The paper explores rigidity of hypersurfaces with constant shifted curvature functions in hyperbolic space.
problem Rigidity of hypersurfaces with constant shifted curvature functions in hyperbolic space.
method Characterizations and rigidity investigations for hypersurfaces with constant weighted shifted mean curvatures or ratios.
result Rigidity results for hypersurfaces with constant linear combinations of weighted shifted mean curvatures and radially symmetric shifted mean curvatures.
New curvature for weighted Sasaki sphere found.
problem Classifying Sasaki manifolds.
method Using shifted cones introduced by Yang and Zhang.
result New curvature characterization for weighted Sasaki sphere.
A feature-weighted mean shift algorithm improves clustering in high-dimensional data.
problem Clustering high-dimensional data with traditional mean shift algorithms.
method Feature-weighted mean shift algorithm.
result The algorithm outperforms conventional mean shift and preserves computational simplicity.
A method to remove mean-shift noise from PCA using knockoffs.
problem High sensitivity of PCA to mean-shift contamination in high-dimensional data.
method Introducing knockoff mean-shift perturbation to separate and remove mean-shift components from PCA.
result The mean-shift spikes are spectrally separable from stable eigenvalues, allowing for robust PCA.
New algorithm improves clustering and quantization using MMD.
problem Approximating probability distributions with weighted mixtures of Dirac measures.
method Gradient flow, mean shift, and MMD-optimal quantization.
result MSIP algorithm is more robust than state-of-the-art methods.
The paper studies weak singular Hermite-Einstein structures on homogeneous vector bundles.
problem Existence of weak singular Hermite-Einstein structures on homogeneous holomorphic vector bundles.
method Using Cartan's highest weight theory, the paper establishes an algebraic criterion for topological splitting and decouples the prescribed mean curvature equation.
result A sufficient algebraic condition for realizing an L2-function as the mean curvature of a singular Hermitian structure on an irreducible homogeneous bundle. Proves new inequality for hyperbolic space hypersurfaces.
problem Finding inequalities for hypersurfaces in hyperbolic space.
method Proves a Heintze-Karcher type inequality for shifted mean convex hypersurfaces.
result Proves Alexandrov type theorem and uniqueness result for hypersurfaces.
Proposes a new method to adapt to covariate shifts in supervised learning.
problem Covariate shift in training and testing samples with different marginal distributions.
method Minimax risk classification (MRC) approach that weights both training and testing samples.
result Significantly enhanced classification performance in synthetic and empirical experiments.
LCW reduces activation shift in neural networks, improving training efficiency and generalization.
problem Activation shift in neural networks leading to non-zero mean preactivation values.
method Linearly constrained weights (LCW) to reduce activation shift in fully connected and convolutional layers.
result LCW resolves the vanishing gradient problem and improves generalization of neural networks.
A new estimator combines KMM and NR to robustly correct covariate shift.
problem Correcting sampling biases in learning problems with different distributions.
method Integrates residuals of nonparametric regression with kernel mean matching reweighting.
result Proposed estimator outperforms or matches existing rates for KMM and NR.
The study examines constant weighted mean curvature hypersurfaces in shrinking Ricci solitons.
problem Characterizing constant weighted mean curvature hypersurfaces in shrinking Ricci solitons.
method Analyzing properties of hypersurfaces in specific ambient spaces (shrinking Ricci solitons).
result Conditions for a constant weighted mean curvature hypersurface to be a level set of the potential function.
The paper classifies hypersurfaces with constant weighted mean curvature.
problem Characterizing and classifying hypersurfaces with specific curvature properties.
method Using intrinsic properties of the second fundamental form and analyzing weighted volume and growth.
result Characterization of hyperplanes and generalized round cylinders.
Utilizing a weight matrix we study surfaces of prescribed weighted mean curvature which yield a natural generalisation to critical points of anisotropic surface energies. We first derive a differential equation for the normal of immersions with prescribed weighted mean curvature, generalising a result of Clarenz and vo…
The paper studies curvature flows in hyperbolic space and proves geometric inequalities.
problem Proving geometric inequalities in hyperbolic space using curvature flows.
method Locally constrained curvature flows, h-convexity, and shifted principal curvatures.
result Established new sharp geometric inequalities comparing curvature integrals to quermassintegrals.
The paper proves a new method to improve generalization in covariate-shift scenarios.
problem Improving performance on test distributions that differ from training distributions.
method Independence-driven importance weighting algorithms for feature selection.
result Theoretical proof that these algorithms can identify optimal variables for covariate-shift generalization.
