Extends small-ball method to broader class without uniform small-ball condition.
problem Obtaining high probability lower bounds on quadratic empirical processes.
method Extends small-ball method to allow broader class without uniform small-ball condition, motivated by tournament learning.
result Obtains high probability, almost-isometric lower bound on quadratic empirical process.
Paper develops error rates for physics-informed learning, comparing it to data-driven methods.
problem Understanding the trade-off between soft penalties and hard constraints in PISL.
method Develops complexity-dependent error rates using the small-ball method.
result Physics-informed estimators have comparable error rates to hard constrained methods, differing only by constants.
Study spectral properties of sub-Laplacians in Carnot groups.
problem Spectral properties of sub-Laplacians in Carnot groups.
method Proved pure point spectrum and spectral gap; applied to small ball problem and heat content.
result Proved existence of spectral gap and pure point spectrum.
OLS estimator nearly optimally identifies linear systems from single trajectory.
problem Identifying linear dynamical systems from a single observed trajectory.
method Generalized small-ball method for dependent data, avoiding mixing-time arguments.
result OLS estimator nearly matches minimax optimal performance for linear systems.
In a compact orbifold, for small prescribed volume, an isoperimetric region is close to a small metric ball; in a Euclidean orbifold, it is a small metric ball.
Identifies bilinear systems from a single trajectory with optimal sample complexity.
problem Learning bilinear systems from a single trajectory of states and inputs.
method Uses a mild marginal mean-square stability assumption and martingale small-ball condition.
result Sample complexity and statistical error rates are optimal.
Formula connects curvature to volume in special geometric spaces.
problem Deriving formulas for curvature in specific geometric spaces.
method Used strong locality of Laplacian and eigenfunction approximation.
result Proved integral type Gauss-Green formula linking curvature to volume.
This paper is a starting point towards computing the Hausdorff dimension of submanifolds and the Hausdorff volume of small balls in a sub-Riemannian manifold with singular points. We first consider the case of a strongly equiregular submanifold, i.e., a smooth submanifold N for which the growth vector of the distributi…
We solve the classical Dirichlet problem for a general complex Hessian equation on a small ball in $\bC^n$. Then, we show that there is a continuous solution, in pluripotential theory sense, to the Dirichlet problem on compact Hermitian manifolds with boundary that equipped locally conformal Kähler metrics, provided a …
Study calculates volume of small sub-Riemannian balls in 3D contact manifolds.
problem Computing the volume of small sub-Riemannian balls in 3D contact manifolds.
method Asymptotic expansion and geometric invariants of the sub-Riemannian structure.
result Expressed first geometric coefficients in terms of sub-Riemannian structure invariants.
We obtain sharp bounds on the performance of Empirical Risk Minimization performed in a convex class and with respect to the squared loss, without assuming that class members and the target are bounded functions or have rapidly decaying tails. Rather than resorting to a concentration-based argument, the method used her…
Method approximates covariance ellipsoid using random slabs or ellipsoids.
problem Approximating the covariance ellipsoid of a random vector.
method Constructing approximations using random slabs or ellipsoids generated from data.
result Approximations can be constructed with a sample size of N=c1dη−4log(2/η) or N=c1dη−2log(2/η) under minimal assumptions. CR structure on S³ with non-compact solutions to CR Yamabe problem.
problem Existence of non-compact solutions to CR Yamabe problem.
method Deforming standard CR structure of S³, using Lyapunov-Schmidt method.
result Existence of a blowing-up sequence of solutions.
On Kahler manifolds with Ricci curvature lower bound, assuming the real analyticity of the metric, we establish a sharp relative volume comparison theorem for small balls. The model spaces being compared to are complex space forms, i.e, Kahler manifolds with constant holomorphic sectional curvature. Moreover, we give a…
Asymptotic factorizations for the small-ball probability (SmBP) of a Hilbert valued random element X are rigorously established and discussed. In particular, given the first d principal components (PCs) and as the radius ε of the ball tends to zero, the SmBP is asymptotically proportional to (a) the joi…
We develop an analog of harmonic replacement in the gauge theory context. The idea behind harmonic replacement dates back to Schwarz and Perron. The technique, as introduced by Jost and further developed by Colding and Minicozzi, involves taking a map v:Σ→M defined on a surface Σ and replacing its values on…
Condorcet's Jury Theorem has been invoked for ensemble classifiers to indicate that the combination of many classifiers can have better predictive performance than a single classifier. Such a theoretical underpinning is unknown for consensus clustering. This article extends Condorcet's Jury Theorem to the mean partitio…
Constructs metrics with Q-curvature on manifolds with singularities.
problem Positive singular Q-curvature problem on compact manifolds with punctures.
method One-parameter family solutions, perturbation methods, gluing techniques, linearized operator mapping properties.
result One-parameter family of solutions constructed for positive Q-curvature.
