Budgeted Prediction with Expert Advice

We consider a budgeted variant of the problem of learning from expert advice with N experts. Each queried expert incurs a cost and there is a given budget B on the total cost of experts that can be queried in any prediction round. We provide an online learning algorithm for this setting with regret after T prediction rounds bounded by O C B log(N)T , where C is the total cost of all experts. We complement this upper bound with a nearly matching lower bound Ω C B T on the regret of any algorithm for this problem. We also provide experimental validation of our algorithm.

• Type: Journal
• Year: 2015
• Where: AAAI
• Authors: Kareem Amin, Satyen Kale, Gerald Tesauro, Deepak S. Turaga
• Keywords: AAAI 2015, Intelligent Agents