Convexity proven in Ricci shrinker limit spaces.
problem Understanding the structure of Ricci shrinker limits.
method Regular-singular decomposition and parabolic smoothing of distance functions.
result The regular part of any Ricci shrinker limit space is convex.
The study limits how many parts regular simplicial partitions can overlap.
problem Bounding the intersection number of regular simplicial partitions.
method Analyzing the properties of regular simplicial partitions.
result Established a maximum limit for the intersection number.
Study shows continuity and geometric regularity of Kähler-Ricci flow blow-up limits.
problem Geometric regularity of blow-up limits of the Kähler-Ricci flow.
method Established geometric regularity for Type I blow-up limits based on sequences of Ricci vertices.
result The limiting flow is continuous in time in Gromov-Hausdorff and Gromov-W1 distance. 2-regular points found in spaces with lower Ricci curvature bound.
problem Characterizing points in spaces with lower Ricci curvature bound.
method Analyzing measured Gromov-Hausdorff limits of Riemannian manifolds.
result 2-regular points in interior geodesics of limit spaces are 2-rectifiable.
Study smooth convergence of metric flows from F-limits.
problem Smooth convergence of F-limit flows. method Extensively studied metric flows and F-limits, showing smooth convergence at regular points. result Each regular point on the limit is a point of smooth convergence.
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 C1,α-regularities and applying Fukaya's fibration theorem. result Optimal generalization of Fukaya's fibration theorem to C1,α limit spaces. Anosov groups limit sets are Ahlfors regular, with applications in Teichmüller spaces.
problem Understanding Ahlfors regularity of limit sets for Anosov groups.
method Proving Ahlfors regularity for limit sets and Patterson-Sullivan measures.
result Patterson-Sullivan measures are Ahlfors regular if and only if associated linear forms are symmetric.
Mean field Gaussian inference limits mutual information to regularize neural networks.
problem Understanding and quantifying the regularization effect of mean field Gaussian inference.
method Empirically observed and theoretically quantified mutual information limitation through noise.
result Bounding mutual information between parameters and data effectively regularizes neural networks.
Regularization methods can overregulate, suppressing causal features.
problem Mitigating shortcuts in models exploiting spurious correlations.
method Analysis of regularization methods and their effects on causal features.
result Regularization can overregulate, suppressing causal features.
Minimal surfaces in the sub-Riemannian Heisenberg group can be constructed by means of a Riemannian approximation scheme, as limit of Riemannian minimal surfaces. We study the regularity of Lipschitz, non-characteristic minimal surfaces which arise as such limits. Our main results are a-priori estimates on the solution…
We study the evolution equations for a regularized version of Dirac-geodesics, which are the one-dimensional version of Dirac-harmonic maps. We show that for the regularization being sufficiently large, the evolution equations subconverge to a regularized Dirac-geodesic. In the end, we discuss the limiting process of r…
We consider a geometrically finite discrete group of conformal transformations of the sphere. Further we consider distributions which are supported on the limit set and are invariant with conformal weight. We estimate their regularity in terms of the conformal weight, the Hausdorff dimension of the limit set, and the m…
This paper examines a novel gradient boosting framework for regression. We regularize gradient boosted trees by introducing subsampling and employ a modified shrinkage algorithm so that at every boosting stage the estimate is given by an average of trees. The resulting algorithm, titled Boulevard, is shown to converge …
Improved covariance matrix estimation for multiple classes with limited data.
problem Estimating covariance matrices for multiple classes with scarce data.
method Coupled regularized sample covariance matrix estimator (RSCM) that combines pooled SCM and scaled identity matrix for regularization.
result The coupled RSCM estimators outperform cross-validation in classification tasks with comparable accuracy but faster computation.
Study boundary behavior of limit interfaces in Riemannian manifolds without convexity assumptions.
problem Boundary behavior of limit interfaces in Riemannian manifolds.
method Proves limit-interface is a free boundary varifold, integer rectifiable up to boundary.
result No convexity assumption required; valid even when limit-interface clusters near boundary.
The Volterra square-root process shows non-uniqueness of limiting distributions and regularity of its law.
problem Non-uniqueness of limiting distributions in the Volterra square-root process.
method Establishing existence of limiting distributions using integrability of the Volterra convolution kernel and exponential-affine transformation.
result The limiting distributions of the Volterra square-root process depend on the initial state and belong to weighted Besov spaces.
