Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds

Cited by: 0|Bibtex|Views21|Links

Abstract:

We revisit the problem of online learning with sleeping experts/bandits: in each time step, only a subset of the actions are available for the algorithm to choose from (and learn about). The work of Kleinberg et al. [2010] showed that there exist no-regret algorithms which perform no worse than the best ranking of actions asymptotically...More

Code:

Data:

Your rating :
0

 

Tags
Comments