Optimal Online Prediction in Adversarial Environments

: In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modeled as an adversary with whom the predictor competes. The predictor's aim is to minimize the regret, or the difference between the predictor's performance and the best performance among some comparison class, whereas the adversary aims to maximize the predictor's regret. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable. The first part of this talk presents two examples of online decision problems of this kind: a resource allocation problem from computational finance and a reactive approach to managing an enterprise's information security risks. In both cases, we present efficient strategies with near

• Type: Conference
• Year: 2010
• Where: ALT
• Authors: Peter L. Bartlett
• Keywords: ALT 2010, Computational Finance, Decision Problems, Machine Learning, Predictor's Regret