This study improves estimation of locally stationary functional time series using NW method.
problem Accurately capturing time-dependence in locally stationary functional time series with time-varying covariates.
method Nadaraya-Watson (NW) estimation procedure for the conditional distribution of LSFTS.
result Established convergence rates of NW estimator for LSFTS with respect to Wasserstein distance.
We prove a new kind of estimate that holds on any manifold with lower Ricci bounds. It relates the geometry of two small balls with the same radius, potentially far apart, but centered in the interior of a common minimizing geodesic. It reveals new, previously unknown, properties that all generalized spaces with a lowe…
New method uses robust estimators for Newton's method in empirical risk minimization.
problem Improving robustness in empirical risk minimization.
method Robust Newton's method with gradient and Hessian replaced by robust estimators.
result Faster convergence rates in high-dimensional settings.
Efficient algorithm predicts unknown linear systems with long-term memory.
problem Predicting unknown and partially observed linear dynamical systems with long-term memory.
method Bounding the generalized Kolmogorov width of the Kalman filter model using spectral methods and conducting tight convex relaxation.
result Competes with Kalman filter in hindsight with only logarithmic regret.
New learning rates derived for Tikhonov-regularized problems without kernel assumptions.
problem Learning rates for Tikhonov-regularized learning problems.
method Minimax adaptive rates derived using Fourier isocapacitary condition and interpolation theory.
result Derivation of minimax adaptive rates without requiring kernel assumptions.
The paper proves stability of curvature bounds in geometric analysis.
problem Stability of local Riemannian Ricci curvature bounds under convergence.
method Gromov-Hausdorff convergence, Lagrangian approach, heat flow, weak gradients, Evolution Variational Inequality.
result Almost everywhere existence of Euclidean weak tangents.
In this short note we show that the lower bounds of Mangoubi on the inner radius of nodal domains can be improved for quantum ergodic sequences of eigenfunctions, according to a certain power of the radius of shrinking balls on which the eigenfunctions equidistribute. We prove such improvements using a quick applicatio…
Spectrahedral regression fits convex functions via a non-convex optimization problem.
problem Fitting convex functions to data sets.
method Fitting a spectrahedral function (maximum eigenvalue of an affine matrix expression) to the data via an alternating minimization algorithm.
result The alternating minimization algorithm converges geometrically to a small ball around the optimal parameter.
The study connects curvature to the elastic energy of non-Euclidean thin bodies.
problem Understanding the elastic energy scaling of non-Euclidean thin bodies.
method Calculating the Γ-limit for the elastic energies of small balls, proving the scaling is \(h^4\).
result The natural scaling for non-Euclidean rods is \(h^4\), confirming previous claims.
The study sharpens local Bernstein estimates for Laplace eigenfunctions on compact manifolds.
problem Understanding local growth properties of Laplace eigenfunctions on compact Riemannian manifolds.
method Refined Donnelly-Fefferman method based on L2--Carleman estimates, combined with elliptic regularity and patching of local Carleman estimates. result Almost sharp local Lp--Bernstein inequalities for p∈[1,∞]. Paper revisits set membership estimation for linear systems with relaxed disturbance bounds.
problem Set membership estimation for linear systems with disturbances bounded by convex sets.
method Adopted block-martingale small-ball condition and random perturbed control policies to establish convergence rates.
result Established convergence rates for disturbances bounded by general convex sets.
New findings on Helmholtz equation solutions show exponential growth in constant for three ball inequality.
problem Analyzing solutions of Helmholtz equation on different manifolds.
method Examining the three ball inequality for solutions of Helmholtz equation on Rn, Sn, or Hn. result The constant in the three ball inequality grows exponentially with the wave number.
