Simplified argument for second order estimate in quaternionic Calabi-Yau problem.
problem Second order estimates for quaternionic Calabi-Yau problem on hyperkähler manifolds.
method Simplified argument to derive the estimate.
result Simplified derivation of second order estimate.
Kernel estimator optimally recovers function from noisy exponential Radon transform.
problem Inverting noisy exponential Radon transform of a function.
method Proposed a kernel estimator to estimate the true function.
result The estimator converges to the true function at minimax optimal rate.
Estimates neural network errors for classification problems.
problem Binary and multi-class classification problems.
method Rademacher complexity estimates and direct approximation theorems.
result A priori error estimates for regularized loss functionals.
Paper solves a long-standing problem with curvature estimates.
problem Long-standing problem in n−2 curvature equation. method Global curvature estimate for the n−2 Hessian equation. result Solves a long-standing problem in n−2 curvature equation. Estimates derived for solutions of Neumann problems on Riemannian manifolds.
problem Gradient and second order estimates for solutions of fully nonlinear elliptic equations on compact Riemannian manifolds.
method Derivation of gradient and second order {\em a priori} estimates.
result Existence and regularity results for solutions of Neumann problems.
Study Neumann problem for special Lagrangian type equations.
problem Neumann problem for special Lagrangian type equations.
method Uniform a priori estimates, continuity method, direct proof of boundary double normal derivative estimates.
result Existence result for Neumann problem of special Lagrangian type equations.
The paper applies a capillary John ellipsoid theorem to solve capillary curvature problems.
problem Solving capillary curvature problems in Euclidean half-spaces.
method Applying a capillary John ellipsoid theorem to derive non-collapsing estimates and gradient estimates.
result Established existence of solutions to capillary curvature problems in certain ranges of p and q. Paper estimates diameter for Minkowski problem solutions.
problem Estimating diameter of solutions to Minkowski problem.
method Uniform diameter estimate for Lp dual Minkowski problem. result Uniform diameter estimate for planar Lp dual Minkowski problem. New neural methods tackle density estimation and likelihood-free inference.
problem Density estimation and likelihood-free inference.
method Neural network-based methods.
result New methods for density estimation and likelihood-free inference.
New biharmonic Steklov problem on forms yields eigenvalue estimates.
problem Eigenvalue estimates for differential forms with curvature quantities.
method Introduced a new biharmonic Steklov problem and proved existence of a discrete spectrum.
result Established Kuttler-Sigillito inequalities connecting eigenvalues of differential forms.
In study of eigenvalue problems, a classical problem is the Stekloff eigenvalue problem. There are many estimates of the first non- zero Stekloff eigenvalue, including a sharp estimate on surfaces, obtained by Escobar in "The geometry of the first non-zero Stekloff eigenvalue, J. Funct. Anal. 150 (1997)". In this paper…
Paper studies unique interior points and estimates for generalized translating soliton problems.
problem Generalized translating soliton type problems.
method Proves uniqueness of interior critical points, derives C0 and C1 estimates using minimum principles. result Derives a priori C0 and C1 estimates for solutions. We define a new class of Bayesian point estimators, which we refer to as risk averse. Using this definition, we formulate axioms that provide natural requirements for inference, e.g. in a scientific setting, and show that for well-behaved estimation problems the axioms uniquely characterise an estimator. Namely, for es…
We discuss the problem of risk estimation in the classification problem, with specific focus on finding distributions that maximize the confidence intervals of risk estimation. We derived simple analytic approximations for the maximum bias of empirical risk for histogram classifier. We carry out a detailed study on usi…
Estimates and optimizes UBSR risk in recursive settings.
problem Estimating and optimizing UBSR risk in a recursive setting with one-at-a-time samples.
method Casts UBSR as a root finding problem, uses stochastic approximation and gradient descent.
result Derives non-asymptotic bounds on estimation and optimization errors.
New method avoids high variance in infinite-horizon off-policy estimation.
problem High variance in importance sampling for long-horizon problems.
method Applies IS directly on stationary state-visitation distributions.
result Developed a novel approach to estimate density ratio.
Paper develops a new state estimation method for nonlinear systems.
problem State estimation for nonlinear state-space models is intractable.
method Developed a variational inference approach based on Gaussian approximations.
result The method outperforms alternative Gaussian approaches in various examples.
