We establish multiparameter resolvent trace expansions for elliptic boundary value problems, polyhomogeneous both in the resolvent and the auxiliary parameter. The present analysis is rooted in the joint project with Matthias Lesch on multiparameter resolvent trace expansions on revolution surfaces with applications to…
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31 results for “multiparameter”
Unified framework connects deformation theory and derived categories for multiparameter persistence.
problem Algebraic complexity of multiparameter persistence modules hinders classification, stability, and interpretability.
method Combines deformation theory and derived categories to study multiparameter persistence geometrically.
result Unified conjecture relating interleaving distance to derived convolution metrics established.
Extends Morse-Forman theory to vector-valued functions for multiparameter persistence.
problem Computing multiparameter persistence with new tools and methods.
method Adapting Forman's theory to vectorial setting and using combinatorial topological dynamics.
result Established more general result for sublevel sets and found a way to induce Morse decomposition.
Stable density-based clustering via multiparameter persistence.
problem Density-based clustering stability to data perturbations.
method Degree-Rips construction, correspondence-interleaving distance, multiparameter stability analysis.
result Persistable pipeline yields stable, consistent density-based clustering.
New method for analyzing multiparameter persistence modules from smooth functions.
problem Analyzing multiparameter persistence modules from smooth functions.
method Generalized Morse theory applied to cobordism and Cerf theory.
result Complete description of persistence modules as direct sums of indecomposables.
The paper uses MDM theory to analyze multifiltering functions on simplicial complexes.
problem Understanding multifiltering functions through discrete Morse theory.
method Applying multiparameter discrete Morse theory to vector-valued multifiltering functions.
result Any multifiltering function can be approximated by a compatible MDM function.
Paper introduces stable vectorization for multiparameter PH using signed barcodes.
problem Lack of stable vectorization methods for multiparameter persistent homology.
method Signed barcodes as measures for stable vectorization of MPH.
result Stable feature vectors from signed barcodes improve performance in data science.
CuMPerLay vectorizes CMP for deep learning, improving image analysis.
problem Complex multifiltration structures hinder using CMP in deep learning.
method Introduces a new algorithm for vectorizing MP homologies of cubical complexes.
result Differentiable vectorization enables robust topological feature vectors for deep learning.
We prove the existence of multiparameter isospectral deformations of metrics on SO(n) and , SU(n) , and . For these examples, we follow a metric construction developed by Schueth who had given one-parameter families of isospectral metrics on orthogonal and unitary group…
The central problem of strip theory is the calculation of potential flowaround 2D sections. One particular method of solutions to this problem is conformal mapping of the body section to the unit circle over which a solution of potential flow is available. Here, a new multiparameter conformal mapping method is presente…
We present Natural Gradient Boosting (NGBoost), an algorithm for generic probabilistic prediction via gradient boosting. Typical regression models return a point estimate, conditional on covariates, but probabilistic regression models output a full probability distribution over the outcome space, conditional on the cov…
Random complexes can be embedded linearly if certain conditions on parameters are met.
problem Embedding random simplicial complexes linearly in Euclidean space.
method Established strict inequalities on parameters for linear embedding into R^(2d).
result Necessary and sufficient conditions for linear embedding of random complexes.
This study explores how choosing noninformative priors affects Thompson Sampling in multiparameter bandit models.
problem The optimality of Thompson Sampling (TS) in multiparameter bandit models depends on the choice of priors, especially when models are complex.
method The study extends regret analysis to uniform distributions and proposes a modified TS policy, TS-T, to achieve asymptotic optimality.
result Changing noninformative priors can significantly affect the expected regret in multiparameter bandit models.
We develop a theory of tubular neighborhoods for the lower strata in manifold stratified spaces with two strata. In these topologically stratified spaces, manifold approximate fibrations and teardrops play the role that fibre bundles and mapping cylinders play in smoothly stratified spaces. Applications include the cla…
Given a Coxeter system and a positive real multiparameter $\bq$, we study the "weighted -cohomology groups," of a certain simplicial complex associated to . These cohomology groups are Hilbert spaces, as well as modules over the Hecke algebra associated to and the multiparameter . The…
Develops 2-categorical methods for multi-parameter persistence.
problem Fundamental limitations of traditional persistence modules.
method 2-categorical structures to capture hierarchical interactions.
result New invariants effectively characterize multidimensional topological features.
