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.
The paper introduces Absolute Shapley Value to handle negative contributions in machine learning model training.
Investigates chaotic financial time series with monthly contributions and devaluation.
Lower bound on minimum vertex degree for non-negative Lin-Lu-Yau curvature on graphs.
Social media systems rely on user feedback and rating mechanisms for personalization, ranking, and content filtering. However, when users evaluate content contributed by fellow users (e.g., by liking a post or voting on a comment), these evaluations create complex social feedback effects. This paper investigates how ra…
Study on 4-manifolds restricts negatively curved metrics and fundamental groups.
False negatives hinder adversarial imitation learning, leading to worse performance.
Model captures asymmetric extreme events in financial returns.
Data analysis in high energy physics often deals with data samples consisting of a mixture of signal and background events. The sPlot technique is a common method to subtract the contribution of the background by assigning weights to events. Part of the weights are by design negative. Negative weights lead to the diver…
Interpretability has become an important issue in the machine learning field, along with the success of layered neural networks in various practical tasks. Since a trained layered neural network consists of a complex nonlinear relationship between large number of parameters, we failed to understand how they could achie…
New method learns from either positive or negative feedback alone.
Data analysis in high energy physics has to deal with data samples produced from different sources. One of the most widely used ways to unfold their contributions is the sPlot technique. It uses the results of a maximum likelihood fit to assign weights to events. Some weights produced by sPlot are by design negative. N…
We introduce a new normalization technique that exhibits the fast convergence properties of batch normalization using a transformation of layer weights instead of layer outputs. The proposed technique keeps the contribution of positive and negative weights to the layer output balanced. We validate our method on a set o…
The paper aims to explore the impacts of bi-demographic structure on the current account and growth. Using a SVAR modeling, we track the dynamic impacts between these underlying variables. New insights have been developed about the dynamic interrelation between population growth, current account and economic growth. Th…
Paper breaks down risk contribution into inherent and correlation risk components.
Negative dependence improves machine learning performance.
Study finds topological restrictions for stable free boundary CMC surfaces in negatively curved settings.
In this paper, we introduce the rich classes of conditional distortion (CoD) risk measures and distortion risk contribution (CoD) measures as measures of systemic risk and analyze their properties and representations. The classes include the well-known conditional Value-at-Risk, conditional Expected Shortfall, and r…
Paper proposes an adversarial sampling method for efficient extreme classification.
Study examines how digital image alterations affect AI classification models.
This study uses NLP to predict stock performance based on analyst reports.
In this paper we describe the Seiberg-Witten invariants, which have been introduced by Witten, for manifolds with . In this case the invariants depend on a chamber structure, and there exists a universal wall crossing formula. We take into account the contribution of the 1-homology of the base-manifold. For ever…
We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can learn deep representations shared across multiple tasks while effectively preventing negative transfer that may happen in the feature sharing process. Specifically, we introduce an asymmetric autoencoder term that allows reliable predictors fo…
A novel GNN architecture improves link prediction by combining positive and negative samples.
Study examines cash conversion cycle in manufacturing firms, finding negative relationships with profitability and size.
The paper examines the tilted empirical risk's generalization and robustness under negative tilt.
Oil prices affect Russian banks' stability, with negative impacts from decreases.
In recent years, deep learning methods have outperformed other methods in image recognition. This has fostered imagination of potential application of deep learning technology including safety relevant applications like the interpretation of medical images or autonomous driving. The passage from assistance of a human d…
New model predicts weekly earthquakes with better tail risk assessment.
This paper shows DPPs can outperform random coresets in machine learning tasks.
A study on optimizing data augmentation weights for improved test-time predictions.
Studies of wealth inequality often assume that an observed wealth distribution reflects a system in equilibrium. This constraint is rarely tested empirically. We introduce a simple model that allows equilibrium but does not assume it. To geometric Brownian motion (GBM) we add reallocation: all individuals contribute in…
Study excess logarithmic residues for foliations to bound invariant hypersurfaces and test log canonicity.
Biclustering is an unsupervised data mining technique that aims to unveil patterns (biclusters) from gene expression data matrices. In the framework of this thesis, we propose new biclustering algorithms for microarray data. The latter is done using data mining techniques. The objective is to identify positively and ne…
This paper examines SVB's failure and its impact on bank stocks.
We discuss a peculiar interplay between the representation theory of the holonomy group of a Riemannian manifold, the Weitzenboeck formula for the Hodge-Laplace operator on forms and the Lichnerowicz formula for twisted Dirac operators. For quaternionic Kaehler manifolds this leads to simple proofs of eigenvalue estima…
Stability of Morse index for harmonic maps on degenerating surfaces analyzed.
Machine learning models predict bluebottles' presence on beaches, addressing class imbalance and unreliable absence data.
We use a continuous version of the standard deviation premium principle for pricing in incomplete equity markets by assuming that the investor issuing an unhedgeable derivative security requires compensation for this risk in the form of a pre-specified instantaneous Sharpe ratio. First, we apply our method to price opt…
In this article we use rate-distortion theory, a branch of information theory devoted to the problem of lossy compression, to shed light on an important problem in latent variable modeling of data: is there room to improve the model? One way to address this question is to find an upper bound on the probability (equival…
The paper analyzes fairness of compensation-based risk-sharing schemes for fund payouts.
Analyzing a comprehensive news dataset, we document that joint news coverage triggers attention contagion, causing temporarily inflated valuations for affected stocks. Tracing SEC EDGAR visits from unique IPs, we provide direct evidence of attention spillovers between stocks. Stocks with greater joint news coverage exh…
In this paper we prove that if the geodesic flow of a {compact or non-compact} complete manifold without conjugate points is of the Anosov type, then the average of the integral of the sectional curvature along the geodesic is negative and away from zero from a uniform time. Moreover, in dimension two, if the manifold …
ELBO of VAEs converges to a sum of three entropies.
Data-driven neighborhood definitions and graph constructions are often used in machine learning and signal processing applications. k-nearest neighbor~(kNN) and -neighborhood methods are among the most common methods used for neighborhood selection, due to their computational simplicity. However, the choice of param…
We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification setting. Two key components underpinning the design of our algorithm are balanced error rate and conditional alignment of representations. …
This paper presents a new fuzzy k-means algorithm for the clustering of high-dimensional data in various subspaces. Since high-dimensional data, some features might be irrelevant and relevant but may have different significance in the clustering process. For better clustering, it is crucial to incorporate the contribut…
Ejiri gave a negative answer to a conjecture of Lichnerowicz concerning Riemannian manifolds with constant scalar curvature admitting an infinitesimal non isometric conformal transformation. With this aim he constructed a warped product of a circle of lenght and a compact manifold. But he omitted in his analysis th…
Non-negative matrix factorization (NMF) approximates a non-negative matrix by a product of two non-negative low-rank factor matrices and . NMF and its extensions minimize either the Kullback-Leibler divergence or the Euclidean distance between and to model the Poisson noise or the Gaussian noise.…