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Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork

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arXiv:2606. 26772v1 Announce Type: cross Abstract: Differentially private (DP) training of neural networks is often hindered by the large amount of noise required by gradient-based methods such as DP-SGD, which repeatedly inject high-dimensional noise in parameter space throughout training.

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Authors: Naoki Nishikawa, Shokichi Takakura, Satoshi Hasegawa
Released: 2026-06-26 (12d ago)
Topic: Related to current project
Relation to my work: Read next because Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "Follow-up to #354: cascading chunk-binding — does A→B, B→C, C→D propagate the full chain on a recipient trained only to emit A?". Matching terms: under, rate, trained, lora, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.26772

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