We introduce a notion of non-local almost minimal boundaries similar to that introduced by Almgren in geometric measure theory. Extending methods developed recently for non-local minimal surfaces we prove that flat non-local almost minimal boundaries are smooth. This can be viewed as a non-local version of the Almgren-…
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48 results for “Local regularity”
Local regularization fails in transductive learning for some multiclass problems.
problem Whether local regularization can learn all transductive multiclass problems.
method Provided a negative answer by exhibiting a specific multiclass problem.
result Local regularization cannot learn all transductive multiclass problems.
High regularity biharmonic wave maps shown to be locally well-posed.
problem Local wellposedness of biharmonic wave maps with high Sobolev regularity.
method Vanishing viscosity and parabolic regularization to prove existence; geometric nature exploited.
result Local wellposedness established in high Sobolev regularity.
Proves regularity of extremal function on compact Kähler manifolds.
problem Regularity of extremal function on compact Kähler manifolds.
method Local property analysis and equivalence of continuity and Hölder continuity.
result Equivalence of classical notions of local -regularity and locally Hölder continuous property.
Unified approach to characterize and regularize deep neural network local minima.
problem Characterize and improve generalizability of deep neural network local minima.
method Information-theoretic Fisher information metric for local minima characterization and regularization.
result Unified approach successfully characterizes and improves generalizability of DNNs.
New algorithms adaptively compete against complex environments with local regularities.
problem Efficiently competing against complex, locally regular comparator functions in nonparametric settings.
method Locally-adaptive online algorithms using hierarchical -nets and tree experts.
result Proved regret bounds scaling with different types of local regularities, delivering better performance for simple profiles.
The paper proves properties of curves in Riemannian manifolds.
problem Characterizing curves in Riemannian manifolds.
method Analyzing locally minimizing and weak geodesics.
result Locally minimizing curves are weak geodesics under certain conditions.
LLE produces unwanted results without regularization, which can be prevented with regularization.
problem LLE's inherent unwanted results without regularization.
method Mathematical proof and numerical examples of regularization effectiveness.
result Regularization prevents unwanted results in LLE.
Sharp bounds on diameter and eigenvalues for amply regular graphs.
problem Finding bounds for amply regular graphs' diameter and eigenvalues.
method New ideas relating discrete Ricci curvature to local matching properties, including a novel construction of a regular bipartite graph.
result Sharp diameter and eigenvalue bounds for amply regular graphs.
FALL improves local model training with anchor regularization.
problem Efficient local model training in regression tasks.
method Regularization with precomputed anchor models, closed-form solution.
result FALL outperforms network Lasso in accuracy with significantly less training time.
Researchers describe local properties of Haantjes operators.
problem Understanding Haantjes operators with vanishing torsion.
method Complete local description of gl-regular Haantjes operators.
result Complete local description of gl-regular Haantjes operators.
Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networkscs.LG
The paper explains implicit regularization in hierarchical tensor factorization and deep CNNs.
problem Understanding implicit regularization in complex neural network architectures.
method Theoretical analysis using dynamical systems to overcome challenges in hierarchy.
result Established implicit regularization towards low hierarchical tensor rank, equivalent to locality in CNNs.
Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysisstat.ML
Local regularization improves geometric estimates from noisy data.
problem Improving geometric understanding from noisy, perturbed data.
method Local regularization of noisy point clouds to define similarity.
result Locally regularized similarity leads to better geometric recovery.
We prove the existence of the flow by curvature of regular planar networks starting from an initial network which is non-regular. The proof relies on a monotonicity formula for expanding solutions and a local regularity result for the network flow in the spirit of B. White's local regularity theorem for mean curvature …
A new algorithm speeds up EEG source localization using regularization.
problem Challenging inverse problem in mapping EEG readings to brain activity.
method Formulated as a graphical generalized elastic net inverse problem, solved with a variable projected algorithm (VPAL).
result VPAL provides faster and more accurate EEG source localization compared to existing methods.
This article announces the completion of the classification of rank 4 locally projective polytopes and their quotients. There are seventeen universal locally projective polytopes (nine nondegenerate). Amongst their 441 quotients are a further four (nonuniversal) regular polytopes, and 152 nonregular but section regular…
Proves a local version of Myers-Steenrod theorem for specific manifolds.
problem Generalization of Myers-Steenrod theorem to local topological groups.
method Proof for local topological groups of isometries acting on specific manifolds.
result New regularity result for locally homogeneous Riemannian metrics.
