Risk-averse learning with delayed feedback
The study investigates risk-averse learning using Conditional Value at Risk (CVaR) to manage potential losses when feedback is delayed randomly but within bounds. It develops two zeroth-order optimization algorithms: one-point and two-point, to estimate CVaR gradients. The two-point algorithm proves superior by achieving a smaller regret bound, making it more effective in real-world scenarios with time lags.