Deep neural networks with adversarial training achieve sup-norm convergence for nonparametric regression.
problem Achieving sup-norm convergence for deep neural network estimators in nonparametric regression.
method Developed an adversarial training scheme to address the sup-norm convergence issue.
result Deep neural network estimators achieve optimal sup-norm convergence with the proposed adversarial training.
New bounds on manifold Betti numbers derived from semigroup norms.
problem Estimating the first Betti number of compact Riemannian manifolds.
method Birman-Schwinger principle and Schatten norm estimates for semigroup differences, without ultracontractivity assumptions.
result Explicit bounds on Betti numbers depend on Ricci tensor norms.
Proposes a new regression method using Lp-norms for non-Gaussian noise.
problem Non-Gaussian noise in residuals affects the performance of local least squares regression.
method Introduces local polynomial Lp-norm regression, replacing weighted least squares with weighted Lp-norm estimation. result Demonstrates superior performance over local least squares in one-dimensional data and higher dimensions.
New method improves signal estimation by convexifying ℓ0-norm constraints.
problem Signal estimation with sparsity and smoothness priors.
method Iterative convex conic quadratic relaxations exploiting ℓ0-norm and smoothness terms. result Significantly better estimators than ℓ1-norm approaches and interpretable parameters. New methods for estimating panel regression models with interactive fixed effects.
problem Estimating panel regression models with interactive fixed effects.
method Nuclear norm regularization and minimization of convex objective functions.
result Consistent estimators with computational advantages over least squares.
Using the ℓ1-norm to regularize the estimation of the parameter vector of a linear model leads to an unstable estimator when covariates are highly correlated. In this paper, we introduce a new penalty function which takes into account the correlation of the design matrix to stabilize the estimation. This norm, ca…
We propose a Generalized Dantzig Selector (GDS) for linear models, in which any norm encoding the parameter structure can be leveraged for estimation. We investigate both computational and statistical aspects of the GDS. Based on conjugate proximal operator, a flexible inexact ADMM framework is designed for solving GDS…
Quantum SU(n) representations are asymptotically faithful with norm estimates.
problem Asymptotic faithfulness of quantum SU(n) representations of mapping class groups.
method Peak sections in Kodaira embedding and parallell transport of projective connection.
result Norm estimates and asymptotic faithfulness of quantum SU(n) representations.
This work improves trace norm regularization for multi-task learning with limited data.
problem Learning from few samples across multiple tasks.
method Trace norm regularization for a linear shared representation model.
result First estimation error bound for trace norm regularized estimator with scarce data.
Optimal estimates derived for residual networks' generalization error.
problem Estimating the generalization error of residual networks.
method Derives optimal a priori estimates using a weighted path norm.
result Optimal error estimates are comparable to Monte Carlo error rates.
The study analyzes robustness of estimators in linear models with adversarial errors.
problem Analyzing robustness of estimators in linear models with adversarial errors.
method Develops a general theory for minimum norm interpolating estimators and RERM in linear models without conditions on errors.
result Quantitative bound for the prediction error relating it to Rademacher complexity, norm of minimum norm interpolator of errors, and subdifferential size.
New algorithms estimate matrix norms without matrix multiplication.
problem Estimating matrix norms efficiently in a matrix-free setting.
method Randomized algorithms based on Hutchinson's estimator modifications.
result Oracle complexity bounds for two-to-infinity and one-to-two norms.
The study analyzes perturbation bounds for HOSVD and introduces new tensor denoising estimators.
problem Perturbation analysis of HOSVD under random noise.
method Developed sup-norm perturbation bounds and introduced new tensor denoising estimators.
result Sharp deviation bounds in the sup-norm for singular subspaces and fast convergence rate for tensor denoising.
Estimates for geodesics on hyperbolic tori improve previous bounds.
problem Counting simple closed geodesics on hyperbolic tori.
method McShane-Rivin norm balls and Markoff numbers.
result The number of simple closed geodesics of length exactly L≥2 is at most CX(logL)2. Analysis of non-asymptotic estimation error and structured statistical recovery based on norm regularized regression, such as Lasso, needs to consider four aspects: the norm, the loss function, the design matrix, and the noise model. This paper presents generalizations of such estimation error analysis on all four aspe…
In this paper, we consider low rank matrix estimation using either matrix-version Dantzig Selector A^λd or matrix-version LASSO estimator A^λL. We consider sub-Gaussian measurements, i.e., the measurements X1,…,Xn∈Rm×m have i.i.d. sub-Gaussian entries. Suppose $\textrm…
Extended Gauss-Markov theorem for linear estimation with bounded bias.
problem Linear estimation with bounded bias operator.
method Derive optimal estimator formulas for Nuclear and Spectral norms, analyze generalization error.
result Cross-validated Nuclear and Spectral regressors outperform Ridge regression in simulations.
