arXiv research

New estimators outperform maximum likelihood without hyper-parameter estimation.

Neural Empirical Bayes estimates source distributions from noisy simulations.

Empirical median performs well in estimating location with varying scales.

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…

New measure of robustness for estimators, with tight bounds for Gaussian mean estimation.

Efficient classifier error estimation without re-training.

The paper improves the empirical bootstrap method for non-normal estimators.

Improved sample complexity for diffusion models without needing empirical risk minimizers.

The paper analyzes high-dimensional linear regression using parametric empirical Bayes methods.

New method for high-dimensional linear regression using empirical Bayes.

Improved bounds for discrete probability distribution estimation under the ℓ∞ norm.

The best-known and most commonly used distribution-property estimation technique uses a plug-in estimator, with empirical frequency replacing the underlying distribution. We present novel linear-time-computable estimators that significantly "amplify" the effective amount of data available. For a large variety of distri…

A new sequential method estimates Poisson means in streaming data, achieving optimality and efficiency.

New method improves model risk prediction using cross-audit projection.

This study examines biases in flow matching samplers using finite-sample estimation.

Empirical error estimates improve graph sparsification reliability.

Paper tackles heavy-tailed data without finite variance, proposing robust risk minimization.

Estimates score function from data with optimal rate in high dimensions.

All too often measuring statistical dependencies between financial time series is reduced to a linear correlation coefficient. However this may not capture all facets of reality. We study empirical dependencies of daily stock returns by their pairwise copulas. Here we investigate particularly to which extent the non-st…

Optimal score function estimation via empirical risk minimization

New method estimates Schrödinger bridge potentials via empirical risk minimization.

This paper studies the partial estimation of Gaussian graphical models from high-dimensional empirical observations. We derive a convex formulation for this problem using $\ell_1$-regularized maximum-likelihood estimation, which can be solved via a block coordinate descent algorithm. Statistical estimation performance …

New method improves estimation of complex models from conditional moment restrictions.

Empirical Bayes rates via variational approximations and prior decomposition.

We rebias estimates to improve interval calibration and prediction accuracy.

Enhances normal mean estimation with side info using NIT approach.

Neural networks estimate statistical divergences with performance guarantees.

Paper shows robust estimators converge to true risk minimizers at optimal rates.

Extends covariance estimation with multiple targets for better performance.

We study the Nonparametric Maximum Likelihood Estimator (NPMLE) for estimating Gaussian location mixture densities in $d$-dimensions from independent observations. Unlike usual likelihood-based methods for fitting mixtures, NPMLEs are based on convex optimization. We prove finite sample results on the Hellinger accurac…

Parameters defined via General Estimating Equations (GEE) can be estimated by maximizing the Empirical Likelihood (EL). Newey and Smith (2004) have recently shown that this EL estimator exhibits desirable higher-order asymptotic properties, namely, that its O(n^-1) bias is small and that bias-corrected EL is higher-ord…

A neural network method estimates densities from characteristic functions.

Parameters defined via general estimating equations (GEE) can be estimated by maximizing the empirical likelihood (EL). Newey and Smith [Econometrica 72 (2004) 219--255] have recently shown that this EL estimator exhibits desirable higher-order asymptotic properties, namely, that its $O(n^{-1})$ bias is small and that …

Unified approach to private statistics from empirical to population data.

EB-PCA reduces noise in high-dimensional PCA by estimating a joint prior distribution.

The Fisher information matrix (FIM) is a foundational concept in statistical signal processing. The FIM depends on the probability distribution, assumed to belong to a smooth parametric family. Traditional approaches to estimating the FIM require estimating the probability distribution function (PDF), or its parameters…

This paper studies the estimation of low-rank Markov chains from empirical trajectories. We propose a non-convex estimator based on rank-constrained likelihood maximization. Statistical upper bounds are provided for the Kullback-Leiber divergence and the $\ell_2$ risk between the estimator and the true transition matri…

In this work we investigate to which extent one can recover class probabilities within the empirical risk minimization (ERM) paradigm. The main aim of our paper is to extend existing results and emphasize the tight relations between empirical risk minimization and class probability estimation. Based on existing literat…

We propose an estimator and confidence interval for computing the value of a policy from off-policy data in the contextual bandit setting. To this end we apply empirical likelihood techniques to formulate our estimator and confidence interval as simple convex optimization problems. Using the lower bound of our confiden…

We develop an approach to risk minimization and stochastic optimization that provides a convex surrogate for variance, allowing near-optimal and computationally efficient trading between approximation and estimation error. Our approach builds off of techniques for distributionally robust optimization and Owen's empiric…

Covariance shrinkage via stochastic interpolation

New method balances covariates for stable causal survival effect estimation.

TSAW improves MCMC integral estimation with faster convergence.

A new method of moments estimator goes beyond data reweighting.

Simple private estimators for mean and covariance outperform existing methods.

New estimator reduces kernel mean estimation error.

R package for Bayesian empirical likelihood sampling using HMC.

CoinDICE estimates confidence intervals for unknown behavior policies in reinforcement learning.