regrett
DifferentiallyPrivateOnline-to-BatchforSmooth Losses
In addition to solving (1), we also wish topreserve privacyfor the people who contributed to the dataset Z.
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses
Recently, researchers from convex optimization proposed the notions of "relative Lipschitz continuity" and "relative strong convexity". Both of the notions are generalizations oftheirclassicalcounterparts.
Country:
- North America > United States > New Jersey > Mercer County > Princeton (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.69)
SyntheticDataGenerators-SequentialandPrivate AnonymousAuthor(s) Affiliation Address email
Our proof relies on anewtype of synthetic data generator,Sequential Synthetic14 Data Generators, which we believe may be of interest of their own right.
Country:
- Oceania > Australia > New South Wales > Sydney (0.04)
- North America > United States > New York > Richmond County > New York City (0.04)
- North America > United States > New York > Queens County > New York City (0.04)
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