AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work analyzes the original same-step coordinate-wise AdaGrad under generalized smoothness and heavy-tailed noise with bounded variance and shows that clipping is a structural stabilizer of the adaptive geometry rather than merely a robustness heuristic for AdaGrad under heavy-tailed noise.
This work introduces ERASE, Erasure via Reconstructive Adversarial Signal Editing, a framework for on-the-go forgetting that suppresses the observable influence of private data without modifying model weights, establishing a scalable, regulation-aligned pathway for continual, privacy-conscious learning.
SOVER, an LLM-assisted SMT framework that separates semantic mapping from formal certification, is introduced, and Z3 checks domain cross-feasibility and global objective-order preservation for mixed-integer linear formulations, while dReal provides tolerance-aware feasibility/range and $\epsilon$-argmin checks for con...
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