arXiv research

The study classifies graphs with specific curvature and maximum degree.

New graphs with maximum degree 4 found to be Ricci-flat.

Graphs with maximum degree Δ have at most O(1) equiangular lines for λ < 3/sqrt(2).

New algorithms find half-optimal independent sets in sparse graphs.

New bound on Jones polynomial for specific positive links.

Polynomial neural networks explore thresholds for maximum expressiveness.

New bounds on Khovanov homology for positive links families.

A new topology improves decentralized learning efficiency and accuracy.

New bounds on HOMFLY polynomial for homogeneous links.

New upper bound on Jones polynomial for fibered positive links.

A family of Markov blankets in a faithful Bayesian network satisfies the symmetry and consistency properties. In this paper, we draw a bijection between families of consistent Markov blankets and moral graphs. We define the new concepts of weak recursive simpliciality and perfect elimination kits. We prove that they ar…

Paper proves convex domains have one maximum for semi-stable solutions.

Margalit and Schleimer constructed nontrivial roots of the Dehn twist about a nonseparating curve. We prove that the conjugacy classes of roots of the Dehn twist about a nonseparating curve correspond to the conjugacy classes of periodic maps with certain conditions. Futhermore, we give data set which determine the con…

The resilience of low-degree Rademacher chaos is studied, providing probabilistic lower bounds.

The paper studies connectivity properties of Morse complexes as simplicial complexes grow.

We show for an alternating knot the minimal boundary slope of an essential spanning surface is given by the signature plus twice the minimum degree of the Jones polynomial and the maximal boundary slope of an essential spanning surface is given by the signature plus twice the maximum degree of the Jones polynomial. For…

In this work we study the degree distribution, the maximum vertex and edge flow in non-uniform random Delaunay triangulations when geodesic routing is used. We also investigate the vertex and edge flow in Erdös-Renyi random graphs, geometric random graphs, expanders and random $k$-regular graphs. Moreover we show that …

The Milnor degree of a 3-manifold is an invariant that records the maximum simplicity, in terms of higher order linking, of any link in the 3-sphere that can be surgered to give the manifold. This invariant is investigated in the context of torsion linking forms, nilpotent quotients of the fundamental group, Massey pro…

This paper tests the multivariate normality of node degrees in Erdős-Rényi graphs.

Upper bounds on map degrees for various manifold types.

We describe a procedure for creating infinite families of knots, each having the maximum degree of their HOMFLY polynomial strictly less than twice their canonical genus. These families build upon examples first found by Stoimenow.

Kernelized cumulants improve statistical analysis in high-dimensional spaces.

New study shows limits of low-degree algorithms in finding large independent sets in sparse hypergraphs.

AI methods often fail to outperform classical CPU-based solvers on Maximum Independent Set problems.

MAXENT method outperforms ML in sparse data with specific prior correlations.

We give quantitative and qualitative results on the family of surfaces in $\mathbb{CP}^3$ containing finitely many twistor lines. We start by analyzing the ideal sheaf of a finite set of disjoint lines $E$. We prove that its general element is a smooth surface containing $E$ and no other line. Afterwards we prove that …

The paper studies inference in hypergraph β-models with multiple layers.

EPGP surrogate outperforms finite elements in solving wave equations.

Given i.i.d. observations of a random vector $X \in \mathbb{R}^p$, we study the problem of estimating both its covariance matrix $Σ^*$, and its inverse covariance or concentration matrix {$Θ^* = (Σ^*)^{-1}$.} We estimate $Θ^*$ by minimizing an $\ell_1$-penalized log-determinant Bregman divergence; in the multivariate G…

Study on likelihood functions, associative equations, and Frobenius manifolds.

Inference in general Markov random fields (MRFs) is NP-hard, though identifying the maximum a posteriori (MAP) configuration of pairwise MRFs with submodular cost functions is efficiently solvable using graph cuts. Marginal inference, however, even for this restricted class, is in #P. We prove new formulations of deriv…

We analyze the performance of spectral clustering for community extraction in stochastic block models. We show that, under mild conditions, spectral clustering applied to the adjacency matrix of the network can consistently recover hidden communities even when the order of the maximum expected degree is as small as $\l…

We consider the problem of learning the underlying graph of an unknown Ising model on p spins from a collection of i.i.d. samples generated from the model. We suggest a new estimator that is computationally efficient and requires a number of samples that is near-optimal with respect to previously established informatio…

Paper provides robustness bounds for GNNs against adversarial attacks.

Logistic regression is one of the most popular methods in binary classification, wherein estimation of model parameters is carried out by solving the maximum likelihood (ML) optimization problem, and the ML estimator is defined to be the optimal solution of this problem. It is well known that the ML estimator exists wh…

Study how noisy labels affect semi-supervised learning.

Graphs with nonnegative Bakry-Émery curvature have volume doubling and Poincaré inequalities.

Score matching offers efficient estimation for certain distributions.

Incorporating feature selection into a classification or regression method often carries a number of advantages. In this paper we formalize feature selection specifically from a discriminative perspective of improving classification/regression accuracy. The feature selection method is developed as an extension to the r…

Let G be a finite group acting orthogonally on a pair (S^d,Γ) where Γis a finite, connected graph of genus g>1 embedded in the sphere S^d. The 3-dimensional case d=3 has recently been considered in a paper by C. Wang, S. Wang, Y. Zhang and the present author where for each genus g>1 the maximum order of a G-action on a…

The performance of spectral clustering can be considerably improved via regularization, as demonstrated empirically in Amini et. al (2012). Here, we provide an attempt at quantifying this improvement through theoretical analysis. Under the stochastic block model (SBM), and its extensions, previous results on spectral c…

In this paper we construct a large class of new normal forms for Levi-nondegenerate real hypersurfaces in complex spaces. We adopt a general approach illustrating why these normal forms are natural and which role is played by the celebrated Chern-Moser normal form. The latter appears in our class as the one with the "m…

It is shown that a self-dual neutral Einstein four-manifold of Petrov type III, admitting a two-dimensional null parallel distribution compatible with the orientation, cannot be compact or locally homogeneous, and its maximum possible degree of mobility is 3. Diaz-Ramos, Garcia-Rio and Vazquez-Lorenzo found a general c…

We present asymptotic and finite-sample results on the use of stochastic blockmodels for the analysis of network data. We show that the fraction of misclassified network nodes converges in probability to zero under maximum likelihood fitting when the number of classes is allowed to grow as the root of the network size …

Study improves bounds on p-covectors and proves stable systolic inequalities.

The paper improves bounds on topological complexity for certain manifolds.

Neuroscientists have enjoyed much success in understanding brain functions by constructing brain connectivity networks using data collected under highly controlled experimental settings. However, these experimental settings bear little resemblance to our real-life experience in day-to-day interactions with the surround…

Recent contributions have framed linear system identification as a nonparametric regularized inverse problem. Relying on $\ell_2$-type regularization which accounts for the stability and smoothness of the impulse response to be estimated, these approaches have been shown to be competitive w.r.t classical parametric met…