In this talk I will show some very recent results on optimal strategies for betting on individual sequences of binary outcomes, that is betting against a non-stochastic coin. This naturally extends the well-known Kelly strategy to the adversarial domain.
Moreover, I will show some surprising links between betting, online learning, and adaptive stochastic optimization. Solving optimally coin betting will allow to solve optimally all these problems, with the very same algorithm.
Empirically results will be shown as well.
Francesco Orabona is a Senior Research Scientist at Yahoo Labs, NY.
Previously, he was at the Toyota Technological Institute at Chicago, the University of Milan, and the Idiap Research Institute. His current research interest is parameter-free machine learning. In particular, he is interested in online learning, domain adaptation, and batch/stochastic convex optimization. He is (co)author of more than 50 peer reviewed papers.