Multiparameter regularization and aggregation in the context of polynomial functional regressionstat.ML
Study introduces a new method for multiple parameter regularization in polynomial functional regression.
problem Handling varying regularization parameters in polynomial functional regression.
method Developed a theoretically grounded algorithm for multiple parameter regularization and model aggregation.
result Promising results from evaluations on synthetic and real-world data.
New financial models use tempered stable subordination for better correlation dynamics.
problem Building financial models with better correlation dynamics.
method Introducing tempered stable Sato subordinators and additive inhomogeneous processes.
result The new process has time-dependent correlation, improving fit for financial data.
A new emulator connects observables directly from data.
problem Constructing fast and accurate surrogate models for robust predictions.
method Introduces Multiparameter Eigenvalue Problem (MEP) emulator trained with Eigenvector Continuation (EC) and Parametric Matrix Model (PMM) data.
result The MEP emulator can make predictions directly from observables to observables.
We study a class of exceptional minimal surfaces in spheres for which all Hopf differentials are holomorphic. Extending results of Eschenburg and Tribuzy \cite{ET0}, we obtain a description of exceptional surfaces in terms of a set of absolute value type functions, the -invariants, that determine the geometry of the…
We associate to a parametrized family of nonlinear Fredholm maps possessing a trivial branch of zeroes an {\it index of bifurcation} which provides an algebraic measure for the number of bifurcation points from the trivial branch. The index is derived from the index bundle of the linearization of the …
New machine learning methods for inference from simulated data.
problem Modeling score and likelihood ratio functions from sampled data.
method InferoStatic Networks (ISN), Kernel Score Estimation (KSE), Kernel Likelihood Ratio Estimation (KLRE).
result Improved inference methods for complex models.
Given a Coxeter system and a multiparameter of real numbers indexed by , one can define the weighted -cohomology groups and associate to them a nonnegative real number called the weighted -Betti number. We show that for ranges of depending on certain subgroups of , the …
We present a new multiparameter resolvent trace expansion for elliptic operators, polyhomogeneous in both the resolvent and auxiliary variables. For elliptic operators on closed manifolds the expansion is a simple consequence of the parameter dependent pseudodifferential calculus. As an additional nontrivial toy exampl…
Anisotropic min-max theory constructs stable minimal surfaces in 3-manifolds.
problem Constructing stable anisotropic minimal surfaces in 3-manifolds.
method Anisotropic min-max theory, removable singularity theorems.
result Constructs stable anisotropic minimal surfaces in 3-manifolds without singularities.
New stable minimal surfaces generalize classical Henneberg surface.
problem Finding new stable minimal surfaces in 3D.
method Generalized Henneberg surface with infinite families of complete, non-orientable surfaces.
result Infinite families of complete, finitely branched, non-orientable, stable minimal surfaces.
New tool for summarizing time-varying data shapes.
problem Understanding dynamic data shapes.
method Introducing crocker stacks for time-varying metric spaces.
result Demonstrated utility in parameter identification task.
The lasso has been studied extensively as a tool for estimating the coefficient vector in the high-dimensional linear model; however, considerably less is known about estimating the error variance in this context. In this paper, we propose the natural lasso estimator for the error variance, which maximizes a penalized …
PS-VAE extracts multi-parameter MRI biomarkers with uncertainty quantification.
problem Uncertainty in inverse problems limits clinical acceptance of quantitative MRI methods.
method Physics-Structured Variational Autoencoder (PS-VAE) integrating physics simulator and self-supervised learning.
result PS-VAE provides full covariance of inter-parameter correlations and accelerates multi-parametric MRI quantification.
Proposes a method to handle sparse multiway count data with false zeros using zero-truncated Poisson regression.
problem Handling sparse multiway count data corrupted by false zeros.
method Zero-truncated Poisson regression with tensor completion.
result Accurate estimation of multiway count data from approximately non-zero counts.
Paper stabilizes persistent homology rank functions for statistical inference.
problem Stability issues in persistent homology rank functions.
method Derive stability results for rank functions under FDA metrics.
result Rank functions stabilize, improving statistical inference.