Efficient Optimal Learning for Contextual Bandits

We address the problem of learning in an online setting where the learner repeatedly observes features, selects among a set of actions, and receives reward for the action taken. We provide the first efficient algorithm with an optimal regret. Our algorithm uses a cost sensitive classification learner as an oracle and has a running time $\mathrm{polylog}(N)$, where $N$ is the number of classification rules among which the oracle might choose. This is exponentially faster than all previous algorithms that achieve optimal regret in this setting. Our formulation also enables us to create an algorithm with regret that is additive rather than multiplicative in feedback delay as in all previous work

A comparison of forecasting methods: fundamentals, polling, prediction markets, and experts

We compare Oscar forecasts derived from four data types (fundamentals, polling, prediction markets, and domain experts) across three attributes (accuracy, timeliness and cost effectiveness). Fundamentals-based forecasts are relatively expensive to construct, an attribute the academic literature frequently ignores, and update slowly over time, constraining their accuracy. However, fundamentals provide valuable insights into the relationship between key indicators for nominated movies and their chances of victory. For instance, we find that the performance in other awards shows is highly predictive of the Oscar victory whereas box office results are not. Polling- based forecasts have the potential to be both accurate and timely. Timeliness requires incentives for frequent responses by high-information users. Accuracy is achieved by a proper transformation of raw polls. Prediction market prices are accurate forecasts, but can be further improved by simple transformations of raw prices, yielding the most accurate forecasts in our study. Expert forecasts exhibit some characteristics of fundamental models, but are generally not comparatively accurate or timely. This study is unique in both comparing and aggregating four traditional data sources, and considering critical attributes beyond accuracy. We believe that the results of this study generalize to many other domains