AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,683 papers
Reinforcement learning (RL) embodies a foundational paradigm for sequential decision-making in the face of uncertainty. The rapid growth of model complexity in emerging applications has significantly amplified the challenges of achieving data-efficient RL, particularly in sample-starved scenarios where data collection...
This study proposes PDE-residual-informed Kriging (PRIK) based on a PDE-residual kernel, which makes PRIK yield the posterior mean with minimum integrated squared PDE residual, and establishes regularity and stability conditions for the posterior mean, together with a nonasymptotic bound for its PDE-residual norm.
Direct Preference Optimization (DPO) treats every reported comparison as correct, yet preference data contain reversed, tied or otherwise noisy comparisons. RW-DPO gives each comparison an unknown reliability weight $w_i\in[0,1]$ that enters the likelihood linearly -- DPO is $w\equiv1$, a reversed comparison $w_i=0...
Conditional generation of time-series paths is a central problem in probabilistic modeling, where the goal is to simulate future trajectories that are both realistic and responsive to external context. It has direct applications in finance and economics. Neural stochastic differential equations provide a natural framew...
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ReCouple is introduced, a controllable framework that constructs coarse-grained OT couplings, to better understand rollout-free OT-based post-training post-training quality, and reveals an NFE-dependent interplay between coupling optimality, neural realizability, and generation quality.
Simplicial neural networks (SNNs) are powerful tools for prediction tasks on higher-order relational datasets stored on simplicial complexes (SCs), a higher-order generalization of graphs. However, SNNs usually lack rigorous uncertainty quantification (UQ), limiting their usefulness in high-stakes scenarios. We propose...
Modern data analysis pipelines increasingly rely on large datasets assembled through retrieval, scraping, automated filtering, and post-hoc curation, where the observed sample is often a contaminated mixture rather than the target population. Standard distributional objectives either ignore contamination and converge t...
We study adaptive UAV sampling for radio environment map (REM) construction from non-Gaussian IoT observations. Integrated nested Laplace approximation(INLA) updates a Mat\'ern GMRF posterior, with waypoints selected by entropy pre-screening and Sherman--Morrison lookahead over the reachable set. We show that the likel...
Learning a shared subspace in multitask learning is an important statistical problem, yet exact rates remain poorly understood in many of the formal settings considered in the literature. To address this gap, which arises from limitations of existing upper-bound techniques, we propose a general multitask lemma. The lem...
Community detection in attributed networks is one of the core tasks of complex network analysis. The stochastic block model (SBM) and its variants are able to detect generalized network structures such as assortative, disassortative and mixed structures. However, existing generative methods either ignore node heterogen...
Instance-dependent limits for bandit regret, sequential tests, best-arm identification and Markov decision processes are proved by one chain. It runs from a divergence decomposition through data processing to a statistic, a testing step that turns a class guarantee into a requirement on the history, and optimization ov...
This work tracks the gradient-flow trajectory, showing that student neurons first collapse toward a bisector saddle before escaping and specializing to teacher neurons, and provides both a landscape and dynamics account of the multi-phase symmetry-breaking behavior that arises in a simple multi-index model and under st...
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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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.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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