Improved bounds for discrete probability distribution estimation under the ℓ∞ norm.
problem Estimating discrete probability distributions under the ℓ∞ norm with improved bounds.
method Minimax bounds in expectation and high-probability tail bounds.
result Resolved open questions posed in Kontorovich and Painsky (JMLR, 2025), including a fully empirical tightest risk bound and identifying the worst-case extremal distribution.
This work shows how penalising bias terms in norm regularisation leads to sparse solutions.
problem Understanding the relation between parameter norm regularization and the sparsity of neural network solutions.
method Analyzes one hidden ReLU layer networks with unidimensional data, showing the norm required for function representation and the importance of the bias term's norm.
result Penalising the bias terms in regularisation leads to sparse solutions, enforcing the uniqueness and sparsity of the minimal norm interpolator.
Improved 2-bit covariance estimator with reduced operator norm error and no tuning needed.
problem Improving 2-bit covariance estimation with reduced operator norm error and no tuning needed.
method Proposed a new 2-bit covariance matrix estimator using triangular dithering scales.
result Improved operator norm error rate that depends on effective rank of covariance matrix, closing theoretical gap.
Data-driven optimization improves mean-variance portfolios by penalizing norms.
problem Estimation error in mean-variance optimization.
method Augment MVO with norm penalties, use neural networks for optimization, and compute derivatives implicitly.
result Data-driven optimization reduces portfolio risk compared to standard MVO.
Complexity measures for neural nets with general activations using path-based norms.
problem Control complexity of neural networks with arbitrary activation functions.
method Approximate general activations with ReLU networks and derive path-based norms for complexity control.
result Preliminary analyses of function spaces and regularized estimators.
This paper studies the matrix completion problem under arbitrary sampling schemes. We propose a new estimator incorporating both max-norm and nuclear-norm regularization, based on which we can conduct efficient low-rank matrix recovery using a random subset of entries observed with additive noise under general non-unif…
Estimates for the norm of the second fundamental form, ∣A∣, play a crucial role in studying the geometry of surfaces. In fact, when ∣A∣ is bounded the surface cannot bend too sharply. In this paper we prove that for an embedded geodesic disk with bounded L2 norm of ∣A∣, ∣A∣ is bounded at interior points, pro…
Estimates intersection pairing in hyperbolic 4-manifolds.
problem Estimating intersection pairing in hyperbolic 4-manifolds.
method Using Thurston norms of homology classes.
result Proved an estimate on intersection pairing.
We consider in this paper the problem of noisy 1-bit matrix completion under a general non-uniform sampling distribution using the max-norm as a convex relaxation for the rank. A max-norm constrained maximum likelihood estimate is introduced and studied. The rate of convergence for the estimate is obtained. Information…
New neural network architecture preserves gradient norms to approximate Lipschitz functions.
problem Training neural networks with strict Lipschitz constraints to ensure robustness and generalization.
method Identified gradient norm preservation as a necessary property, combined with norm-constrained weight matrices and GroupSort activation function.
result Norm-constrained GroupSort architectures can approximate Lipschitz functions and achieve tighter Wasserstein distance estimates.
Study automorphic forms on bounded domains, proving spanning results and estimating norms.
problem Understanding automorphic forms on bounded symmetric domains and their norms.
method Proving spanning results for vector-valued Poincaré series and analyzing holomorphic automorphic forms.
result Found different asymptotic behaviors of norms for certain submanifolds.
The paper estimates matrix-valued functions with low rank using penalized estimators.
problem Estimating matrix-valued functions with low rank from incomplete data.
method Innovative nuclear norm penalized local polynomial estimator and bias-reducing kernels.
result Optimal rates of convergence for various matrix norms.
Study pinches curvature under Laplacian G_2 flow, proving Weyl tensor norm blows up.
problem Pinching estimate on traceless Ricci curvature under Laplacian G_2 flow.
method Derive pinching estimate in terms of scalar curvature and Weyl tensor norm.
result Weyl tensor norm blows up at least at a certain rate under bounded scalar curvature.
Alpha-norm regularization simplifies marketing demand forecasting.
problem Ultra high-dimensional problems in demand estimation and forecasting.
method Nonconvex alpha-norm objective with coordinate descent and proximal operators.
result Alpha-norm regularization provides accurate out-of-sample estimates for promotion effects.
The study analyzes convergence rates for sparse pivotal estimators in high-dimensional regression.
problem Sparse pivotal estimation in high-dimensional regression problems.
method Theoretical analysis and comparison of non-smooth + non-smooth optimization problems, including smoothing techniques.
result Minimax sup-norm convergence rates for square-root Lasso-type estimators are derived.
