Unverified50% confidenceFactExact time
The previously best differentially private online expert prediction algorithm had a regret bound of Õ(√T); when L* is much smaller than T, this bound is far higher than the non-private lower bound Õ(√L*).
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/22/2026
First Seen
Valid until: 1/20/2027
Sources
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Related Claims
UnverifiedFor adaptively generated input (where the input may depend on the algorithm's previous outputs), even with L*=0, any differentially private algorithm cannot avoid Ω̃(√T) regret.72% similarUnverifiedThe algorithm's regret upper bound is O(√(L* log N) + √(T log N)/ε), where ε is the privacy parameter.71% similarVerifiedDifferential privacy mathematically guarantees that individual user behavior cannot be inferred from aggregated data by injecting calibrated noise into statistical results.64% similarUnverifiedLocal inference means data never leaves the local machine, making it attractive for privacy-sensitive scenarios while also reducing costs and providing offline availability.63% similarUnverifiedThe Littlestone dimension is finite if and only if a no-regret online learning algorithm exists, with a mistake bound of O(d log T).63% similar
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