Locality regularized reconstruction finds sparse coefficients for sparse and structured data.
problem Finding sparse coefficients for linear representations of data.
method Solves a regularized least squares regression problem with a locality function promoting use of columns close to the target vector.
result Optimal coefficients have at most non-zero entries, and can be supported on the vertices of the Delaunay simplex.
Classifies local boundary conditions for Dirac-type operators on manifolds.
problem Determining all local smooth boundary conditions for Dirac-type operators.
method Combining general theory of boundary value problems for Dirac operators and pointwise considerations.
result Classification of local self-adjoint regular boundary conditions for Dirac spinors in dimensions 3 and 4.
LocalDrop uses local Rademacher complexity for neural network regularization.
problem Overfitting in deep neural networks.
method Developed a new regularization function based on local Rademacher complexity.
result Demonstrated effectiveness of LocalDrop through extensive experiments.
The aim of this paper is to provide new theoretical and computational understanding on two loss regularizations employed in deep learning, known as local entropy and heat regularization. For both regularized losses we introduce variational characterizations that naturally suggest a two-step scheme for their optimizatio…
In the paper, the martingales and super-martingales relative to a regular set of measures are systematically studied. The notion of local regular super-martingale relative to a set of equivalent measures is introduced and the necessary and sufficient conditions of the local regularity of it in the discrete case are fou…
Consider an integral Brakke flow , , inside some ball in Euclidean space. If has small height, its measure does not deviate too much from that of a plane and if is non-empty, then Brakke's local regularity theorem yields that is actually smooth and graphical inside a smaller b…
LOCO-Reg improves CNN accuracy by promoting feature cohesion near filter centers.
problem Current regularization schemes in CNNs violate the principle that weights near the center of a filter are larger than weights on the outside.
method Introduces Locality-Promoting Regularization (LOCO-Reg) to correct this issue.
result LOCO-Reg yields accuracy gains across multiple architectures and datasets.
In this paper we give a new proof of Bismut-Freed's result on the local regularity of the eta invariant of a Dirac operator in odd dimension.
New regularizer for machine learning using private data.
problem Machine learning with private data.
method Distributionally-robust optimization with locally-differentially-private datasets.
result New regularizer for training linear regression models.
In this paper we consider regularized convex cone programming problems. In particular, we first propose an iterative hard thresholding (IHT) method and its variant for solving regularized box constrained convex programming. We show that the sequence generated by these methods converges to a local minimizer.…
Efficient regularization mitigates catastrophic overfitting in single-step adversarial training.
problem Catastrophic overfitting in single-step adversarial training.
method ELLE regularization term to enforce local linearity of the loss function.
result Our regularization term effectively mitigates catastrophic overfitting without the drawbacks of previous methods.
DiCE uses diverse agents to explore and learn, avoiding local minima.
problem Local minima in RL due to limited exploration and correlated behavior.
method DiCE employs a group of heterogeneous agents to explore simultaneously and share experiences, with a diversity regularization mechanism.
result DiCE achieves substantial improvement over baselines in MuJoCo locomotion tasks.
This paper analyzes l1-regularized PageRank for local graph clustering, proving its effectiveness and efficiency.
problem Local graph clustering in large graphs, focusing on recovering a single target cluster given a seed node.
method Statistical analysis of l1-regularized PageRank method for recovery of a target cluster.
result l1-regularized PageRank recovers the full target cluster with bounded false positives and exactly the target cluster if the seed is connected solely to it.
Proves smoothness of minimal surfaces near polyhedral boundaries.
problem Smoothness of free-boundary minimal surfaces near polyhedral domains.
method Allard-type regularity theorem for minimal surfaces in convex polyhedra.
result Minimal surfaces are graphical over a free-boundary plane if close to it.
Proves curvature of conference graphs and finds local matchings.
problem Proving precise values of curvature in conference graphs.
method Combining parameter relations and combinatorial approach.
result Existence of local perfect matchings in broader classes of graphs.
