Pandora's Regret is a closed-form pairwise scoring rule derived from expected optimal search costs that elicits true probabilities and outperforms log loss, accuracy, and F1 at predicting diagnostic costs on MedMNIST models.
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UCB-AA is a screening-enhanced UCB algorithm for bandits with arriving arms that delivers arrival-dependent regret bounds and sublinear dynamic regret under gap regularity conditions.
Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
A generalization of the Benjamini-Hochberg procedure controls the FDR curve below any specified level in location families, and the standard procedure simultaneously controls the entire curve for free.
The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.
Flash endurance is priced via shadow price η making placement cost-optimal for any sign of value-write correlation χ, with χ positive only in recurrent long-horizon manipulation and the budget binding only on low-endurance commodity hardware.
Derives three EVPI-based stopping policies for document screening and shows higher net utility than recall-target methods on CLEF-IP and medical review datasets.
Mixed Poisson regression models with Gaussian latent variables are asymptotically robust to infinite target values but not to infinite covariate values, as shown for Poisson-Gamma, Poisson-log-t, and Poisson-RSB targets.
Joint location-scale minimization for geometric medians on product manifolds degenerates to marginal medians, and three new scale-selection methods restore identifiability with asymptotic guarantees.
A double machine learning framework that residualizes standard outcome-above-expectation metrics to support valid frequentist inference and player-specific effect estimation in sports analytics.
citing papers explorer
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Pandora's Regret: A Proper Scoring Rule for Evaluating Sequential Search
Pandora's Regret is a closed-form pairwise scoring rule derived from expected optimal search costs that elicits true probabilities and outperforms log loss, accuracy, and F1 at predicting diagnostic costs on MedMNIST models.
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Multi-Armed Bandits with Arriving Arms: Sequential Screening, Dynamic Regret, and Sublinear Guarantees
UCB-AA is a screening-enhanced UCB algorithm for bandits with arriving arms that delivers arrival-dependent regret bounds and sublinear dynamic regret under gap regularity conditions.
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Scale-Calibrated Median-of-Means for Robust Distributed Principal Component Analysis
Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
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Simultaneous false discovery rate control in location families
A generalization of the Benjamini-Hochberg procedure controls the FDR curve below any specified level in location families, and the standard procedure simultaneously controls the entire curve for free.
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Profile Likelihood Inference for Anisotropic Hyperbolic Wrapped Normal Models on Hyperbolic Space
The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.
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Memory as a Wasting Asset: Pricing Flash Endurance for Embodied Agents, and the Limits of Doing So
Flash endurance is priced via shadow price η making placement cost-optimal for any sign of value-write correlation χ, with χ positive only in recurrent long-horizon manipulation and the budget binding only on low-endurance commodity hardware.
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Decision-Theoretic Stopping Rules for Document Screening
Derives three EVPI-based stopping policies for document screening and shows higher net utility than recall-target methods on CLEF-IP and medical review datasets.
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On Asymptotic Outlier Rejection in Bayesian Mixed Poisson Regression Models Under Extreme Target and Covariate Values
Mixed Poisson regression models with Gaussian latent variables are asymptotically robust to infinite target values but not to infinite covariate values, as shown for Poisson-Gamma, Poisson-log-t, and Poisson-RSB targets.
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Scale selection for geometric medians on product manifolds
Joint location-scale minimization for geometric medians on product manifolds degenerates to marginal medians, and three new scale-selection methods restore identifiability with asymptotic guarantees.
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Rethinking player evaluation in sports: Goals above expectation and beyond
A double machine learning framework that residualizes standard outcome-above-expectation metrics to support valid frequentist inference and player-specific effect estimation in sports analytics.