New Potential-Based Bounds for the Geometric-Stopping Version of Prediction with Expert Advice
This work addresses a theoretical gap in machine learning for sequential decision-making, but it is incremental as it builds on existing frameworks.
The paper tackles the online prediction with expert advice problem by extending a potential-based framework to the geometric stopping version, deriving new explicit lower and upper bounds that are optimal for two and three experts.
This work addresses the classic machine learning problem of online prediction with expert advice. A new potential-based framework for the fixed horizon version of this problem has been recently developed using verification arguments from optimal control theory. This paper extends this framework to the random (geometric) stopping version. To obtain explicit bounds, we construct potentials for the geometric version from potentials used for the fixed horizon version of the problem. This construction leads to new explicit lower and upper bounds associated with specific adversary and player strategies. While there are several known lower bounds in the fixed horizon setting, our lower bounds appear to be the first such results in the geometric stopping setting with an arbitrary number of experts. Our framework also leads in some cases to improved upper bounds. For two and three experts, our bounds are optimal to leading order.