Bayesian inference for inverse problems using mean-shift interacting particles
problem Bayesian inference for inverse problems
method Amortized mean-shift interacting particles
result Improves accuracy of Bayesian inference by reducing the number of samples needed
New methods for Bayesian inference using mean shift particle systems.
problem Approximating expectations with unnormalized densities in Bayesian inference.
method Mean shift interacting particle systems that minimize maximum mean discrepancy (MMD).
result Mean shift interacting particle systems converge quickly and capture complex distributions.
The paper integrates behavioral distortions into portfolio optimization using implied probability weighting functions.
problem Behavioral distortions in probability weighting affect portfolio optimization under different return distributions.
method Developed a unified framework to extract probability weighting functions from optimal portfolios modeled under Gaussian and NIG distributions.
result Increasing tail fatness amplifies behavioral distortions, and shifts in risk-free rates alter the curvature of these distortions.
The paper addresses missing data imputation issues by correcting for distribution shift.
problem Missing data imputation and the resulting distribution shift between observed and full data.
method Formulates imputation as a risk minimization problem and proposes a novel algorithm to correct for distribution shift.
result The proposed algorithm consistently improves imputation accuracy, reducing RMSE and Wasserstein distance by 3% and 7%, respectively.
HypeGBMS clusters data in hyperbolic space, overcoming Euclidean limitations.
problem Clustering in hierarchical or tree-like datasets in curved spaces.
method Hyperbolic Gaussian Blurring Mean Shift with Möbius-weighted means.
result HypeGBMS effectively captures latent hierarchies in non-Euclidean data.
Study on curvature bounds for specific hypersurfaces in Anti-de Sitter space.
problem Bounding principal curvatures of constant mean curvature hypersurfaces.
method Generalized convex hull concept and quantitative estimates based on width.
result Explicit bounds on sectional curvature and quasiconformal dilatation.
Develops a new random forest method for clustered data with improved prediction and inference.
problem Improving prediction and inference accuracy for clustered data with within-cluster dependence.
method Clustered Random Forests, using weighted least squares estimators for leaf predictions.
result Optimal prediction and inference weights vary under covariate shift, necessitating user-chosen weights.
Let Σ be a compact immersed surface with constant weighted mean curvature Hf in a weighted manifold (M3,g,f). In this paper we obtain upper bounds for the first eigenvalue of the weighted Jacobi operator on Σ in terms of Hf and the curvature of the ambient. As consequence we obtain that there is no stable …
We propose a novel calibration method for computer simulators, dealing with the problem of covariate shift. Covariate shift is the situation where input distributions for training and test are different, and ubiquitous in applications of simulations. Our approach is based on Bayesian inference with kernel mean embeddin…
The paper proves rigidity for hypersurfaces with constant shifted curvature functions in warped product manifolds.
problem Characterizing and proving rigidity for hypersurfaces with constant shifted curvature functions.
method Using integral inequalities and Minkowski-type formulas, the paper derives rigidity theorems in sub-static warped product manifolds.
result The paper provides new characterizations and rigidity results for hypersurfaces with constant shifted curvature functions in warped product manifolds.
In this paper, we prove that a noncompact complete hypersurface with finite weighted volume, weighted mean curvature vector bounded in norm, and isometrically immersed in a complete weighted manifold is proper. In addition, we obtain an estimate for f-stability index of a constant weighted mean curvature hypersurface…
A new method for covariate shift adaptation using nearest neighbors.
problem Mitigating distribution shift between source and target datasets.
method Directly work on unlabeled target data, labeled by nearest neighbors in source data.
result Optimal choice of k=1 simplifies hyper-parameter tuning and improves efficiency. Derives integral formulae on weighted manifolds.
problem No specific problem stated; focuses on mathematical derivations.
method Introduces weighted mean sigma-r curvature and uses weighted Newton transformations.
result Derives integral formulae generalizing previous work.
The paper studies hypersurfaces with constant weighted mean curvature in Gaussian space.
problem Characterizing hypersurfaces with specific properties of their Gauss map.
method Analyzing the Gauss map and its image in the Gaussian space.
result Hypersurfaces with certain properties of their Gauss map are either hyperplanes or generalized cylinders.
Corrects distribution shift in target shift scenarios using importance weighting.
problem Analyzes importance weighting for correcting distribution shift under target shift.
method Analyzed importance-weighted kernel ridge regression under target shift.
result Shows that importance weighting corrects the train-test mismatch without altering input-space complexity.
Paper proves inequalities in sub-static warped product manifolds.
problem Proving inequalities in sub-static warped product manifolds.
method Proved Heintze-Karcher type inequalities involving shifted mean curvature.
result Uniqueness results for hypersurfaces satisfying curvature equations.
