progress in non-Euclidean Fourier Analysis and proves some new results for the geodesic Radon transform on hyperbolic spaces.
Study compares BERT with other sentiment analysis models.
Bayesian analysis reveals asymmetry in financial data.