This paper characterizes how estimation error in covariance matrices affects the global minimum-variance portfolio and derives a decision geometry for regret.

This paper develops a framework for identifying and estimating parameters of interest in automatic debiased machine learning using a Riesz representer, which is identified when it uniquely optimizes a…

This paper derives deterministic equivalents for the prediction risk of over-parameterized linear regression with degenerate covariance matrices and dependent covariates, and identifies the configurat…

This paper establishes recovery guarantees for low-rank Hermitian matrices from quadratic sampling matrices under the assumption of finite 4+δ moments of the entries.

The paper analyzes low-degree estimation thresholds for recovering hidden signals in planted hypergraphs and tensor PCA, establishing sharp phase transitions and providing polynomial-time recovery alg…

This paper proposes a novel estimator for the target linear coefficient in a partial linear model with black-box nuisance estimation and establishes its unimprovable error rate.

The paper proposes a new, optimal estimator for semiparametric inference that improves upon standard double machine learning (DML) rates by eliminating the first-order stochastic error of nuisance fun…

This paper proves that the minimax lower bound of estimation for popular kernel discrepancies is n^-(1/2) on general topological spaces and under mild assumptions on the kernel.

This paper proposes a comprehensive benchmarking framework for evaluating mutual information estimation methods on complex, realistic data using copula theory.

This paper improves the theoretical bounds for estimating discrete probability distributions using the $\ell_\infty$ norm, resolving several open questions in the field.

This paper investigates the necessity of interaction for order-optimal 1-bit mean estimation in nonparametric finite-moment classes.

This paper analyzes the statistical and computational limits of learning bounded linear operators between Sobolev spaces from noisy data, and constructs a finite-resolution blockwise least-squares est…

The paper proposes a Secure Parallel Determinant Computation (SPDC) framework that enables efficient, privacy-preserving, and scalable matrix determinant calculation across multiple untrusted edge ser…

This paper introduces Curvature-Weighted Gradient Diversity (CWGD), a geometry-aware measure for optimization noise that reduces the asymptotic optimization error floor by up to a factor of two compar…

An algorithm is given to estimate quantum state eigenvalues with fewer copies using a new tomography guarantee.

The paper investigates applying Riemannian optimization techniques to low-rank matrix parameters for deep learning, but finds that the proposed methods do not conclusively outperform the AdamW baselin…

This paper derives deterministic limits for transfer learning performance of linear discriminant analysis in high-dimensional two-class classification under spiked covariance models.