POOL is a new RL algorithm that adds privacy protection in continuous spaces with one-sided feedback and achieves sample complexity matching known non-private lower bounds.
International colloquium on automata, languages, and programming , pages=
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 5roles
background 1polarities
background 1representative citing papers
Post-processing via random selection or linear combination of differentially private models allows meeting arbitrary target privacy parameters without additional training.
FedProxy replaces weak adapters with a proxy SLM for federated LLM fine-tuning, outperforming prior methods and approaching centralized performance via compression, heterogeneity-aware aggregation, and training-free fusion.
Systematic experiments show that text decomposition methods and privacy budget allocation strategies produce significantly different privacy-utility trade-offs even under comparable total epsilon budgets.
citing papers explorer
-
Privacy Preserving Reinforcement Learning with One-Sided Feedback
POOL is a new RL algorithm that adds privacy protection in continuous spaces with one-sided feedback and achieves sample complexity matching known non-private lower bounds.
-
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.
-
FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion
FedProxy replaces weak adapters with a proxy SLM for federated LLM fine-tuning, outperforming prior methods and approaching centralized performance via compression, heterogeneity-aware aggregation, and training-free fusion.
-
A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation
Systematic experiments show that text decomposition methods and privacy budget allocation strategies produce significantly different privacy-utility trade-offs even under comparable total epsilon budgets.
- Modulated learning for private and distributed regression with just a single sample per client device