We give two structural conditions on a codimension integral -varifold with first variation locally summable to an exponent that imply the following: whenever each orientable portion of the -embedded part of the varifold (which is non-empty by the Allard regularity theory) is stationarity and the $C^…
We present a reduction procedure for locally conformally symplectic (LCS) manifolds with an action of a Lie group preserving the conformal structure, with respect to any regular value of the momentum mapping. Under certain conditions, this reduction is compatible with the existence of a locally conformally Kähler struc…
We compute approximate solutions to L0 regularized linear regression using L1 regularization, also known as the Lasso, as an initialization step. Our algorithm, the Lass-0 ("Lass-zero"), uses a computationally efficient stepwise search to determine a locally optimal L0 solution given any L1 regularization solution. We …
In this article we introduce local gauge conditions under which many curvature tensors appearing in conformal geometry, such as the Weyl, Cotton, Bach, and Fefferman-Graham obstruction tensors, become elliptic operators. The gauge conditions amount to fixing an -harmonic coordinate system and normalizing the determi…
CurvSSL improves SSL by aligning local manifold curvature.
problem Improving self-supervised learning by capturing local manifold geometry.
method CurvSSL augments Barlow Twins with a curvature-based regularizer to align and decorrelate embeddings across augmentations.
result Curvature-regularized SSL yields competitive or improved linear evaluation performance.
New method improves deep neural networks' generalization using Local Rademacher Complexity.
problem Improving generalization of deep neural networks.
method Developed a novel regularizer based on Local Rademacher Complexity.
result Demonstrated effectiveness of the LRC-based regularizer in improving generalization.
We investigate the local regularity of pointed spacetimes, that is, time-oriented Lorentzian manifolds in which a point and a future-oriented, unit timelike vector (an observer) are selected. Our main result covers the class of Einstein vacuum spacetimes. Under curvature and injectivity bounds only, we establish the ex…
LDReg addresses local dimensional collapse in self-supervised learning.
problem Local dimensional collapse in self-supervised learning representations.
method Local dimensionality regularization based on Fisher-Rao metric.
result LDReg improves representation quality and regularizes local and global dimensions.
Proposes a new graph kernel framework using regularized Wasserstein distances.
problem Learning optimal transport distances for graph kernels.
method Introduces Regularized Wasserstein (RW) discrepancy with two regularization terms.
result Empirically validated method outperforms state-of-the-art methods.
Multifractal Analysis and Local Hoelder Exponents Approach to Detecting Stock Markets Crashescond-mat.other
This paper is devoted to problem of detecting critical events at finiacial markets using methods of multifractal analysis. Namely, the local regularity of time-series is studied. As a result, one can find out a special behavior or signal of regularity before crashes. This spesial behaviour of local Hoelder exponents in…
FedElasticNet reduces communication costs and handles client drift in FL.
problem Expensive communication costs and client drift issues in federated learning.
method Leverages elastic net regularizers to sparsify local updates and limit client drift.
result FedElasticNet effectively resolves communication cost and client drift problems.
The abstract proves the existence and regularity of Brakke flows starting from a given set.
problem Existence and regularity of Brakke flows starting from a given set.
method Proves the existence and regularity of Brakke flows using a closed countably 1-rectifiable set in R^2.
result For almost all time, the flow locally consists of a finite number of embedded curves of class W^{2,2} whose endpoints meet at junctions with angles of 0, 60, or 120 degrees.
The paper proves rigidity and ε-regularity theorems for Ricci shrinkers.
problem Understanding the structure and behavior of Ricci shrinkers.
method Proving rigidity and ε-regularity theorems for Ricci shrinkers using entropy and curvature.
result Non-compact Ricci shrinkers are asymptotic to cones under certain curvature conditions.
A regular F-manifold is an F-manifold (with Euler field) (M, \circ, e, E), such that the endomorphism {\mathcal U}(X) := E \circ X of TM is regular at any p\in M. We prove that the germ ((M,p), \circ, e, E) is uniquely determined (up to isomorphism) by the conjugacy class of {\mathcal U}_{p} : T_{p}M \rightarrow T_{p}M…
Spectral methods are popular in detecting global structures in the given data that can be represented as a matrix. However when the data matrix is sparse or noisy, classic spectral methods usually fail to work, due to localization of eigenvectors (or singular vectors) induced by the sparsity or noise. In this work, we …
Convergence of Ricci-limit spaces under bounded Ricci curvature and local covering geometry Imath.DG
The study extends convergence theorems for Ricci-limit spaces with bounded curvature.
problem Understanding convergence properties of Ricci-limit spaces with bounded curvature.
method Establishing -regularities and applying Fukaya's fibration theorem.
result Optimal generalization of Fukaya's fibration theorem to limit spaces.