Unified approach amplifies data for distribution property estimation.
problem Estimating properties of discrete distributions efficiently.
method Unified, linear-time, competitive estimator using just 2n samples.
result Achieves performance of empirical estimator with n√log n samples using only 2n samples.
Unified method for estimating properties of large domain distributions efficiently.
problem Estimating properties of distributions over large domains efficiently.
method Piecewise-polynomial approximation technique for constructing sample- and time-efficient estimators.
result Near-linear-time computable estimators with optimal and highly-concentrated approximation values.
Paper offers a framework for estimating symmetric properties efficiently.
problem Estimating symmetric properties of distributions from samples.
method General framework using profile maximum likelihood (PML) distribution.
result Optimal sample complexity for many properties, practical algorithms.
Unified plug-in approach for estimating symmetric properties of distributions efficiently.
problem Estimating symmetric properties of distributions with high accuracy and efficiency.
method Profile-maximum-likelihood (PML) based estimator.
result Achieves theoretical limit for universal symmetric property estimation.
New estimators amplify data, achieving plug-in accuracy with fewer samples.
problem Efficiently estimating distribution properties with minimal data.
method Linear-time computable estimators that amplify data.
result Achieve plug-in accuracy with n samples, compared to nlogn for empirical estimators. Paper studies estimating network properties with missing data using SRL and GNN.
problem Estimating aggregate properties in networks with missing data attributes.
method Comparative study of SRL and GNN approaches for inferring missing attributes and estimating aggregate properties.
result SRL-based approaches tend to outperform GNN-based approaches in estimating aggregate properties and predictive accuracy.
Bayesian approach estimates sub-resolution reservoir properties from seismic data.
problem Estimating sub-resolution reservoir properties from seismic data.
method Bayesian evidential learning approach, direct relation between seismic data and reservoir properties.
result Efficient estimation of reservoir properties with uncertainty quantification.
PML estimator optimally solves three statistical learning problems.
problem Distribution estimation, property estimation, and property testing.
method Profile Maximum Likelihood (PML) estimator.
result PML achieves optimal sample complexity for various learning tasks.
Develops Active Fourier Auditor to estimate ML model properties without reconstructing them.
problem Verifying and auditing properties of Machine Learning models in real-world applications.
method A new framework that quantifies ML model properties using Fourier coefficients, without reconstructing the model.
result Active Fourier Auditor (AFA) is more accurate and sample-efficient than baselines for estimating robustness, individual fairness, and group fairness.
Study GLS estimator properties in multivariate regression with heteroskedastic and autocorrelated errors.
problem Asymptotic properties of GLS estimator in multivariate regression with specific error structures.
method Derive Wald statistics for linear restrictions and assess their performance.
result Wald statistics remain robust to heteroskedasticity and autocorrelation.
A debiasing method improves nonparametric regression's statistical properties.
problem Lack of theoretical guarantees for modern nonparametric regression methods.
method Model-free debiasing method incorporating a correction term.
result Debiased estimator satisfies pointwise and uniform risk convergence, asymptotic normality.
New algorithm efficiently estimates symmetric properties using approximate PML.
problem Estimating symmetric properties of a distribution efficiently.
method Developed an algorithm to compute an approximate PML distribution in nearly linear time.
result Achieved nearly linear time universal plug-in estimator for all symmetric functions.
GANPOP uses deep learning to estimate optical properties from single images, improving accuracy over existing methods.
problem Estimating optical properties from single wide-field images.
method Conditional generative adversarial networks trained on paired images and optical property maps.
result GANPOP estimates optical properties with 58% higher accuracy than single-snapshot optical property technique in human gastrointestinal specimens.
Kernel estimator improves spectral risk measure estimation.
problem Estimating spectral risk measures accurately.
method Kernel-based estimation of L-statistics for SRMs.
result Kernel estimator is strongly consistent and asymptotically normal.
We study some equivalent properties of the curvature-dimension conditions CD(n,K) inequality on infinite, but locally finite graph. These equivalences are gradient estimate, Poincaré type inequalities and reverse Poincaré inequalities. And we also obtain one equivalent property of gradient estimate for a new notion o…
Optimal Strichartz estimates for Schrödinger on Zoll manifolds.
problem Optimal Strichartz estimates for solutions to the Schrödinger equation on Zoll manifolds.
method Arithmetic properties of the spectrum of the Laplacian and bilinear oscillatory integral estimates.
result Optimal Strichartz estimates for all q≥2 in Lt,xq spaces. Graphs with non-negative curvature have improved heat semigroup estimates.
problem Estimating heat semigroup norms on graphs with curvature.
method Proved a reverse Poincaré inequality for graphs with non-negative Ollivier curvature.
result Heat semigroup norm estimates lead to Buser inequality, Liouville property, and eigenvalue estimates.
