arXiv Statistics ML
Conditional regression for the Nonlinear Single-Variable Model
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A Closed-Form Formula for Consistent Lipschitz Regression on Metric Spaces with Sparse Neural Network Realizations
arXiv:2609. 03129v1 Announce Type: cross Abstract: Several classical machine-learning methods, such as KRRs and SVRs, are both computationally and analytically tractable since their estimators either admit closed-form expressions or are obtained by minimizing convex training objectives; neither feature is generally available for deep neural networks.
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Local polynomial density ratio estimation
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Scalable Minimum-Volume Simplex Estimation with Non-asymptotic Analysis
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Algorithms for adaptive and heteroskedastic linear regression at the computational threshold
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Error Bounds for Statistical Estimators in BTL Model with Parametric Multivariate Utility Functions
The paper investigates preference elicitation under the Bradley‑Terry‑Luce model, focusing on estimating an unknown partworth vector from pairwise queries that satisfy a joint identifiability condition. It derives minimax lower bounds and shows that the canonical maximum likelihood estimator (MLE) exists, is unique, and achieves near‑optimal error rates once the sample size exceeds a design‑dependent threshold, without requiring compactness constraints or external regularizers. The analysis decomposes the estimation error into a linear stochastic term, a second‑order bias, and a higher‑order remainder, providing a unified non‑asymptotic theory for parametric utility elicitation.
arXiv AI
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A Temporal Spatial Minimax Rate for Smoothly-Varying Distributions in Wasserstein Space
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