Contrastive pair presentations yield exact identifiability characterizations via a geometric refinement of Angluin's condition, a new contrastive closure dimension for generation, mutual incomparability with text identification, and a single algorithm that tolerates any finite corruption budget.
2010 IEEE 51st annual symposium on foundations of computer science , pages=
3 Pith papers cite this work. Polarity classification is still indexing.
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Post-processing via random selection or linear combination of differentially private models allows meeting arbitrary target privacy parameters without additional training.
DP-FLogTinyLLM combines federated learning, differential privacy, and LoRA-tuned tiny LLMs to match centralized log anomaly detection performance on Thunderbird and BGL datasets while preserving privacy.
citing papers explorer
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Contrastive Identification and Generation in the Limit
Contrastive pair presentations yield exact identifiability characterizations via a geometric refinement of Angluin's condition, a new contrastive closure dimension for generation, mutual incomparability with text identification, and a single algorithm that tolerates any finite corruption budget.
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Differentially Private Model Merging
Post-processing via random selection or linear combination of differentially private models allows meeting arbitrary target privacy parameters without additional training.
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DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs
DP-FLogTinyLLM combines federated learning, differential privacy, and LoRA-tuned tiny LLMs to match centralized log anomaly detection performance on Thunderbird and BGL datasets while preserving privacy.