“Our estimator can be trained without computing the autocovariance kernels and it can be parallelized to provide the estimates much faster than existing approaches.”

“The paper develops an approximate Stein's Unbiased Risk Estimator (SURE) for the average mean squared error and establishes asymptotic optimality and regret bounds for a class of machine learning-assisted linear shrinkage estimators.”

“The key methodological innovation is that orthogonal complement projections completely eliminate cross-modal interference when estimating each loading space.”

“The paper proposes a joint-state abstraction that compresses the state space while preserving the information necessary to discover strongly coordinated behaviours.”

“The paper provides the first non-vacuous guarantees in high-dimensional sparse MDPs with single-policy concentrability coverage and corruption, showing that learning a near-optimal policy remains possible in regimes where traditional robust offline RL techniques may fail.”

“The paper establishes the consistency of the QMLE and derives its asymptotic distribution, and proposes a bias-corrected estimator.”

“CREPES-X achieves RMSE of 0.073m and 1.817° in real-world datasets, demonstrating robustness to up to 90% bearing outliers.”

“The paper presents the first resource-adaptive distributed bilevel optimization framework with a second-order free hypergradient estimator.”

“The Bayesian DP algorithm alternates between posterior updates and value iteration, employing an estimator for the risk-based Bellman operator that combines Monte Carlo sampling with convex optimization.”

“The paper proposes 'weighted geometric mean' odds ratios and relative risks, and 'weighted mean' risk differences as transparent summary measures for ordinal outcomes.”

“The paper derives least squares estimators for the drift, diffusion, and jump-diffusion coefficients and establishes their asymptotic rate of convergence.”

“The paper provides the first provable poly-time unbiased estimators for counting traces, a problem of considerable importance when allocating model checking resources.”

“The paper proposes a novel Generative Adversarial Network (GAN) framework, which incorporates 3D pose generators, a 2D pose discriminator, and a 3D pose estimator.”

“The paper proposes to use a standard reduced basis method (RBM) to construct this low-order rational function. Algorithmically, this procedure is an iterative greedy approach, where the greedy objective is evaluated through an error estimator that exploits the linearity of the frequency domain representation.”

“The paper demonstrates that the LCV method provides a better-fit bandwidth parameter for tropical KDE, leading to improved accuracy and computational efficiency compared to nearest neighbor methods, as shown through simulations and empirical data analysis.”

“Model belief explains and predicts ground-truth model choice better than model choice itself, and reduces the computation needed to reach sufficiently accurate estimates by roughly a factor of 20.”

“The paper derives upper and lower bounds on estimation error, proving optimality of the method with noisy data.”

“The paper formalizes Kaplan-Meier and Nelson-Aalen estimators for right-censored data under both perfect and concomitant-based imperfect ranking and establishes their large-sample properties.”

“FLLTPE and FLLTE demonstrated superior performance compared to other estimators.”

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“TimeBill proposes a fine-grained response length predictor (RLP) and an execution time estimator (ETE) to accurately predict the end-to-end execution time of LLMs.”

“Using estimator configurations resulting in unbiased gradients leads to better performance on in-domain as well as out-of-domain tasks.”

“The paper introduces a private estimator for the $p_0$ model parameters and demonstrates its asymptotic properties.”

“When stability holds, the ordinary least-squares estimator satisfies a central limit theorem, and classical Wald-type confidence intervals -- designed for i.i.d. data -- become asymptotically valid even under adaptation, \emph{without} incurring the $\\sqrt{d \\log T}$ price of adaptivity.”

“We develop a generative perspective on hyper-parameter tuning that combines two ideas: (i) optimization-based approximations to Bayesian posteriors via randomized, weighted objectives (weighted Bayesian bootstrap), and (ii) amortization of repeated optimization across many hyper-parameter settings by learning a transport map from hyper-parameters (including random weights) to the corresponding optimizer.”

“Building on this insight, we propose a new nonparametric score-based GoF test through a special class of IPM induced by kernelized Stein's function class, called semiparametric kernelized Stein discrepancy (SKSD) test.”

“The research focuses on a hybrid Hill estimator.”

“The paper focuses on fixed effects estimators with three-dimensional panel and network data.”

“The research focuses on the bias of the Gini estimator in Poisson and geometric cases, also characterizing the gamma family and unbiasedness under gamma distributions.”

“The paper investigates a pretrained deep learning estimator.”

“The paper examines learning-curve monotonicity for Maximum Likelihood Estimators.”

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“An Iteration-Free Fixed-Point Estimator is developed for Diffusion Inversion.”