Proposes EpG and OOI metrics showing agentic workflows use 4.33x more energy per successful goal than linear baselines due to orchestration structure.
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Estimation of parameters and large quantiles based on the k largest observations
18 Pith papers cite this work, alongside 2,865 external citations. Polarity classification is still indexing.
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DR-ME is the first semiparametrically efficient finite-location kernel test for interpretable distributional treatment effects, using orthogonal doubly robust features derived from observational data.
Primitive sequences obtained from iterated antiderivatives of the CDF are homeomorphic to probability measures on compact intervals, equivalent to factorial-rescaled moments of the reflected variable, and yield sharp bounds on functionals when the first m terms are fixed.
Profile MLE for the regime-switching threshold in null-recurrent diffusion converges at rate n^{-(1+γ)/2} to the arg sup of a doubly stochastic drifted Poisson process involving local time of oscillating Brownian motion.
AdaptNC jointly adapts nonconformity scores and thresholds in conformal prediction to shrink prediction region volumes under distribution shifts while preserving target coverage.
Presents the first kernel framework for distributional treatment effect inference from adaptively collected data, using doubly robust RKHS scores, cross-fold witness functions, and sequentially normalized statistics with valid type-I error.
Central limit theorems are established for SAA value functions in finite-horizon stochastic optimal control via an abstract limit theorem for stochastic backward recursions, yielding recursive asymptotic variance formulas under unique optimal policies.
Proposes APUB optimization framework for stochastic programming, proves asymptotic correctness and consistency of the new bound, and develops bootstrap and L-shaped solvers for two-stage linear problems with empirical tests on a product mix example.
Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
Proves finite-shot mean-squared-error laws for virtual distillation and symmetry verification that define certified operating windows and a selection trichotomy for their comparison.
Proposes and analyzes a homogeneity test using squared L2 distance of empirical EOT maps to uniform-on-ball reference, with FCLT, Gaussian quadratic null limit, consistency, local power, and weighted multiplier bootstrap.
Introduces convolution smoothing of the check-loss for prediction-powered quantile regression, derives asymptotics under misspecification, and proposes an ensemble estimator.
Develops a unified framework representing performance metrics as smooth functionals of confusion-matrix probabilities to enable cluster-robust sandwich variance estimation for asymptotically valid confidence intervals and tests under clustered data.
A functional central limit theorem for pattern frequencies in 2D samples enables nonparametric goodness-of-fit, two-sample, and symmetry tests for copulas, with bootstrap critical values and parametric examples.
L2C2 is a deep RL framework that learns to clean tabular data by aligning it to the synthetic prior of tabular foundation models, yielding higher accuracy on some benchmarks and cross-dataset policy transfer.
A consistent bias-corrected estimator based on blockwise top-two order statistics is developed for extreme value analysis after showing the naive independence-likelihood approach is inconsistent.
Proves that rescaled deviations of kernel gradient flow and infinitesimal gradient boosting from their deterministic ODE limits converge to a Gaussian process via a general stochastic perturbation analysis of ODEs in Banach spaces.
Constrained weighted Bayesian bootstrap extends weighted Bayesian bootstrap to constrained posteriors with asymptotics matching restricted MLE and is demonstrated on option pricing.
citing papers explorer
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Central Limit Theorems for Sample Average Approximations in Stochastic Optimal Control
Central limit theorems are established for SAA value functions in finite-horizon stochastic optimal control via an abstract limit theorem for stochastic backward recursions, yielding recursive asymptotic variance formulas under unique optimal policies.
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Minimizing Upper Confidence Bounds: A Data-Driven Framework for Stochastic Programming
Proposes APUB optimization framework for stochastic programming, proves asymptotic correctness and consistency of the new bound, and develops bootstrap and L-shaped solvers for two-stage linear problems with empirical tests on a product mix example.