Estimates the dual Thurston norm for foliations on negative curvature 3-manifolds.
problem Bounding the dual Thurston norm of foliations on 3-manifolds of negative curvature.
method Uses constants like injectivity radius, volume, curvature, and mean curvature of foliation leaves to estimate the dual Thurston norm.
result Provides an upper bound estimate on the dual Thurston norm of the Euler class of a foliation.
Estimates Markov chain parameters from a single long sequence, analyzing complexity based on mixing properties.
problem Estimating parameters of a discrete-state Markov chain kernel from a single long sequence of observations.
method Characterizes minimax sample complexity in finite and countably infinite cases, focusing on mixing properties.
result Sample complexity is governed by mixing properties, with finite-sample estimators available for finite-state cases.
Paper improves distributed mean estimation and variance reduction without relying on input norm.
problem Distributed mean estimation and variance reduction with large input norms.
method Quantization and lattice theory connection for improved error bounds.
result Output error bounds depend only on input distance, not norm.
Paper introduces structured sparsity estimators for Generalized Linear Models.
problem Estimating structured sparsity in GLMs with debiased estimators.
method Extends Stucky and van de Geer's results to GLMs with structured sparsity.
result Proves oracle inequalities for structured sparsity estimators in GLMs.
We study the density estimation problem with observations generated by certain dynamical systems that admit a unique underlying invariant Lebesgue density. Observations drawn from dynamical systems are not independent and moreover, usual mixing concepts may not be appropriate for measuring the dependence among these ob…
In this paper we present several curvature estimates and convergence results for solutions of the Ricci flow. The curvature estimates depend on smallness of certain local space-time integrals of the norm of the Riemann curvature tensor, while the convergence results require finiteness of space-time integrals of the nor…
The paper analyzes methods for estimating linear functionals from observational data, proving upper bounds and showing optimal procedures.
problem Estimating linear functionals from observational data in causal inference and bandit literature.
method Two-stage procedures that first estimate treatment effect function, then use it to estimate the linear functional.
result Proves non-asymptotic upper bounds on mean-squared error for two-stage procedures and shows instance-dependent optimality.
Study calculates stable norm of slit tori using Farey sequence.
problem Computing the stable norm of slit tori.
method Explicit computations using the Farey sequence and gluing slit tori.
result Estimates the asymptotic counting of simple homology classes.
Paper optimizes private PCA for covariance estimation in statistics.
problem Private estimation of covariance matrices and principal components.
method Developed differentially private estimators for spiked covariance model.
result Established minimax rates of convergence for principal components and covariance matrix estimation.
A new algorithm estimates mean adaptively to covariance, faster and more flexible than existing methods.
problem Estimating mean of a distribution with unknown covariance efficiently and privately.
method Adaptive differentially private algorithm with optimal convergence rates and near-linear sample complexity.
result Achieves optimal rates of convergence with respect to the Mahalanobis norm ∣∣⋅∣∣Σ. SpINNEr uses matrix regression to analyze brain connectivity, improving accuracy over other methods.
problem Analyzing multi-dimensional data like brain imaging arrays using traditional scalar regression methods.
method SpINNEr applies matrix regression with nuclear norm and lasso norms to encourage low rank and sparse solutions.
result SpINNEr outperforms other methods in estimating brain connectivity, especially in well-connected regions.
The spectral k-support norm enjoys good estimation properties in low rank matrix learning problems, empirically outperforming the trace norm. Its unit ball is the convex hull of rank k matrices with unit Frobenius norm. In this paper we generalize the norm to the spectral (k,p)-support norm, whose additional para…
This paper aims at achieving a simultaneously sparse and low-rank estimator from the semidefinite population covariance matrices. We first benefit from a convex optimization which develops l1-norm penalty to encourage the sparsity and nuclear norm to favor the low-rank property. For the proposed estimator, we then p…
We study the adaptive estimation of copula correlation matrix Σ for the semi-parametric elliptical copula model. In this context, the correlations are connected to Kendall's tau through a sine function transformation. Hence, a natural estimate for Σ is the plug-in estimator Σ^ with Kendall's tau statistic. We …
We make an estimation of the value of the Gromov norm of the Cartesian product of two surfaces. Our method uses a connection between these norms and the minimal size of triangulations of the products of two polygons. This allows us to prove that the Gromov norm of this product is between 32 and 52 when both factors hav…
BALSON optimizes parameters with Bayesian approach and Dirichlet distribution.
problem Data fitting with nonnegative L1-norm constraints.
method Bayesian approach, Gaussian likelihood, Dirichlet distribution, sampling methods.
result BALSON outperforms conventional methods in polynomial fitting.