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.
Unsupervised method discovers interpretable directions in GAN latent space.
Direct method finds Yang-Mills connections for SO(3) bundles.
Constructs ε-splitting maps for geodesic balls with non-negative Ricci curvature.
New algorithms improve direction finding using prior signal knowledge.
Paper finds conditions for benign overfitting in neural networks.
This paper provides a block coordinate descent algorithm to solve unconstrained optimization problems. In our algorithm, computation of function values or gradients is not required. Instead, pairwise comparison of function values is used. Our algorithm consists of two steps; one is the direction estimate step and the o…
We consider the problem of identifying the causal direction between two discrete random variables using observational data. Unlike previous work, we keep the most general functional model but make an assumption on the unobserved exogenous variable: Inspired by Occam's razor, we assume that the exogenous variable is sim…
We study a class of Riemannian manifolds with respect to the covariant derivative of their curvature tensors. We introduce geometrically the class of directed Riemannian manifolds of pointwise constant relative sectional curvature and give a tensor characterization for such manifolds. We prove that all rotational hyper…
edGNN improves graph embeddings for directed labeled graphs.
We investigate whether the bid/ask queue imbalance in a limit order book (LOB) provides significant predictive power for the direction of the next mid-price movement. We consider this question both in the context of a simple binary classifier, which seeks to predict the direction of the next mid-price movement, and a p…
Automated method finds meaningful directions in neural network activations.
This letter presents a new spectral-clustering-based approach to the subspace clustering problem. Underpinning the proposed method is a convex program for optimal direction search, which for each data point d finds an optimal direction in the span of the data that has minimum projection on the other data points and non…
Is it possible to find the sparsest vector (direction) in a generic subspace with ? This problem can be considered a homogeneous variant of the sparse recovery problem, and finds connections to sparse dictionary learning, sparse PCA, and many other …
A new pruning method finds sparse minimizers in flat regions of deep neural networks.
This paper proposes a novel kernel approach to linear dimension reduction for supervised learning. The purpose of the dimension reduction is to find directions in the input space to explain the output as effectively as possible. The proposed method uses an estimator for the gradient of regression function, based on the…
We find the explicit expression for the equilibrium wealth distribution of the Directed Random Market process, recently introduced by Martínez-Martínez and López-Ruiz, which turns out to be a Gamma distribution with shape parameter . We also prove the convergence of the discrete-time process describing the…
Graphical causal models are an important tool for knowledge discovery because they can represent both the causal relations between variables and the multivariate probability distributions over the data. Once learned, causal graphs can be used for classification, feature selection and hypothesis generation, while reveal…
This paper analyzes the direction of the causality between crude oil, gold and stock markets for the largest economy in the world with respect to such markets, the US. To do so, we apply non-linear Granger causality tests. We find a nonlinear causal relationship among the three markets considered, with the causality go…
New algorithms estimate Hessians using random directions for faster stochastic optimization.
Efficient algorithm learns direct causes and effects from data.
Study uses deep learning to predict asset prices, finds complex target processes lead to meaningless predictions.
Constructs Lie algebras from labeled directed graphs and identifies properties of these algebras.
Improved DOA estimation with distributed sensors across multiple frequencies.
DiMMSB models directed mixed membership networks, identifying distinct community structures.
Study curvature of direct image bundles in deformations of maps.
New method identifies valid IVs for bi-directional MR with invalid instruments.
Study finds central points of double heptagon surface are not connection points.
We study the time dependent cross correlations of stock returns, i.e. we measure the correlation as the function of the time shift between pairs of stock return time series using tick-by-tick data. We find a weak but significant effect showing that in many cases the maximum correlation is at nonzero time shift indicati…
In this paper, we study a simple iterative method for finding the Dantzig selector, which was designed for linear regression problems. The method consists of two main stages. The first stage is to approximate the Dantzig selector through a fixed-point formulation of solutions to the Dantzig selector problem. The second…
In order to investigate whether government regulations against corruption can affect the economic growth of a country, we analyze the dependence between Gross Domestic Product (GDP) per capita growth rates and changes in the Corruption Perceptions Index (CPI). For the period 1999-2004 on average for all countries in th…
Forward gradients improve neural network training without backpropagation issues.
The study finds a subgroup of graph braid groups that is a direct product of non-abelian free groups.
Researchers study solitons on homogeneous spaces, finding useful geometric structures.
The paper finds non-Gaussian directions in high-dimensional data using Wasserstein distance.
Study on null hypersurfaces with constant angle in Lorentzian manifolds.
Direct optimization of binary latent VAEs achieves competitive results without sampling.
Paper characterizes and represents pairwise causal background knowledge for improved causal inference.
LLMs outperform human analysts in predicting earnings direction.
The classification of multivariate functional data is an important task in scientific research. Unlike point-wise data, functional data are usually classified by their shapes rather than by their scales. We define an outlyingness matrix by extending directional outlyingness, an effective measure of the shape variation …
Adversarial examples are maliciously perturbed inputs designed to mislead machine learning (ML) models at test-time. They often transfer: the same adversarial example fools more than one model. In this work, we propose novel methods for estimating the previously unknown dimensionality of the space of adversarial inputs…
Simple DP algorithms find approximate solutions for nonconvex ERM.
Let M be a simply connected Riemannian symmetric space, with at most one flat direction. We show that every Riemannian (or unitary) vector bundle with parallel curvature over M is an associated vector bundle of a canonical principal bundle, with the connection inherited from the principal bundle. The problem of finding…
Study of curves and surfaces in Riemannian spaces making a constant angle with a parallel transported direction.
New method finds invariants of Lie algebras, especially for semi-direct sums.
GOLS finds activation functions affect training robustness, especially ReLU.
This paper establishes the existence of observable footprints that reveal the "causal dispositions" of the object categories appearing in collections of images. We achieve this goal in two steps. First, we take a learning approach to observational causal discovery, and build a classifier that achieves state-of-the-art …
Gradient flow in phase retrieval escapes spurious minima with high probability.
We revisit the classical Douglas-Rachford (DR) method for finding a zero of the sum of two maximal monotone operators. Since the practical performance of the DR method crucially depends on the stepsizes, we aim at developing an adaptive stepsize rule. To that end, we take a closer look at a linear case of the problem a…