arXiv research

New findings show mapping class groups of certain high-dimensional manifolds are not residually finite.

GTBO uses group testing to optimize high-dimensional functions efficiently.

Study on high-dimensional solid tori reveals infinite generation in their diffeomorphism groups.

Classifies hyperbolic manifolds with specific automorphism groups.

New groups algebraically fibre with high-dimensional hyperbolic groups.

GCAO improves clustering of high-dimensional data by grouping low-density boundary points.

Proves triviality of inertia groups in high-dimensional manifolds.

Safe screening rule improves Group SLOPE efficiency.

Proposes a group-splicing algorithm for efficient BSGS in high-dimensional settings.

GTBO uses group testing to optimize high-dimensional functions efficiently.

Paper develops a new estimator for high-dimensional panel data with common shocks.

We explicitly classify all pairs $(M,G)$, where $M$ is a connected complex manifold of dimension $n\ge 2$ and $G$ is a connected Lie group acting properly and effectively on $M$ by holomorphic transformations and having dimension $d_G$ satisfying $n^2+2\le d_G<n^2+2n$. These results extend -- in the complex case -- the…

We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asymptotic variance and develop the inferential theory in the high-dimensional setting. To recognize the ti…

The homotopy theory of gauge groups has received considerable attention in recent decades. In this work, we study the homotopy theory of gauge groups over some high dimensional manifolds. To be more specific, we study gauge groups of bundles over $(n-1)$-connected closed $2n$-manifolds, the classification of which was …

Clustering aims to divide a set of points into groups. The current paradigm assumes that the grouping is well-defined (unique) given the probability model from which the data is drawn. Yet, recent experiments have uncovered several high-dimensional datasets that form different binary groupings after projecting the data…

Novikov theorem extended to rational Pontryagin classes for cyclic group $C_4$.

New theorem for generalized group sparsity improves consistency and convergence rates.

Develops methods for selecting and estimating smooth functional coefficients in high-dimensional multivariate functional data.

The paper triangulates Heisenberg groups with horizontal and straight simplexes.

Improves robustness of high-dimensional regression with rank objective and group lasso regularization.

New rules reduce SLOPE model fitting time by screening out irrelevant variables.

Paper detects and estimates breaks in high-dimensional functional time series.

Proposes a two-stage method for selecting correlated predictors in high-dimensional data.

We determine all connected homogeneous Kobayashi-hyperbolic manifolds of dimension $n\ge 2$ whose holomorphic automorphism group has dimension $n^2-2$. This result complements an existing classification for automorphism group dimension $n^2-1$ and greater obtained without the homogeneity assumption.

In this note we give the quasi-isometry classification for a class of right angled Artin groups. In particular, we obtain the first such classification for a class of Artin groups with dimension larger than 2; our families exist in every dimension.

Screening is the problem of finding a superset of the set of non-zero entries in an unknown p-dimensional vector β* given n noisy observations. Naturally, we want this superset to be as small as possible. We propose a novel framework for screening, which we refer to as Multiple Grouping (MuG), that groups variables, pe…

The sparse group lasso optimization problem is solved using a coordinate gradient descent algorithm. The algorithm is applicable to a broad class of convex loss functions. Convergence of the algorithm is established, and the algorithm is used to investigate the performance of the multinomial sparse group lasso classifi…

Given a hyperbolic knot $K$ and any $n\geq 2$ the abelian representations and the holonomy representation each give rise to an $(n-1)$-dimensional component in the $\operatorname{SL}(n,\Bbb{C})$-character variety. A component of the $\operatorname{SL}(n,\Bbb{C})$-character variety of dimension $\geq n$ is called high-d…

PROBE algorithm efficiently solves sparse high-dimensional linear regression.

Develops MGQDA for multi-group classification with theoretical guarantees and practical applications.

In this paper, we recall Quillen's plus construction for high-dimensional smooth manifolds and the solution to the group extension problem. We then develop a geometric procedure due for producing a "reverse" to the plus construction, a one-sided s-cobordism called a semi-s-cobordism, when the total group of the group e…

Develops a hybrid MtFA approach for high-dimensional data clustering.

Scalable model learns from mixed data groups.

Proposes a neural network framework for feature selection in high-dimensional settings.

For a complex projective space the inertia group, the homotopy inertia group and the concordance inertia group are isomorphic. In complex dimension 4n+1, these groups are related to computations in stable cohomotopy. Using stable homotopy theory, we make explicit computations to show that the inertia group is non-trivi…

Sparsity learning with known grouping structure has received considerable attention due to wide modern applications in high-dimensional data analysis. Although advantages of using group information have been well-studied by shrinkage-based approaches, benefits of group sparsity have not been well-documented for greedy-…

High-dimensional data pose challenges in statistical learning and modeling. Sometimes the predictors can be naturally grouped where pursuing the between-group sparsity is desired. Collinearity may occur in real-world high-dimensional applications where the popular $l_1$ technique suffers from both selection inconsisten…

We investigate the existence of homotopy comoment maps (comoments) for high-dimensional spheres seen as multisymplectic manifolds. Especially, we solve the existence problem for compact effective group actions on spheres and provide explicit constructions for such comoments in interesting particular cases.

We determine all connected homogeneous Kobayashi-hyperbolic manifolds of dimension $n\ge 4$ whose group of holomorphic automorphisms has dimension either $n^2-4$, or $n^2-5$, or $n^2-6$. This paper continues a series of articles that achieve classifications for automorphism group dimension $n^2-3$ and greater.

Study shows mapping class groups differ for $h$-cobordant manifolds.

The classification of high-dimensional mu-component boundary links motivates decomposition theorems for the algebraic K-groups of the group ring A[F_mu] and the noncommutative Cohn localization Sigma^{-1}A[F_mu], for any mu>0 and an arbitrary ring A, with F_mu the free group on mu generators and Sigma the set of matric…

New test for comparing high-dimensional text data.

We present a rigidity theorem for the action of the mapping class group $π_0(\mathrm{Diff}(M))$ on the space $\mathcal{R}^+(M)$ of metrics of positive scalar curvature for high dimensional manifolds $M$. This result is applicable to a great number of cases, for example to simply connected $6$-manifolds and high dimensi…

Unified framework for high-dimensional bandit problems with low-dimensional structures.

Exclusive Lasso improves survival prediction in cancer datasets.

S-DIDML integrates structural DID with ML for causal inference in high-dimensional data.

Robust methods for high-dimensional linear learning improve performance under heavy-tailed distributions and outliers.

SDAMI enhances interpretable high-dimensional regression with sparse deep learning and footprint principle.