Study how regularization and optimization affect margin in deep models.
problem Understanding margin maximization in deep learning models.
method Analyze the limit of loss minimization with diverging norm constraints and margin paths.
result Discovers lexicographic max-margin solutions for homogeneous models and shows convergence under certain conditions.
Characterizes extreme points in polygon limit sets.
problem Identifying boundary points in polygon limit sets.
method Characterization through affine dilations and polygon vertices.
result Characterizes which points lie on the boundary of convex hull.
TAMD prevents degeneracy in finite mixtures, offering strong guarantees but modest practical improvements.
problem Degeneracy in maximum likelihood estimation of finite mixtures.
method Transcendental regularization with analytic barrier functions.
result Strong theoretical guarantees (identifiability, consistency, robustness) but modest practical improvements.
Entropy defined for submanifolds; applies to mean curvature flow limits of surfaces.
problem Entropy for submanifolds in Riemannian manifolds.
method Entropy defined and shown to be monotone along mean curvature flow.
result Partial regularity of mean curvature flow limits of surfaces.
Paper finds only one unique regular shrinker with 2 closed regions.
problem Characterizing blow-up limits of planar curve networks.
method Analysis of Huisken's monotonicity formula.
result There is only one regular shrinker with 2 closed regions.
New method tackles reinforcement learning of complex ω-regular objectives without models.
problem Learning ω-regular objectives in unknown MDPs.
method Constructive reduction to almost-sure reachability, compilation to limit-deterministic Buechi automata.
result Optimal strategies computed from MDP observations using reinforcement learning.
Compactness theory for super Ricci flows provides convergence results.
problem Understanding convergence of super Ricci flows.
method Developed a compactness theory for super Ricci flows.
result Subsequential convergence to a metric flow under certain conditions.
The paper characterizes SLOPE's trade-off between FDP and TPP, showing its power limit and superiority over Lasso.
problem Characterizing the SLOPE trade-off between FDP and TPP.
method Using variational perspective and Gaussian random designs, the paper derives upper and lower bounds on the optimal trade-off.
result SLOPE outperforms Lasso in terms of FDP, TPP, and l2 estimation risk.
The paper examines how adversarial training and noise affect neural network performance.
problem Overfitting in adversarial training and data augmentation.
method Adversarial training and data augmentation with noise in the context of regularized regression in RKHS.
result Appropriate regularization can prevent overfitting and improve performance.
Study the limiting shape of solutions to the L_p-Minkowski problem as p approaches negative infinity.
problem Understanding the limiting shape of solutions to the L_p-Minkowski problem as p → -∞.
method Group-invariant method to study the asymptotic shape of solutions.
result Existence of a solution Ω^(p) to the L_p-Minkowski problem that converges to a regular polytope T as p → -∞.
New method uses Gaussian process for limited-data CT reconstruction.
problem Reconstructing internal structures from limited x-ray projections.
method Gaussian process with basis function expansion for parameter estimation.
result Less sensitive to streak artifacts compared to filtered backprojection.
Unified framework for accelerating DNNs on resource-limited platforms.
problem Accelerating DNN execution on resource-limited platforms.
method Block-based pruning framework with reweighted regularization.
result First universal framework for both CNNs and RNNs with real-time acceleration and no accuracy compromise.
Proposes RDIV for IV estimation avoiding limitations of existing methods.
problem Nonparametric estimation of IV regressions with practical limitations.
method Tikhonov-regularized DeepIV regression with model selection.
result Matches state-of-the-art convergence rate and provides rigorous guarantees.
A new method sparsifies neural networks by reducing sensitive parameters to zero.
problem Challenges of memory-limited applications due to large number of neural network parameters.
method Quantifies output sensitivity, introduces a regularization term to gradually reduce sensitive parameters.
result Surpasses most recent techniques in sparsity and error rates, achieving twice the sparsity at equal error rates in some cases.
Paper analyzes blowup of regularized Jang solutions and constant expansion surfaces.
problem Blowup behavior of regularized solutions to Jang equation inside apparent horizons.
method Two geometric treatments: dilation and translation. Characterization of limits of rescaled and translated solutions.
result Limits of properly rescaled solutions are constant expansion surfaces.
In this paper we study the behaviour of the limit set of complete proper compact minimal immersions in a regular domain G of R^3. We prove that the second fundamental form of the boundary surface of G is nonnegatively defined at every point of the limit set of such immersions.