Local mass perspective on Bayesian inference
problem Measuring distributional discrepancy in Bayesian inference
method Introducing Mass Index and Regularised Extended KL
result Proving inequalities for comparing local small-ball masses
Let (M,g) be a compact Riemannian manifold with dimension n > 2. The Yamabe problem is to find a metric with constant scalar curvature in the conformal class of g, by minimizing the total scalar curvature. The proof was completed in 1984. Suppose (M',g') and (M'',g'') are compact Riemannian n-manifolds with constant sc…
The Hidden Markov Model (HMM) is one of the mainstays of statistical modeling of discrete time series, with applications including speech recognition, computational biology, computer vision and econometrics. Estimating an HMM from its observation process is often addressed via the Baum-Welch algorithm, which is known t…
Let (M,w) be a compact symplectic 2n-manifold, and g a Riemannian metric on M compatible with w. For instance, g could be Kahler, with Kahler form w. Consider compact Lagrangian submanifolds L of M. We call L Hamiltonian stationary, or H-minimal, if it is a critical point of the volume functional under Hamiltonian defo…
Max-affine regression tackles estimating coefficients of up to k affine functions.
problem Estimating coefficients of up to k affine functions in a high-dimensional setting.
method Alternating minimization (AM) algorithm for non-convex least squares objective, with spectral method and random search initialization.
result AM algorithm converges geometrically to near-optimal coefficients with high probability.
We start by a review of the chronology of mathematical results on the Dirichlet-to-Neumann map which paved the way towards the physics of transformational acoustics. We then rederive the expression for the (anisotropic) density and bulk modulus appearing in the pressure wave equation written in the transformed coordina…
Exact minimax risk derived for linear prediction with sample covariance analysis.
problem Understanding the minimax risk in linear prediction under various covariate distributions.
method Exact minimax risk analysis, leveraging statistical leverage scores and PAC-Bayes techniques.
result The minimax risk is of order d/(n−d+1) for any covariate distribution, nearly matching the risk for Gaussian design. The paper proves convergence of 4-manifolds with almost vanishing curvature.
problem Proving convergence of Riemannian 4-manifolds with vanishing curvature.
method Used L2-curvature flow and smoothing techniques. result Proves convergence to flat or Einstein manifolds.
In this article, we investigate large sample properties of model selection procedures in a general Bayesian framework when a closed form expression of the marginal likelihood function is not available or a local asymptotic quadratic approximation of the log-likelihood function does not exist. Under appropriate identifi…
New metrics help predict Brownian motion on surfaces and higher dimensions.
problem Predicting Brownian motion on complex surfaces and higher dimensions.
method Developed new metrics (Uniform Drainage Metric) for surfaces and higher dimensions.
result Uniform Drainage Metric predicts Brownian motion's narrow escape time consistently.
Local LMO optimizes constrained problems using local linear minimization.
problem Constrained optimization problems with complex feasible sets.
method Designs a new projection-free gradient method using local linear minimization.
result Transfers convergence rates of Projected Gradient Descent to the projection-free world.
Improved iterative methods for risk parity portfolio weights.
problem Solving for portfolio weights in risk parity allocation.
method Enhanced CCD and Newton methods, including a rescaling step and improved initial guess.
result Improved CCD method is the best, three times faster with 40% fewer iterations.
We describe a novel optimization method for finite sums (such as empirical risk minimization problems) building on the recently introduced SAGA method. Our method achieves an accelerated convergence rate on strongly convex smooth problems. Our method has only one parameter (a step size), and is radically simpler than o…
A new method combines Laplace and Variational Bayes for scalable inference.
problem Complex models and large datasets make exact inference infeasible.
method Low-Rank Variational Bayes Correction (VBC) using Laplace method and Variational Bayes correction in a lower dimension.
result The method ensures scalability in both model complexity and data size.
Unified framework for model explanation methods based on feature removal.
problem Unclear relationships and preferences among various model explanation methods.
method Characterizes removal-based explanations along three dimensions.
result Unified 26 existing methods, including widely used approaches.
This work reviews and evaluates methods for predicting prediction intervals in regression problems.
problem Calibration of prediction intervals in regression problems.
method Four classes of methods: Bayesian, ensemble, direct interval estimation, and conformal prediction.
result Conformal prediction can be used as a general calibration procedure.
Derives kernel PCA with Nyström method for scalability.
problem Scalability of kernel PCA.
method Nyström method for kernel PCA.
result Provides scalable alternative to full kernel PCA.
In this paper, the author considers the numerical computation of CVA for large systems by Mote Carlo methods. He introduces two types of stochastic mesh methods for the computations of CVA. In the first method, stochastic mesh method is used to obtain the future value of the derivative contracts. In the second method, …