New technique trains deep neural networks without normalization or minibatch statistics.
problem Training deep neural networks at high learning rates without normalization.
method Channel-wise zero-mean initialization and gradient modification to maintain common mode rejection.
result Achieves higher accuracy compared to batch normalization and shows minibatches are unnecessary.
New method optimizes model selection in high-dimensional regression models.
problem Model selection in high-dimensional misspecified regression models with covariate shift.
method Importance-weighted orthogonal greedy algorithm (IWOGA) and high-dimensional importance-weighted information criterion (HDIWIC).
result IWOGA + HDIWIC achieves optimal convergence rates in terms of prediction error.
We study Riemannian manifolds with boundary under a lower weighted Ricci curvature bound. We consider a curvature condition in which the weighted Ricci curvature is bounded from below by the density function. Under the curvature condition, and a suitable condition for the weighted mean curvature for the boundary, we ob…
The study proves topological rigidity for certain geometric shapes using Poincaré inequalities.
problem Proving topological rigidity for translators and self-expanders in mean curvature flow.
method Abstract structure theorem for weighted manifolds, Poincaré inequality, and topological control.
result Full topological control on translators and self-expanders under stability or curvature assumptions.
As first noted in Korevaar, Kusner and Solomon ("KKS"), constant mean curvature implies a homological conservation law for hypersurfaces in ambient spaces with Killing fields.In Theorem 3.5 here, we generalize that law by relaxing the topological restrictions assumed in [KKS] and by allowing a weighted mean curvature f…
Mean curvature flow is not a gradient flow on two nondegenerate metric spaces.
problem Whether mean curvature flow is a gradient flow on nondegenerate metric spaces of simple closed plane curves.
method Examined two nondegenerate metric spaces: uniformness-preserving and curvature-weighted structures.
result Mean curvature flow is not a gradient flow on either metric space.
Optimizes weights for better model performance in shifting data.
problem Improper importance weighting leads to poor model performance in data shifts.
method Interprets weights as a bias-variance trade-off and optimizes them simultaneously with model parameters.
result Optimizing weights significantly improves model generalization performance.
Extend CPS to non-exchangeable settings with observation-specific permutation weights
problem Calibrated predictive bands under distributional shifts
method Encoding distributional shifts through observation-specific permutation weights
result Shift-aware predictive systems remain valid
Solves constant mean curvature Dirichlet problem on catenoids with improved estimates.
problem Solving constant mean curvature Dirichlet problem on catenoidal necks.
method Found solutions in exponentially weighted Hölder spaces with non-integer weight.
result Improved estimate to γ=1 by comparing solutions with their limits on the disk.
New loss function restores importance weighting in overparameterized models.
problem Restoring importance weighting in overparameterized neural networks.
method Introduced polynomially-tailed losses to restore effects of importance weighting.
result Polynomially-tailed losses improve performance in correcting distribution shift.
In this article, we study properly immersed complete noncompact submanifolds in a complete shrinking gradient Ricci soliton with weighted mean curvature vector bounded in norm. We prove that such a submanifold must have polynomial volume growth under some mild assumption on the potential function. On the other hand, if…
K-means clustering improved for robustness to outliers and distribution shifts.
problem K-means is brittle to outliers, distribution shifts, and limited samples.
method Developed a distributionally robust variant using Wasserstein-2 ball around the empirical distribution.
result Substantial gains in outlier detection and robustness to noise demonstrated.
Paper proves Harnack inequality for f-mean curvature flow.
problem Proving Harnack inequality for f-mean curvature flow. method Gradient flow of the weighed area functional with measure density function e−f. result Proves Li-Yau-Hamilton type Harnack estimate.
The mean-shift algorithm is a popular algorithm in computer vision and image processing. It can also be cast as a minimum gamma-divergence estimation. In this paper we focus on the "blurring" mean shift algorithm, which is one version of the mean-shift process that successively blurs the dataset. The analysis of the bl…
In this paper, we study how the mean shift algorithm can be used to denoise a dataset. We introduce a new framework to analyze the mean shift algorithm as a denoising approach by viewing the algorithm as an operator on a distribution function. We investigate how the mean shift algorithm changes the distribution and sho…
New insights into SGD and generalization via shift-curvature and bias-curvature mechanisms.
problem Understanding the role of curvature in generalization and how SGD affects it.
method Derivation of new SGD steady-state distribution and analysis of shift-curvature and bias-curvature mechanisms.
result Shift-curvature is a significant factor in test performance, especially for small SGD noise.
The paper proves rigidity results for self-shrinkers and surfaces with parallel weighted mean curvature.
problem Proving rigidity for self-shrinkers and surfaces with parallel weighted mean curvature.
method Using a new generalization of Cauchy's Theorem in complex analysis.
result Rigidity results for self-shrinkers and surfaces with parallel weighted mean curvature.