Study of Calabi-Yau manifold degenerations near complex structure limits.
problem Understanding polarized degenerations of Calabi-Yau manifolds.
method Improvement of metric convergence results on generic regions.
result Metric convergence for collapsing Ricci-flat Kähler metrics on generic regions.
The paper explores optimal regularizers for data sources, linking them to star bodies.
problem Understanding optimal regularizers for data sources.
method Investigates optimal regularizers for data distributions using star bodies and dual Brunn-Minkowski theory.
result Identifies optimal regularizers and assesses amenability to convex regularization.
Study on vortex sheet formation in Abelian gauge theories.
problem Understanding vortex sheet formation in Abelian gauge theories.
method Inspired by Allard's regularity theory, constructs approximate solutions and analyzes their perturbations.
result Establishes a geometric framework and regularity theory for the limiting defect set.
The paper analyzes SGD with dropout regularization in linear models, proving asymptotic properties and providing inference tools.
problem Analyzing the behavior of SGD with dropout regularization in linear models.
method Establishing geometric-moment contraction (GMC) and proving quenched central limit theorems (CLT).
result The existence of a unique stationary distribution and asymptotic normality results for SGD with dropout.
New methods improve estimation of nonhomogeneous Poisson processes from limited data.
problem Estimating nonhomogeneous Poisson processes from limited data.
method Formulated as a learning generalization problem, proposed adaptive and data-driven binning methods.
result Improved estimation of nonhomogeneous Poisson processes with limited data.
Let D be a regular strictly convex bounded domain of R3, and consider a regular Jordan curve Γ⊂∂D. Then, for each ε>0, we obtain the existence of a complete proper minimal immersion ψε:D→D satisfying that the Hausdorff distance δH(ψε(∂D),Γ)<ε, whe…
Improves GAN-based semi-supervised learning with consistency regularization.
problem Lack of consistency in class probability predictions under local perturbations.
method Introduces consistency regularization to GANs, leveraging both local and interpolation consistency.
result Significantly improves performance and achieves new state-of-the-art results.
The paper solves isomonodromy problems and describes limits of Stokes matrices.
problem Solving isomonodromy problems and describing limits of Stokes matrices.
method Analyzes the boundary and monodromy problems of isomonodromy equations, derives explicit expressions for Stokes matrices, and describes limits of Stokes matrices as irregular data degenerates.
result Derives explicit expressions for Stokes matrices and describes limits of Stokes matrices as irregular data degenerates.
Study shows how optimal transport behaves in higher dimensions.
problem Characterizing optimal transport in higher dimensions with Euclidean distance.
method Investigates the small regularization limit of entropic optimal transport.
result The limiting transport plan is supported on transport rays and uniquely minimizes a relative entropy functional.
Assuming minimal regularity assumptions on the data, we revisit the classical problem of finding isometric immersions into the Minkowski spacetime for hypersurfaces of a Lorentzian manifold. Our approach encompasses metrics having Sobolev regularity and Riemann curvature defined in the distributional sense, only. It ap…
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.
Let Y be a Gromov-Hausdorff limit of complete Riemannian n-manifolds with Ricci curvature bounded from below. A point in Y is called k-regular, if its tangent is unique and is isometric to an k-dimensional Euclidean space. By \cite{B5}, there is k>0 such that the set of all k-regular point Rk h…
Efficiently learns optimal regularizers to improve model performance.
problem Improving model generalization and performance on test data.
method Views regularizers as upper bounds on generalization gap and uses linear programming to find optimal hyperparameters.
result The approach can jump to optimal hyperparameters with limited data, improving model performance.
Hidden regular variation defines a subfamily of distributions satisfying multivariate regular variation on E=[0,∞]d\{(0,0,...,0)} and models another regular variation on the sub-cone E(2)=E\∪i=1dLi, where Li is the $i…
This work studies Gaussian geometry under entropy-regularized 2-Wasserstein distance.
problem Understanding Gaussian distributions in uncertainty quantification and diffusivity.
method Entropy-regularized 2-Wasserstein distance, closed-form solutions, fixed-point characterization.
result Closed-form expressions for the 2-Sinkhorn divergence and fixed-point barycenter.
Study shows local topologies of certain geometric spaces.
problem Local topological properties of geometric spaces.
method Analysis of Gromov-Hausdorff limits of manifolds with bounded Ricci curvature.
result Local b1 vanishes for regular loci in limits of non-collapsed manifolds.