In this paper, we establish the first variational formula and its Euler-Lagrange equation for the total -th mean curvature functional of a submanifold in a general Riemannian manifold for . As an example, we prove that closed complex submanifolds in compl…
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48 results for “total relative variation”
Volatility and Arbitrageq-fin.PM
New findings show strong arbitrage is not possible over arbitrary time horizons.
problem Whether strong arbitrage is possible over arbitrary time horizons.
method Analyzing the total relative variation of an equity market.
result Strong arbitrage is not possible over arbitrary time horizons under the stated condition.
We study the supremum of the total mean curvature on the boundary of compact, mean-convex 3-manifolds with nonnegative scalar curvature, and a prescribed boundary metric. We establish an additivity property for this supremum and exhibit rigidity for maximizers assuming the supremum is attained. When the boundary consis…
Estimating the level set of a signal from measurements is a task that arises in a variety of fields, including medical imaging, astronomy, and digital elevation mapping. Motivated by scenarios where accurate and complete measurements of the signal may not available, we examine here a simple procedure for estimating the…
Estimates TV distance between autoregressive models under different access models.
problem Estimating the total variation distance between two autoregressive distributions.
method Three access models: sample access, logit access, and noisy logit access; provides query complexity for each.
result Improved query complexity for estimating TV distance in autoregressive models.
This work analyzes Gibbs samplers for Bayesian hierarchical models without dimensionality constraints.
problem Analyzing convergence properties of Gibbs samplers for Bayesian hierarchical models.
method Using Bayesian asymptotics and total variation mixing times, the study provides dimension-free convergence results.
result Dimension-free convergence results for Gibbs samplers targeting hierarchical models under random data-generating assumptions.
Paper analyzes SGMs for learning sub-Gaussian distributions without dimensionality constraints.
problem Learning sub-Gaussian distributions in high dimensions with SGMs.
method Introduced complexity notion and proved approximation and generalization rates.
result SGMs can approximate target sub-Gaussian distributions in total variation with dimension-independent rate.
New framework estimates staged tree models using hierarchical clustering on the probability simplex.
problem Estimating staged tree models with context-specific dependencies.
method Hierarchical clustering on the probability simplex, using simplex-based divergences and linkage methods.
result Total Variation divergence with Ward.D2 linkage produces staged trees with better model fit, structure recovery, and computational efficiency.
Fast algorithms for learning a Gaussian under halfspace truncation with optimal sample complexitycs.DS
Optimal algorithm learns Gaussian under halfspace truncation with minimal samples.
problem Learning a Gaussian distribution truncated to an unknown halfspace.
method Efficient algorithm using samples and runtime dominated by empirical covariance matrix computation.
result Optimal sample and time complexity bounds for learning a Gaussian under halfspace truncation.
Algorithm learns affine transformations robustly from corrupted samples.
problem Learning affine transformations from corrupted samples.
method New geometric certificate and iterative improvement method.
result Total variation distance of between learned and original distributions.
The asymptotics of the -curvature and the second variation of analytic torsion on Teichmüller spacemath.DG
Analyzes curvature and torsion on Teichmüller space for large k.
problem Analyzing curvature and torsion on Teichmüller space for large k.
method Examines the curvature and torsion of specific metrics on Teichmüller space.
result The second variation of analytic torsion satisfies a specific asymptotic behavior.
Ideas from the image processing literature have recently motivated a new set of clustering algorithms that rely on the concept of total variation. While these algorithms perform well for bi-partitioning tasks, their recursive extensions yield unimpressive results for multiclass clustering tasks. This paper presents a g…
Total variation denoising improves image quality adaptively.
problem Improving image quality from noisy data.
method Total variation regularization for image denoising.
result Denoised images converge to true images at a parametric rate.
The paper studies curves in Riemannian manifolds using total variation flow.
problem Analyzing the evolution of curves in Riemannian manifolds using total variation.
method Defining and proving the existence of strong solutions to the flow equations, showing variational equality, and proving convergence.
result Strong solutions converge to a constant map in finite time for non-positive sectional curvature.
Optimal Pre-Processing to Achieve Fairness and Its Relationship with Total Variation Barycentercs.IT
Optimal pre-processing reduces disparate impact by minimizing total variation distance.
problem Achieving fairness in data outputs based on protected attributes.
method Using pre-processing to enforce fairness, minimizing total variation distance between pre-processed and original data distributions.
result The problem of fairness can be formulated as a linear program, efficiently solvable.
New method improves tensor completion by selectively preserving important elements.
problem Recovering corrupted high-dimensional tensor data with missing entries and noise.
method Tensor weighted correlated total variation (TWCTV) regularizer with ADMM algorithm.
result Superior performance in image completion, denoising, and background subtraction tasks.
