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3,686 papers
It is shown how a limited budget for ground-truth data-annotation can be used via doubly-robust OPE with missing rewards, and the optimal annotation probabilities for sequential forward-monotone annotation protocols are characterized, and a feasible batch-adaptive implementation is provided.
The no-data criterion is a safe default, matching or improving on truncation for HSGP, VFF and VISH, with substantial gains for VISH and improvements over a recently developed selection heuristic for that basis family.
This manuscript formalizes the most popular model validation tools used in general insurance actuarial modeling. These include graphical tools like calibration plots, actual-vs-expected plots, lift charts, Murphy diagrams, as well as classical statistical tools such as Bregman losses, deviance losses, elementary losses...
Principal component analysis (PCA) is a fundamental tool to reduce the dimensionality of the data in many applications. PCA finds a few signal directions that contain most of the variability of the data by computing the eigenvectors of the sample covariance matrix. In this work, we focus on the spiked covariance model,...
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We study preference elicitation under the Bradley-Terry-Luce (BTL) model where the true partworth vector is unknown and has to be estimated as a parameter with elicited preference information. The set of selected pairwise queries is non-uniform, deterministic, and arbitrary over a collection of alternatives, provided t...
Causal tabular foundation models amortize effect estimation across synthetic mechanisms, but latent-effect supervision rewards posterior shrinkage rather than encoding the repeated-sample response needed in a fixed deployment population. We introduce fluctuation-supervised pretraining (FSP): each synthetic table is lab...
Counterfactual Tucker Diffusion (CFTDiff), which integrates the treatment mask and latent Tucker structure into conditional diffusion to recover this distribution given observed control outcomes through efficient nonlinear score learning in a low-dimensional core, and establishes high-probability error bounds.
This work studies looping, which repeatedly composes one update function with shared parameters; each output becomes the next input, and establishes an upper squared Hellinger risk bound for looped sieve maximum likelihood and a minimax lower bound for the jointly tuned untied family.
A sharp width--magnitude tradeoff at fixed depth is established using one elementary bounded $1$-Lipschitz Dyadic--Triangular Activation and yields a continuum of statistically optimal choices, ranging from unit parameter radius to fixed network size.
It is demonstrated that, for any $(f,\kappa)$-robust aggregator with coefficient $\kappa = O(f/n)$, where f is the number of adversarial clients and n is the total number of clients, convergence holds after an explicit sample burn-in.
This paper studies the estimation of a marginal regression function from independent units with repeated binary, count, or continuous responses using ReLU deep neural networks, and derives an oracle inequality for unequal cluster sizes and a rate for compositionally smooth functions.
DeepMVSA is proposed, which re-expresses the minimum-volume principle in neural implicit form and proves a non-asymptotic sample-complexity bound of the polynomial-time benchmark order for a localized surrogate estimator.
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
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Data Mining & Modeling
GPT-Lab
· gpt-lab.eu
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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