Paper characterizes DLN distribution, its properties, and estimation methods.
problem No specific problem stated, focuses on DLN distribution properties.
method Characterization of PDF, CDF, moments; generalization to N-dimensions; methods to handle double-exponential nature.
result Characterization of DLN distribution and its properties, including estimation methods.
Deep learning model improves seismic rock property estimation.
problem Estimating reservoir rock properties from seismic reflection data.
method Proposes a deep learning-based seismic inversion workflow that models seismic traces spatiotemporally.
result Achieves best performance on SEAM dataset with r2 coefficient of 79.77\% Paper proposes a simple estimator for DPP correlation kernels.
problem Estimating the correlation kernel matrix of DPPs.
method Closed-form estimator for correlation kernel, easy to implement.
result Consistency and asymptotic normality of the estimator proved.
Examines WENDy-IRLS algorithm's noise robustness and efficiency in various differential equations.
problem Noise robustness and efficiency of WENDy-IRLS algorithm.
method Studied coverage and bias properties of WENDy-IRLS algorithm's estimators in various differential equations and noise distributions.
result WENDy-IRLS algorithm shows notable noise robustness and computational efficiency.
Estimates Kaehler metrics' diameter in big cohomology classes.
problem Estimating the diameter of Kaehler metrics in big cohomology classes.
method Proves uniform diameter estimates using integrability conditions and stability properties of complex Monge-Ampere equations.
result Uniform diameter estimates for Kaehler metrics in big cohomology classes.
Study geometric properties and spectral estimates on warped products.
problem Investigate Ricci curvature and spectral estimates in warped products.
method Establish integral inequalities and sufficient conditions for geometric properties.
result Sufficient conditions for intersection of warped products with totally geodesic hypersurfaces.
Log-normal continuous random cascades form a class of multifractal processes that has already been successfully used in various fields. Several statistical issues related to this model are studied. We first make a quick but extensive review of their main properties and show that most of these properties can be analytic…
Proposes a method to stabilize treatment effect estimation with unbalanced data.
problem Unbalanced treatment assignment leading to unstable propensity score estimations.
method Undersamples data for propensity score modeling and calibrates scores to match original distribution.
result The estimator retains asymptotic properties of the DML estimator and improves finite sample performance.
Jointly estimates flow fields and particle properties from Lagrangian data.
problem Estimating flow fields and particle properties from sparse, noisy Lagrangian data.
method Data assimilation framework coupling Eulerian and Lagrangian models.
result Joint estimation of flow fields and particle properties in various flow regimes.
Proposes robust ABC method for outlier detection.
problem Outliers sensitivity in ABC methods.
method γ-divergence estimator with redescending property.
result Significantly higher robustness than existing methods.
Comparative statistical properties of Parkinson, Garman-Klass, Roger-Satchell and bridge oscillation estimators are discussed. Point and interval estimations, related with mentioned estimators are considered
This paper explores how to choose scoring rules for estimating properties with parametric assumptions.
problem Indirect elicitation of properties with parametric assumptions.
method Developed a framework for choosing proper scoring rules for indirect elicitation, considering constraints and optimal solutions.
result The optimal estimation of the target property changes monotonically with the increase of each weight, and often setting some weights as zero yields the best configuration.
C-Learner improves stability of plug-in estimators for causal inference.
problem Limited overlap between treatment and control groups leads to unstable estimates.
method Constrained learning framework that achieves stability and asymptotic properties.
result Constrained learning produces stable estimates with desirable asymptotic properties.
This paper presents a natural extension of stagewise ranking to the the case of infinitely many items. We introduce the infinite generalized Mallows model (IGM), describe its properties and give procedures to estimate it from data. For estimation of multimodal distributions we introduce the Exponential-Blurring-Mean-Sh…
Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamenta…
Paper proposes estimators for sparse PCA with oracle property.
problem Estimating sparse principal subspace in high-dimensional settings.
method Semidefinite relaxation with novel regularizations.
result One estimator achieves exact support recovery and statistical rate.
