"Trading without Regret" by Dr. Michael Kearns

Talk by Dr. Michael Kearns, Professor at the Computer and Information Science Department at the University of Pennsylvania. From QuantCon NYC 2017.
No-regret learning is a collection of tools designed to give provable performance guarantees in the absence of any statistical or other assumptions on the data (!), and thus stands in stark contrast to most classical modeling approaches. With origins stretching back to the 1950s, the field has yielded a rich body of algorithms and analyses that covers problems ranging from forecasting expert advice to online convex optimization.
Dr. Kearns surveys the field, with special emphasis on applications to quantitative finance problems, including portfolio construction and inventory risk.
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[@samidelhi6150]

Secondly linear decomposition of risk via projection results doesn't add up back to the whole ambient space , because of non-linear Dynamics as well as non-symmetry

[@samidelhi6150]

Great work, but I don't think the Sharpe ratio is meaningful especially in the context of ML in general , it does not tell you much even the dynamic version of it