Many problems in machine learning and statistics involve nested expectations and thus do not permit conventional Monte Carlo (MC) estimation. For such problems, one must nest estimators, such that terms in an outer estimator themselves involve calculation of a separate, nested, estimation. We investigate the statistica…
BBCI uses meta prediction to estimate causal effects from datasets.
problem Estimating causal effects from observed data.
method Meta prediction to learn causal effect estimation.
result BBCI accurately estimates ATEs and CATEs across various causal inference problems.
Estimating a constrained relation is a fundamental problem in machine learning. Special cases are classification (the problem of estimating a map from a set of to-be-classified elements to a set of labels), clustering (the problem of estimating an equivalence relation on a set) and ranking (the problem of estimating a …
Abstract: Translates causal inference into statistical formalism, examines estimability and ill-posedness.
problem What can be estimated from causal inference problems?
method Uses abstract statistical formalism and category theory to analyze identifiability and estimability.
result Identifiability does not guarantee stability, making estimability a stricter condition.
The paper proposes a simple method for estimating parameters in inverse problems using a diffusion model.
problem Estimating observation parameters in inverse problems with regularization and prior diffusion modeling.
method A Bayesian approach using a diffusion process prior and MCMC algorithms for posterior sampling.
result An optimal estimator for observation parameters and image of interest is defined, with quantified uncertainty.
Novel mean estimation method under user-level differential privacy reduces noise in continual mean estimates.
problem Maintaining accurate running mean estimates under user-level differential privacy.
method Developed a novel mean estimation specific factorization under approximate differential privacy.
result Achieved asymptotically lower mean-squared error bounds in continual mean estimation.
A new method avoids partition function computation for Gibbs density estimation.
problem Estimating Gibbs density functions without partition function computation.
method Maximum Recovery MAP (MR-MAP) and least-action type potential.
result MR-MAP estimators solve optimization problem quickly using neural network.
We study the problem of off-policy value evaluation in reinforcement learning (RL), where one aims to estimate the value of a new policy based on data collected by a different policy. This problem is often a critical step when applying RL in real-world problems. Despite its importance, existing general methods either h…
Uniform estimates for elliptic problems near polygonal domains.
problem Proving uniform solvability estimates for elliptic problems near polygonal domains.
method Suitable conformal modification of the metric to make the union of domains a manifold with boundary and relative bounded geometry.
result Rounding off the corners of the limit polygonal domain.
Solves Dirichlet problem for fully nonlinear equations on Hermitian manifolds.
problem Solving Dirichlet problem for fully nonlinear equations on Hermitian manifolds.
method Derived C2 estimates and gradient estimates for solutions. result Solved Dirichlet problem with admissible subsolutions in some cases.
New methods reduce bias in estimating optimality gaps for risk-averse stochastic programs.
problem Optimality gap estimation bias in risk-averse stochastic programs.
method Two independent samples, each estimating a different component of the optimality gap.
result Our method reduces bias in estimating optimality gaps for risk-averse problems.
New method estimates robust mean in high dimensions with minimized outliers.
problem Estimating the mean in high dimensions when a fraction of data is corrupted.
method Formulating the problem as ℓ0-norm minimization under second moment constraints, and using ℓ1 and ℓp minimization techniques. result The proposed method achieves order optimal robust mean estimation and significantly outperforms existing methods.
Log-density gradient estimation is a fundamental statistical problem and possesses various practical applications such as clustering and measuring non-Gaussianity. A naive two-step approach of first estimating the density and then taking its log-gradient is unreliable because an accurate density estimate does not neces…
MUSE provides unbiased stopping estimates for optimal problems.
problem Estimating the utility of optimal stopping problems.
method Backward recursive construction of the Multilevel Unbiased Stopping Estimator (MUSE).
result MUSE achieves ε-accuracy with O(1/ε^2) computational cost.
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…
A new sequential method estimates Poisson means in streaming data, achieving optimality and efficiency.
problem Estimating Poisson means in a streaming, or online, framework.
method A quasi-Bayesian approach based on Newton's algorithm for a sequential estimate.
result Established frequentist guarantees including consistency and asymptotic optimality.