Study the averaging estimator on graphs with labeled nodes.
problem Understanding the quality of averaging estimators on graph data.
method Rigorously study concentration properties, variance bounds, and risk bounds.
result Contributes to theoretical understanding of graph learning.
This work extends Ledoit-Wolf shrinkage to unknown mean covariance estimation.
problem Large dimensional covariance matrix estimation with unknown mean under Kolmogorov asymptotics.
method Extending Ledoit-Wolf linear shrinkage to translation-invariant estimators, proving their convergence properties.
result A new estimator outperforms other standard estimators empirically.
The paper proves conditions for a manifold to have the Liouville property for the drifted Laplacian.
problem Conditions for a manifold to have the Liouville property for the drifted Laplacian.
method Local gradient estimates for positive solutions to the semilinear equation and structural conditions on F.
result The manifold has the Liouville property for the drifted Laplacian under specific curvature conditions.
New method improves estimation of complex models from conditional moment restrictions.
problem Estimation of complex models from conditional moment restrictions.
method Functional Generalized Empirical Likelihood (GEL) with a practical method.
result The method achieves state-of-the-art performance on two problems.
Develops a statistical framework for coherent risk estimation.
problem Constructing coherent risk estimators with sound financial and statistical properties.
method Inspired by axiomatic risk measure theory, defines coherent risk estimators through robust representations linked to L-estimators. result Demonstrates that coherence of a risk measure does not necessarily carry over to its estimators and shows alternative weight structures can lead to different outcomes.
Estimates manifold distances using graph Laplacian, proving consistency.
problem Estimating distances in compact Riemannian manifolds.
method Graph Laplacian estimates of the Laplace-Beltrami operator, bounding errors.
result Proof of consistency for manifold distances.
Estimates for Poisson equation on manifolds with weighted Poincare inequality.
problem Existence and estimates of Poisson equation solutions on manifolds.
method Develops Green's function estimate using weighted Poincare inequality and Ricci curvature.
result Proves Liouville property for finite energy holomorphic functions on Kähler manifolds.
We present a unified framework for low-rank matrix estimation with nonconvex penalties. We first prove that the proposed estimator attains a faster statistical rate than the traditional low-rank matrix estimator with nuclear norm penalty. Moreover, we rigorously show that under a certain condition on the magnitude of t…
DRIVE improves IV estimation by accounting for distributional uncertainties.
problem Challenges in IV estimation due to untestable model assumptions and poor finite sample properties.
method DRIVE is a distributionally robust IV estimation method that minimizes a square root TSLS objective with a Wasserstein ambiguity set.
result DRIVE achieves consistency without requiring regularization parameter to vanish, ensuring robustness to distributional uncertainties.
Unified view of score estimators for flexible densities.
problem Estimating the score from unknown distributions.
method Regularized nonparametric regression framework.
result Unified convergence analysis and new estimators with desirable properties.
New algorithms estimate Jacobian matrices for large-scale machine learning.
problem Efficiently computing search directions for large nonlinear least squares.
method Exploit low-rank structure in Hessian to estimate Jacobian matrices.
result Two algorithms perform well compared to state-of-the-art methods.
This paper solves the convergence problem for estimating MGGD parameters with a convex formulation.
problem Establishing convergence properties for estimating MGGD parameters with unknown mean and precision matrix.
method Proposes a convex formulation with well-established convergence properties for robust estimation in noisy scenarios.
result Demonstrates improved accuracy in precision and covariance matrix estimation compared to existing methods.
New algorithms improve community detection and parameter estimation for PABM.
problem Improving community detection and parameter estimation for PABM.
method Connecting PABM to GRDPG, constructing new algorithms, and deriving asymptotic properties.
result Absolute number of community detection errors tends to zero as graph vertices increase.
Paper proposes robust LAD estimators for 2D sinusoidal model, proving consistency and normality.
problem Estimation of parameters in 2D sinusoidal models with outliers or heavy-tailed noise.
method Least absolute deviation (LAD) estimators for robust parameter estimation.
result Strong consistency and asymptotic normality of LAD estimators for 2D sinusoidal model parameters.
New unbiased variance estimator for random forests using Hoeffding decomposition.
problem Uncertainty quantification in random forests with large kernel sizes and small sample sizes.
method Proposes a new Hoeffding decomposition view for variance estimation, establishing unbiased estimators and ratio consistency.
result Establishes the ratio consistency of the proposed variance estimator, justifying confidence interval coverage rates.