This work provides guaranteed bounds on the total variation distance for univariate mixtures.
problem Lack of closed-form expressions for total variation distance between mixtures.
method Two methods: information monotonicity for lower bounds and geometric envelopes for upper bounds.
result Demonstrated tightness of bounds on Gaussian, Gamma, and Rayleigh mixtures.
We show a very simple and general total second variation formula for Perelman's -functional at arbitrary points in the space of Riemannian metrics. Moreover we perform a study of the properties of the variations of Kähler structures. We deduce a quite simple and general total second variation formula for P…
Total variation minimization clusters partially labeled data points.
problem Clustering partially labeled data points in stochastic block models.
method Total variation minimization as a clustering method.
result Total variation minimization allows for accurate clustering under certain model parameters.
We derive variational formulas for the total Q-prime curvature under the deformation of strictly pseudoconvex domains in a complex manifold. We also show that the total Q-prime curvature agrees with the renormalized volume of such domains with respect to the complete Einstein-Kähler metric. In the appendix, by Rod Gove…
Study shows improper learning can outperform proper learning in misspecified models.
problem Misspecification in probabilistic prediction models.
method Investigates the performance of proper and improper learning strategies in misspecified models.
result Improper learning can achieve lower regret compared to proper learning, especially in high-dimensional settings.
We find the maximum regularization parameter for total-variation denoising.
problem Finding the maximum regularization parameter for anisotropic total-variation denoising.
method Established a closed form expression for the one-dimensional case and an upper-bound for the two-dimensional case using the pseudo-inverse of the divergence.
result The maximum regularization parameter is crucial for optimal parameter tuning and can be computed efficiently.
The paper derives oracle inequalities for estimators with fast and slow rates.
problem Developing fast and slow oracle inequalities for estimators.
method Direct study of analysis estimator and adaptation of Dalalyan, Hebiri and Lederer's arguments.
result Constant-friendly rates for (square root) total variation regularized estimators over graphs.
Mass in relativity linked to polyhedra geometry.
problem Understanding ADM mass in general relativity.
method Relating ADM mass to the total mean curvature and defect of dihedral angles of Riemannian polyhedra.
result Expressed -dimensional mass as an integral of geometric quantities.
Measure homology was introduced by Thurston in his notes about the geometry and topology of 3-manifolds, where it was exploited in the computation of the simplicial volume of hyperbolic manifolds. Zastrow and Hansen independently proved that there exists a canonical isomorphism between measure homology and singular hom…
We consider the problem of estimating a function defined over locations on a -dimensional grid (having all side lengths equal to ). When the function is constrained to have discrete total variation bounded by , we derive the minimax optimal (squared) estimation error rate, parametrized by …
Construct Kähler metrics with negative holomorphic bisectional curvature on compact relative Kähler fibrations.
problem Constructing Kähler metrics with negative holomorphic bisectional curvature on compact relative Kähler fibrations.
method Constructing Kähler metrics with negative holomorphic bisectional curvature on compact relative Kähler fibrations using specific curvature conditions.
result Explicit construction of Kähler metrics with negative holomorphic bisectional curvature on compact relative Kähler fibrations.
Study improves oracle inequality for tree graphs using total variation regularization.
problem Improving oracle inequality for tree graphs with total variation regularization.
method Generalized Fused Lasso result to tree graphs, using harmonic mean of distances.
result Proved a lower bound on compatibility constant for total variation penalty.
Study variational formulas for distribution geometry, finding critical metrics.
problem Analyzing the total mixed scalar curvature of a distribution.
method Developed variational formulas for extrinsic geometry, solved Euler-Lagrange equations.
result Found critical metrics related to various geometric properties.
Study on curves in Riemannian surfaces, focusing on total intrinsic curvature.
problem Understanding the total intrinsic curvature of irregular curves in Riemannian surfaces.
method Weak notion of parallel transport, bounded variation of angle, energy functional analysis.
result Total intrinsic curvature of irregular curves matches an energy functional.
Stability of Pareto-Zipf Law in Non-Stationary Economiescond-mat.stat-mech
Generalized Lotka-Volterra (GLV) models extending the (70 year old) logistic equation to stochastic systems consisting of a multitude of competing auto-catalytic components lead to power distribution laws of the (100 year old) Pareto-Zipf type. In particular, when applied to economic systems, GLV leads to power laws in…
Paper introduces Wasserstein total correlation for disentangled representation learning.
problem Learning disentangled representations from data.
method Adversarial training of a critic to estimate Wasserstein total correlation in variational and Wasserstein autoencoders.
result Proposed method achieves comparable disentanglement performance with less reconstruction loss.