Study on biharmonic Steklov problems with Neumann boundary conditions and eigenvalue estimates.
problem Biharmonic Steklov problems with Neumann boundary conditions.
method Introduced a biharmonic Steklov problem and proved its well-posedness. Established eigenvalue estimates using Kuttler-Sigillito inequalities.
result Eigenvalue estimates for the biharmonic Steklov problem with Neumann boundary conditions.
Integrates estimation and optimization for uncertain parameters.
problem Optimizing with uncertain parameters whose distributions can be estimated.
method Integrated Conditional Estimation-Optimization (ICEO) framework.
result Asymptotically consistent and provides finite performance guarantees.
ASEs use surrogate estimation to efficiently evaluate model performance with minimal labels.
problem Efficient model evaluation with limited labels.
method Surrogate-based estimation and active learning.
result ASEs offer greater label-efficiency than current methods for deep neural networks.
Paper proposes an online covariance estimator for sketched Newton methods.
problem Estimating the limiting covariance matrix of sketched Newton methods.
method Proposes a fully online covariance matrix estimator from Newton iterates.
result Establishes the consistency and convergence rate of the proposed estimator.
Gradient estimates for hyperbolic space CMC equation solved.
problem Gradient estimates for solutions to constant mean curvature equation in hyperbolic space.
method Maximum principles theory of Φ-functions.
result Gradient estimates obtained for bounded strictly convex domains.
Paper proposes robust estimators for GANs under Wasserstein contamination.
problem Robust estimation of distributions under contamination.
method Wasserstein GAN-based estimators for location, covariance, and regression.
result Proposed estimators are minimax optimal in many scenarios.
New method for estimating parameters in inverse problems using double robustness.
problem Estimating parameters defined as linear functionals of solutions to linear inverse problems.
method Source condition double robust inference method that uses iterated Tikhonov regularized adversarial estimators.
result Asymptotic normality of the parameter of interest as long as either the primal or dual inverse problem is sufficiently well-posed.
The paper develops a new algorithm for constructing minimax estimators using online learning techniques.
problem Designing minimax estimators for probability distribution parameters.
method Viewing the problem as a zero-sum game and using online learning with non-convex losses to find a Nash equilibrium.
result The algorithm constructs both a minimax estimator and a least favorable prior.
The paper analyzes risk estimation methods and derives bounds for OCE risk.
problem Estimating the Optimized Certainty Equivalent (OCE) risk from samples.
method Derives mean-squared error and concentration bounds for SAA of OCE, and analyzes an efficient stochastic approximation-based estimator.
result Finite sample bounds and mis-identification probability bounds for the efficient estimator.
Study measures malware detection metrics without ground truth.
problem Accurate measurement of security metrics in the absence of ground truth.
method Statistical estimation methods for five malware detection metrics.
result Characterized and improved statistical estimators for accuracy.
DML-CMR estimator reduces bias in CMR problems using deep neural networks.
problem Solving conditional moment restrictions with deep neural networks.
method Double/debiased machine learning framework for unbiased estimation.
result Achieves minimax optimal convergence rate of O(N−1/2). The paper proves estimates for solutions to nonlinear equations on manifolds with boundary.
problem Boundary estimates for fully nonlinear Yamabe equations on Riemannian manifolds.
method Deriving a priori second derivative estimates for subsolutions.
result Existence of smooth solutions with uniform estimates.
Paper estimates curvature of semi-convex hypersurfaces in hyperbolic space.
problem Estimating curvature of semi-convex hypersurfaces in hyperbolic space.
method Established C2 estimates using a new concavity inequality for hessian equations. result Derived C2 estimates for semi-convex complete hypersurfaces with constant σk curvature. Estimates LATE using combined datasets, overcoming data limitations.
problem Estimating LATE when compliance is incomplete and data is split.
method Combines separately observed datasets to estimate LATE using direct and weighted least squares methods.
result Proposes a stable and practical estimator for LATE.
The problem of estimating sparse eigenvectors of a symmetric matrix attracts a lot of attention in many applications, especially those with high dimensional data set. While classical eigenvectors can be obtained as the solution of a maximization problem, existing approaches formulate this problem by adding a penalty te…