arXiv research

In Peña (2007), MCMC sampling is applied to approximately calculate the ratio of essential graphs (EGs) to directed acyclic graphs (DAGs) for up to 20 nodes. In the present paper, we extend that work from 20 to 31 nodes. We also extend that work by computing the approximate ratio of connected EGs to connected DAGs, of …

This paper analyzes OGDA and EG methods for nonconvex minimax problems.

Unified analysis of gradient-based methods for finding Nash equilibria in games.

Paper generalizes extragradient methods for solving equations and inclusions with improved convergence rates.

New analysis shows convergence rate of 1/k for gradient and extra-gradient methods.

Last iterate of Extragradient algorithm converges slower than averaged iterates in saddle point problems.

Survey on extragradient methods for solving nonlinear equations and inclusions.

Unified analysis of EG and OGDA for saddle point problems using proximal point method.

Smooth classifying spaces for groups defined using diffeological spaces.

AI-GAN generates realistic adversarial examples efficiently.

Enhanced visual feature attribution via adaptive baseline weighting.

New algorithms identify Pareto optimal sets in multi-objective bandit problems.

Given a compact Riemannian manifold together with a group of isometries, we discuss MCF of the orbits and some applications: eg, finding minimal orbits. We then specialize to Lagrangian orbits in Kaehler manifolds. In particular, in the Kaehler-Einstein case we find a relation between MCF and moment maps which, for exa…

To obtain groups with bounded harmonic functions (which are not hyperbolic), one of the most frequent way is to look at some semi-direct products (\eg lamplighter groups). The aim here is to show that many of these semi-direct products do not admit harmonic functions with gradient in $\ell^p$, for $p\in [1,\infty[$.

ROMs predict thermal power output in EGS systems, accounting for uncertainties.

Paper improves text-to-SQL models with schema-aware denoising.

Unified analysis of online optimization with self-concordant barriers, improving regret bounds.

EG-LF-MCMC infers posterior densities without likelihoods.

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image s…

By use of a variety of techniques (most based on constructions of quasipositive knots and links, some old and others new), many smooth 3-manifolds are realized as transverse intersections of complex surfaces in complex 3-space with strictly pseudoconvex 5-spheres. These manifolds not only inherit interesting intrinsic …

Configuration spaces of distinct labeled points on the plane are of practical relevance in designing safe control schemes for Automated Guided Vehicles (robots) in industrial settings. In this announcement, we consider the problem of the construction and classification of configuration spaces for graphs. Topological da…

This work aims to reduce inexplicable errors in deep neural networks by obtaining class-level semantics and penalizing misclassifications.

We consider magnetic flows on 2-step nilmanifolds $M = Γ\backslash G$, where the Riemannian metric $g$ and the magnetic field $σ$ are left-invariant. Our first result is that when $σ$ represents a rational cohomology class and its restriction to $\mathfrak{g} = T_eG$ vanishes on the derived algebra, then the associated…

Neuroscientific studies of drawing-like movements usually analyze neural representation of either geometric (eg. direction, shape) or temporal (eg. speed) features of trajectories rather than trajectory's representation as a whole. This work is about empirically supported mathematical ideas behind splitting and merging…

Develops a new method for equivariant Lagrangian Floer homology using symplectic homotopy quotients.

A Lorentzian flat Lie group is a Lie group $G$ with a flat left invariant metric $μ$ with signature $(1,n-1)=(-,+,\ldots,+)$. The Lie algebra $\mathfrak{g}=T_eG$ of $G$ endowed with $\langle\;,\;\rangle=μ(e)$ is called flat Lorentzian Lie algebra. It is known that the metric of a flat Lorentzian Lie group is geodesical…

The paper extends Chern-Weil theory to simplicial principal bundles.

Let $(M^n,g,\nabla f)$, $n\geq 3$, be an expanding gradient Ricci soliton with nonnegative sectional curvature whose asymptotic cone is isometric to $C(\mathbb{S}^{n-1}(c))$ where $\mathbb{S}^{n-1}(c)$ is the standard $(n-1)$-sphere of curvature $1/c^2$, with $c\in(0,1)$. We prove that if the convergence to the asympto…

We describe the Cartan and Weil models of twisted equivariant cohomology together with the Cartan homomorphism among the two, and we extend the Chern-Weil homomorphism to the twisted equivariant cohomology. We clarify that in order to have a cohomology theory, the coefficients of the twisted equivariant cohomology must…

This is the first in a series of four papers (with research announcement posted on this arXiv) that together develop a decomposition theory for subgroups of Out(F_n). In this paper we develop further the theory of geometric EG strata of relative train track maps originally introduced in the work of Bestvina, Feighn, an…

We introduce O-systems (Definition \ref{DO}) of orthogonal transformations of ${\Bbb R}^{m}$, and establish $1-1$ correspondences both between equivalence classes of Clifford systems and that of O-systems, and between O-systems and orthogonal multiplications of the form $μ:{\Bbb R}^{n} \times {\Bbb R}^{m} \longrightarr…

BYOV combines SSL and Bayesian methods for uncertainty estimation.

We describe the moduli space of extensions in the model category of simplicial presheaves. This article can be seen as a generalization of Blomgren-Chacholski results in the case of simplicial sets. Our description of the moduli space of extensions treat the equivariant and the nonequivariant case in the same setting. …

We study Kahler manifolds-with-boundary, not necessarily compact, with weakly pseudoconvex boundary, each component of which is compact. If such a manifold $K$ has $l\ge2$ boundary components (possibly $l=\infty$), then it has first betti number at least $l-1$, and the Levi form of any boundary component is zero. If $K…

G-TRACER optimizes deep learning by promoting flat minima.

DFMs generalize neural networks for topological layer design.

Unsupervised model learns data invariance without labels.

Efficient semi-analytic methods for pricing double barrier options with time-dependent parameters.

In this paper we consider a version of the zero-shot learning problem where seen class source and target domain data are provided. The goal during test-time is to accurately predict the class label of an unseen target domain instance based on revealed source domain side information (\eg attributes) for unseen classes. …

Improved autoencoder for realistic face manipulation.

Small sample sizes lead to unreliable error bars in predictive models.

The paper constructs hyperbolic elements in multiple spaces.

The study uses ML and AI to forecast pension fund mortality, outperforming traditional methods.

A new method for discrete data normalizing flows using latent transformations.

Left invariant affine structures in a Lie group $G$ are in one-to-one correspondence with left-symmetric algebras over its Lie algebra $\mathfrak g=T_eG$ (``over'' means that the commutator $[x,y]=xy-yx$ coincides with the Lie bracket; left-symmetric algebras can be defined as Lie-admissible algebras such that the mult…

Defends classifiers from adversarial attacks using self-supervised data estimation.

Automates treatment recommendation decisions by learning cost-effective and interpretable rules.

The paper highlights AI brittleness and the need for robust testing out-of-distribution performance.