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3,686 papers
The various kernel methods that are encountered are surveyed and it is shown that each can be derived from one or the other of two competing strategies: *projection* or *restricted reconstruction*.
We endeavour to estimate numerous multi-dimensional means of various probability distributions on a common space based on independent samples. Our approach involves forming estimators through convex combinations of empirical means derived from these samples. We introduce two strategies to find appropriate data-dependen...
A utility-based framework that incorporates the consequences of decisions for the affected individuals and groups into the assessment of fairness and shows that a decision-making process can satisfy $\varepsilon$-fairness while nevertheless being maximally unfair once the utilities associated with its outcomes are take...
We study the Bayes-optimal spherical linear model as the ambient dimension and sample size grow proportionally, under a quantitative Marchenko--Pastur spectral-regularity condition on the design. This condition is satisfied by normalized i.i.d. designs with standardized entries of finite fourth moment, but does not req...
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One hospital runs bloods and imaging at the same time. Another runs them one after the other, in either order, equally often. Knowing which actually happened, and how it is recorded in data, is critical for all operational managers. In process mining, the standard approach is to construct an event log, and attempt to d...
Diagnostics limit explanations based solely on terminal optimization accuracy and distinguish numerical cost from quality, rank coverage and freshness; they establish neither practical-tracker convergence nor predictive or deployment benefits from basis availability.
This paper proposes a model-based bootstrap framework for uncertainty quantification of OPE in finite-horizon, time-inhomogeneous Markov decision processes (MDPs), and establishes bootstrap distributional consistency, asymptotically valid confidence intervals, and consistent variance estimation for the target policy va...
We study smooth strongly convex--strongly concave minimax optimization with general nonlinear coupling in the deterministic unconstrained setting. We propose a pure single-loop damped extragradient method with fixed parameters and two new full-gradient evaluations per iteration after one initialization query. The metho...
We comment on the optimally-transported generalized method of moments (OTGMM) estimator proposed by Schennach&Starck (2026a) and give counterexamples to Theorems 2-6 under their stated assumptions. First, the assumptions used in the small-error analysis are insufficient for consistency in Theorem 2 and asymptotic norma...
This work analyzes how well Sobolev functions in W^{n,p} can be approximated by neural networks with width $W$, depth $L$ and path norm bounded by $K$ when the approximation error is measured in the $W^{1,p}$-norm.
A leave-a-window-out estimator is proposed, which deletes a window of length $\tau$ after each index before forming the empirical measure and reduces to leave-one-out at $\tau = 1$.
A unified perspective based on null importance is developed: a population-level characterization of when a feature is irrelevant under a specified notion of relevance, which provides a common statistical language for relating scientific questions, data-generating assumptions, and algorithms, and clarify the conclusions...
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
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Health & Bioscience
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Data Mining & Modeling
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Nov 3, 2026
Is your company’s AI sovereign? A practical guide for CIOs on AI vendor lock-in, EU Data Act switching, cloud sovereignty, and testing your exit plan before you need it. The post Sovereignty Is the Option to Leave appeared first on GPT-Lab.
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