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.
RSIC identifies multiple ranks of interest in NMF by analyzing residual sensitivity.
Unified framework evaluates different nearest neighbor classification methods.
Rotates MFVI for better Gaussian approximations.
Deep learning network matches radiologists in breast cancer segmentation.
A new method learns DAGs from Gaussian data without verifying acyclicity.
Detecting adversarial examples currently stands as one of the biggest challenges in the field of deep learning. Adversarial attacks, which produce adversarial examples, increase the prediction likelihood of a target class for a particular data point. During this process, the adversarial example can be further optimized…
Develops a fast algorithm for high-dimensional LASSO penalized quantile regression.
Develops fractional de Rham theory for Maxwell equations.
Paper uses robust GMM to reconstruct missing data in Sentinel-2 images for crop monitoring.
Deep RL evaluation underestimates uncertainty, leading to misleading conclusions.
Ability to quantify and predict progression of a disease is fundamental for selecting an appropriate treatment. Many clinical metrics cannot be acquired frequently either because of their cost (e.g. MRI, gait analysis) or because they are inconvenient or harmful to a patient (e.g. biopsy, x-ray). In such scenarios, in …
The paper introduces MRVaR and MRCov for elliptical and log-elliptical distributions.
We investigate the Japanese personal income distribution in the high income range over the 112 years 1887-1998, and that in the middle income range over the 44 years 1955-98. It is observed that the distribution pattern of the lognormal with power law tail is the universal structure. However the indexes specifying the …
Investigates RI strategies for life insurers with LRD mortality rates.
Neural optimal transport improves multivariate conformal prediction.
We describe the range of a restricted spherical mean transform, which sends a function supported inside a closed ball in a hyperbolic space to its mean values on the geodesics spheres centered at the boundary of the ball. The description resembles that of the same transform on the Euclidean spaces obtained by Mark Agra…
Improved gap for mean curvature of biharmonic hypersurfaces in spheres.
Monotone aggregation of dependent random vectors has an absolutely continuous distribution under certain conditions.
We construct a -axisymmetric, spacelike, spherically symmetric, constant mean curvature hypersurfaces foliation in the Kruskal extension with properties that the mean curvature varies in each slice and ranges from minus infinity to plus infinity. This family of hypersurfaces extends the CMC foliation discussions pos…
Sampling from distributions to find the one with the largest mean arises in a broad range of applications, and it can be mathematically modeled as a multi-armed bandit problem in which each distribution is associated with an arm. This paper studies the sample complexity of identifying the best arm (largest mean) in a m…
We consider the problem of minimizing a Lipschitz differentiable function over a class of sparse symmetric sets that has wide applications in engineering and science. For this problem, it is known that any accumulation point of the classical projected gradient (PG) method with a constant stepsize satisfies the $L…
Quantitative estimate for curvature in mean curvature flow.
Proves uniqueness of geometric flow in various Riemannian manifolds.
Let be a quasi-Fuchsian three-manifold that contains a closed incompressible surface with principal curvatures within the range of the unit interval, for a prescribed function (with mild conditions) on , we construct a closed incompressible surface with mean curvature . A direct application is the existe…
We review recent quantitative results on the approximation of mean field diffusion equations by large systems of interacting particles, obtained by optimal coupling methods. These results concern a larger range of models, more precise senses of convergence and links with the long time behaviour of the systems to be con…
A new copula model for multi-attribute data using optimal transport.
Learning in the presence of outliers is a fundamental problem in statistics. Until recently, all known efficient unsupervised learning algorithms were very sensitive to outliers in high dimensions. In particular, even for the task of robust mean estimation under natural distributional assumptions, no efficient algorith…
K-Means is one of the most used algorithms for data clustering and the usual clustering method for benchmarking. Despite its wide application it is well-known that it suffers from a series of disadvantages; it is only able to find local minima and the positions of the initial clustering centres (centroids) can greatly …
New algorithms protect user-level privacy in learning tasks.
The paper examines 4D hypersurfaces with constant mean curvature in pseudo-Riemannian space forms.
The price of electricity is far more volatile than that of other commodities normally noted for extreme volatility. The possibility of extreme price movements increases the risk of trading in electricity markets. However, underlying the process of price returns is a strong mean-reverting mechanism. We study this featur…
A mean function in reproducing kernel Hilbert space, or a kernel mean, is an important part of many applications ranging from kernel principal component analysis to Hilbert-space embedding of distributions. Given finite samples, an empirical average is the standard estimate for the true kernel mean. We show that this e…
Sharp spectral extension of rigidity theorem for mean-convex manifolds.
Deep Neural Networks (DNNs) have begun to thrive in the field of automation systems, owing to the recent advancements in standardising various aspects such as architecture, optimization techniques, and regularization. In this paper, we take a step towards a better understanding of Spectral Normalization (SN) and its po…
We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using prior domain knowledge. We used an openly available dataset from 20 healthy young adults for evaluation and applied 20-fold …
We study some properties of mean curvature flow solitons in general Riemannian manifolds and in warped products, with emphasis on constant curvature and Schwarzschild type spaces. We focus on splitting and rigidity results under various geometric conditions, ranging from the stability of the soliton to the fact that th…
Study pinched submanifolds in space forms, proving rigidity results.
New Minkowski inequality for capillary surfaces in half-space.
The study pinches the rigidity of self-shrinking surfaces in mean curvature flow.
Median-of-means sampling outperforms mean-of-means for large sample sizes in numerical integration.
Numerous studies have been carried out to measure wind pressures around circular cylinders since the early 20th century due to its engineering significance. Consequently, a large amount of wind pressure data sets have accumulated, which presents an excellent opportunity for using machine learning (ML) techniques to tra…
New model incorporates long-range dependence in mortality rates for better valuation and risk management.
We test for departures from normal and independent and identically distributed (NIID) returns, when returns under the alternative hypothesis are self-affine. Self-affine returns are either fractionally integrated and long-range dependent, or drawn randomly from an L-stable distribution with infinite higher-order moment…
The paper estimates common mean of entangled Gaussians with bounded variances.
We analyze the sample complexity of the thresholding bandit problem, with and without the assumption that the mean values of the arms are increasing. In each case, we provide a lower bound valid for any risk and any -correct algorithm; in addition, we propose an algorithm whose sample complexity is of the same o…
A new method for multi-expert learning-to-defer avoids optimization issues.
New methods for Bayesian inference using mean shift particle systems.
FuseInit optimizes neural network initialization by fusing layers from deeper networks.