Asymptotic Optimality of Thompson Sampling for Risk-Averse Bandits with Sub-Gaussian Rewards
The episode discusses a paper proving the asymptotic optimality of Thompson Sampling for risk-averse bandits using sub-Gaussian rewards. Hosts explain that the algorithm achieves instance-optimal regret by requiring only continuity of the risk functional rho, making it applicable to complex measures like the Sharpe ratio.