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 proposes pricing methods for multi-asset generalized variance swaps.
In this paper, we obtain some properties of biconservative Lorentz hypersurface in having shape operator with complex eigen values. We prove that every biconservative Lorentz hypersurface in whose shape operator has complex eigen values with at most five distinct prin…
This paper has been withdrawn by the author and it is published in AGAG
We prove polynomial and exponential decay at infinity of eigen-vectors of partial differential operators related to radiation problems for time-harmonic generalized Maxwell systems in an exterior domain with non-smooth inhomogeneous, anisotropic coefficients converging near infinity with a certain rate towards the iden…
In this article we give a classification of three dimensional m-quasi Einstein manifolds with two distinct Ricci-eigen values. Our study provides explicit description of local and complete metrics and potential functions. We also describe the associated warped product Einstein manifolds in detail. For the proof we pres…
This work studies the linear approximation of high-dimensional dynamical systems using low-rank dynamic mode decomposition (DMD). Searching this approximation in a data-driven approach is formalised as attempting to solve a low-rank constrained optimisation problem. This problem is non-convex and state-of-the-art algor…
The Hessian-vector product has been utilized to find a second-order stationary solution with strong complexity guarantee (e.g., almost linear time complexity in the problem's dimensionality). In this paper, we propose to further reduce the number of Hessian-vector products for faster non-convex optimization. Previous a…
In this paper, we have proposed a brain signal classification method, which uses eigenvalues of the covariance matrix as features to classify images (topomaps) created from the brain signals. The signals are recorded during the answering of 2D and 3D questions. The system is used to classify the correct and incorrect a…
A hierarchical approach improves classification accuracy in large datasets.
This is a revised version of our short note [arxiv.math.DG/0403065] where we discuss the monotonicity of the eigen-values of the Laplacian operator to the Ricci-Hamilton flow on a compact or a complete non-compact Riemannian manifold. We show that the eigenvalue of the Lapacian operator on a compact domain associated w…
Recent studies have shown that proximal gradient (PG) method and accelerated gradient method (APG) with restarting can enjoy a linear convergence under a weaker condition than strong convexity, namely a quadratic growth condition (QGC). However, the faster convergence of restarting APG method relies on the potentially …
Lower bounds on eigenspectrum show rich action spaces force polynomial regret in linear bandits.
The maximum number of maximum cliques in a graph is determined for graphs with at least 15 vertices.
The paper establishes maximum principles and stochastic completeness for pseudo-Hermitian manifolds.
One of the earliest conjectures in computational learning theory-the Sample Compression conjecture-asserts that concept classes (equivalently set systems) admit compression schemes of size linear in their VC dimension. To-date this statement is known to be true for maximum classes---those that possess maximum cardinali…
The study classifies graphs with specific curvature and maximum degree.
A new algorithm learns diverse policies in reinforcement learning.
We find the maximum mutual information for neural networks and its key determinants.
Establishes a boundary maximum principle for varifolds with fixed contact angle.
In this paper we present a proof of a Neumann type maximum principle for the Laplace operator on compact Riemannian manifolds. A key p oint is the simple geometric nature of the constant in the a priori estimate of this maximum principle. In particular, this maximum principle can be applied to manifolds with Ricci curv…
Maximum likelihood estimation fails to be well-posed in Gaussian process regression.
Note establishes a local maximum principle for Ricci flow under curvature conditions.
Modeling maximum drawdown records in capital markets using PDMP.
We present a new statistical learning paradigm for Boltzmann machines based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from Jaynes maximum entropy principle and from standard maximum likelihood estimation.We demonstrate the LME principle BY deriving …
The paper calculates genus bounds for multibranched surfaces.
Improved text summarization using belief propagation on weighted bipartite graphs.
Study proves Maximum Principles for unbounded Riemannian domains.
New maximum score estimators using ReLU functions and deep neural networks.
In this paper we characterize the degenerate elliptic equations F(D^2u)=0 whose viscosity subsolutions, (F(D^2u) \geq 0), satisfy the strong maximum principle. We introduce an easily computed function f(t) for t > 0, determined by F, and we show that the strong maximum principle holds depending on whether the integral …
In this work we consider viscosity solutions to second order partial differential equations on Riemannian manifolds. We prove maximum principles for solutions to Dirichlet problem on a compact Riemannian manifold with boundary. Using a different method, we generalize maximum principles of Omori and Yau to a viscosity v…
Maximum entropy modeling is a flexible and popular framework for formulating statistical models given partial knowledge. In this paper, rather than the traditional method of optimizing over the continuous density directly, we learn a smooth and invertible transformation that maps a simple distribution to the desired ma…
This article studies a discrete geometric structure on triangulated manifolds and an associated curvature flow (combinatorial Yamabe flow). The associated evolution of curvature appears to be like a heat equation on graphs, but it can be shown to not satisfy the maximum principle. The notion of a parabolic-like operato…
New graphs with maximum degree 4 found to be Ricci-flat.
Paper develops MRCs for supervised classification using generalized maximum entropy.
Maximum Levine-Tristram signature of torus knots follows a reduction formula.
The authors found necessary and sufficient conditions for Samuelson's web to be of maximum rank.
Many inference problems involving questions of optimality ask for the maximum or the minimum of a finite set of unknown quantities. This technical report derives the first two posterior moments of the maximum of two correlated Gaussian variables and the first two posterior moments of the two generating variables (corre…
The well known maximum-entropy principle due to Jaynes, which states that given mean parameters, the maximum entropy distribution matching them is in an exponential family, has been very popular in machine learning due to its "Occam's razor" interpretation. Unfortunately, calculating the potentials in the maximum-entro…
Researchers use Gaussian processes to approximate Lagrange multipliers for Maximum-Entropy distributions.
The study finds a unique systole maximum in non-hyperelliptic surfaces.
Paper finds maximum curvature of Bézier-spline curves.
We assume that an individual invests in a financial market with one riskless and one risky asset, with the latter's price following geometric Brownian motion as in the Black-Scholes model. Under a constant rate of consumption, we find the optimal investment strategy for the individual who wishes to minimize the probabi…
Proves a principle for one-phase Bernoulli problem minimizers.
Adversarial learning of probabilistic models has recently emerged as a promising alternative to maximum likelihood. Implicit models such as generative adversarial networks (GAN) often generate better samples compared to explicit models trained by maximum likelihood. Yet, GANs sidestep the characterization of an explici…
Enhances power of covariance matrix tests for high-dimensional data.
Learning-based BnB improves maximum common subgraph search efficiency.
MGD combines maximum entropy and diffusion methods for efficient sampling.
Graphical lasso may fail to fit models when data points are insufficient.