Research map / Bandits and online learning / Online convex optimization and games
Optimal No-Regret Learning for One-Sided Lipschitz Functions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Hybrid binary search + KL (asymmetric bisection) algorithm for left-Lipschitz maximization using a potential function | LEFT-LIPSCHITZ MAXIMIZATION (1-left-Lipschitz f: [0,1]->[0,1]) · online deterministic noise-free feedback, T rounds | total regret | Helped |
| Naive equally spaced grid querying instead of adaptive scheme | LEFT-LIPSCHITZ MAXIMIZATION · noise-free one-sided Lipschitz maximization | total regret: O(L sqrt(T)) regret at best | Hurt |
| Maximum value hint (knowing max f) given to the algorithm | LEFT-LIPSCHITZ MAXIMIZATION · noise-free one-sided Lipschitz maximization | total regret: reduces regret from O(log log T) to O(1) | Helped |
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