Risk-Averse Online Optimization with Time-Varying Distributions

The core message of this paper is to design a risk-averse learning algorithm that achieves sub-linear dynamic regret in online convex optimization problems with time-varying distributions, using Conditional Value at Risk (CVaR) as the risk measure.