Paper proposes a new method to minimize submodular functions with fewer calls to simpler oracles.
problem Minimizing the sum of submodular set functions with limited information.
method Introduces a modified convex problem requiring constrained total variation oracles that can be solved with fewer calls to minimization oracles.
result Shows significant reduction in the number of calls to minimization oracles.
We refine VAEs to learn disentangled representations without extra hyperparameters.
problem Learning disentangled representations in VAEs.
method Decompose evidence lower bound, propose β-TCVAE, mutual information gap (MIG).
result Total correlation and disentanglement are strongly related.
Total variation and mean curvature flows on a Lie group quotient enhance and denoise crossing structures.
problem Preserving crossing curvilinear structures in image enhancement and denoising.
method Lifting images to the homogeneous space , applying PDEs for TVF and MCF, and using locally optimal differential frames.
result Better preservation of bundle boundaries and angular sharpness in fiber orientation densities at crossings compared to data-driven diffusions.
We decrease the mean curvature and area of a variable surface with a fixed boundary by iterating a few times through a curvature-based variational algorithm. For a boundary with a known minimal surface, starting with a deliberately chosen non-minimal surface, we achieve up to 65 percent of the total possible decr…
SaR-SVM-STV improves hyperspectral image classification with shape-adaptive reconstruction and denoising.
problem Classifying hyperspectral images with limited labeled data.
method Shape-adaptive Reconstruction (SaR) for pixel preprocessing, SVM for probability estimation, and Smoothed Total Variation (STV) for denoising.
result SaR-SVM-STV outperforms SVM-STV with fewer labeled data.
The paper establishes prediction bounds for trend filtering with higher order total variation penalties.
problem Estimating signals with jumps of varying orders using total variation regularization.
method Combining oracle inequalities and interpolating vectors to bound effective sparsity.
result The -penalty on order differences allows adaptive estimation for .
Improved DNN robustness to adversarial attacks using data-dependent activation and total variation minimization.
problem Improving Deep Neural Network robustness to adversarial attacks.
method Data-dependent activation function and total variation minimization.
result Robust accuracy of adversarially trained ResNet20 increased from ~46% to ~69% under IFGSM attack.
Variational properties of the total inverse mean curvature in the plane under boundary constraintsmath.DG
Study variational properties of curves in half-plane with area constraints.
problem Characterize critical points of inverse mean curvature.
method Variational analysis of curves with boundary constraints.
result Existence and stability of critical points with prescribed area.
Study variations of Riemannian submersions to maintain geodesic fibers and positive curvatures.
problem Maintain geodesic fibers and positive sectional curvatures in Riemannian submersions.
method Vary Riemannian metrics while keeping fibers totally geodesic and horizontal distribution fixed.
result Conditions for making sectional curvatures positive and existence of fat submersions.
Deep nets' complexity and risk are quantified using total path variation.
problem Quantifying the complexity and risk of deep neural networks.
method Using total path variation, the paper establishes relationships between network complexity and statistical risk.
result The statistical risk and metric entropy of deep nets are proportional to the total variation of path weights.
This paper improves total variation based convex clustering for better data clustering.
problem Improving data clustering methods, especially for general data.
method Proposes a weighted sum-of--norm relating convex model for total variation based clustering.
result Established exact clustering property applicable to general data, sharper than existing results.
This paper describes a new online convex optimization method which incorporates a family of candidate dynamical models and establishes novel tracking regret bounds that scale with the comparator's deviation from the best dynamical model in this family. Previous online optimization methods are designed to have a total a…
Estimates parameters of interconnected linear systems using total variation penalization.
problem Joint estimation of parameters in interconnected linear dynamical systems.
method Total variation penalized least-squares estimator.
result The MSE goes to zero as the number of systems increases, even with constant trajectory length.
In this paper we discuss an extension of Perelman's comparison for quadrangles. Among applications of this new comparison theorem, we study the equidistance evolution of hypersurfaces in Alexandrov spaces with non-negative curvature. We show that, in certain cases, the equidistance evolution of hypersurfaces become tot…
Sharp inequality between TV and Hellinger distances for Gaussian mixtures.
problem Understanding the relationship between total variation and Hellinger distances for Gaussian mixtures.
method Established a general upper bound on Hellinger distance in terms of TV distance raised to a power, demonstrating sharpness with specific examples.
result The Hellinger distance between two Gaussian mixtures is bounded by the TV distance raised to a power , where is of order .
Discriminative Estimation of Total Variation Distance: A Fidelity Auditor for Generative Datastat.ML
Paper proposes a method to estimate total variation distance for synthetic data fidelity.
problem Assessing the fidelity of synthetic data generated by AI.
method Discriminative approach to estimate total variation distance between two distributions.
result Estimation of total variation distance reduces to quantifying Bayes risk in classification.