Learning Optimal Forecast Aggregation in Partial Evidence Environments
We consider the forecast aggregation problem in repeated settings where the forecasts are of a binary state of nature. In each period multiple experts provide forecasts about the state. The goal of…
Efficient Online Linear Optimization with Approximation Algorithms
We revisit the problem of online linear optimization in the case where the set of feasible actions is accessible through an approximated linear optimization oracle with a factor α multiplicative a…
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