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New algorithm offers near-optimal regret for private online expert prediction

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new differentially private algorithm for online prediction from experts. This algorithm achieves a regret bound of \tilde{O}(\sqrt{L^\star}), which is a significant improvement over existing methods in the low-loss regime where $L^\star$ represents the cumulative loss of the best expert. The new bound is comparable to non-private algorithms, indicating only a minor cost for privacy. However, the algorithm's performance is dependent on the input sequence being oblivious, and a lower bound of \tilde{\Omega}(\sqrt{T}) regret is unavoidable for adaptive inputs. AI

IMPACT This research advances the field of differentially private machine learning, potentially enabling more secure and efficient expert prediction systems.

RANK_REASON The item is a research paper detailing a new algorithm for a machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]

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New algorithm offers near-optimal regret for private online expert prediction

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The item is a research paper detailing a new algorithm for a machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Private Online Prediction from Experts with Small Losses

    We consider online prediction from experts, a fundamental problem in machine learning, under differential privacy constraints. Existing private algorithms achieve near-optimal regret $\tilde{O}(\sqrt{T})$, where $T$ is the time horizon. However, this bound becomes suboptimal when…

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