AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,686 papers
It is argued that ARD endures because of its seamless integration with kernel learning, universal software support, and low cost, and it is argued that ARD delivers effective rather than exact sparsity.
It is shown that a studentized multiplier bootstrap of the GP derivative process repairs both its validity through an invariance principle for quadratic forms, and it is shown that asymptotic family-wise and false-discovery-rate control across inputs are obtained.
This work develops a theoretical framework for characterizing the approximation and generalization errors of transformer-based operator learning, and derives a universal approximation error of transformer-based operator learning for H\"older-regular operators".
Domain-Adapted Diffusion (DA-Diff), a multi-source domain adaptation framework for conditional diffusion models based on weighted empirical risk minimization over both target and source domains, is proposed andExperiments demonstrate that DA-Diff improved conditional generation quality compared with transfer-learning d...
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Weighted split conformal prediction reweights calibration scores by the likelihood ratio between the test and training covariate distributions and guarantees marginal coverage under covariate shift. We study its coverage conditional on the calibration data. An elementary argument, based on a single concentration inequa...
It is found that multiple responses provide a qualitatively stronger benefit in this setting of generative systems, and is shown that a greedy multiplicative-weights learner achieving the upper bounds without any assumption on demonstrator quality is possible.
Finite-sample bounds are derived that separate the effects of critic estimation, neural drift regression, finite-sample SNIS, diffusion discretization, and inherited actor error on expected average policy suboptimality.
A convex combination of one-step and two-step residuals with a fixed mixing weight is considered to provide a natural bias--variance trade-off between approximation error under a restricted value-function class and the increased variance arising from multi-step importance weighting.
TabCon is developed, an amortized inference system built on a tabular foundation model that produces confidence intervals for new datasets through a simple forward pass, and offers considerably greater computational efficiency than the classical bootstrap procedure.
Retrieval-augmented generation (RAG) lets language models answer questions more accurately by consulting relevant documents. Many valuable collections, such as medical records, cannot be pooled because of privacy rules. Federated RAG leaves each collection with its owner, or node, which scores candidate answers from it...
We propose sliced Orlicz-Wasserstein (SOW) distance which is a generalization of sliced Wasserstein (SW) distance. SOW replaces the $L^p$ norm in SW with a Luxemburg norm cost induced by an Orlicz function $\phi$. First, we prove that SOW distance is a metric on the space of measures with finite Orlicz norm, and show t...
A doubly-anchored DRO framework whose ambiguity set is the intersection of $\phi$-divergence balls centered at the source law and a source-completed target reference law, the latter pairing the target covariate law with the source conditional law is introduced.
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Health & Bioscience
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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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