Regret Bounds for Log-Loss via Bayesian Algorithms

We study sequential probability assignment in the context of online learning under logarithmic loss and obtain tight lower and upper bounds for sequential minimax regret. Sequential minimax regret is defined as the minimum excess loss over data horizon $T$ that a predictor incurs over the best expert in a class, when the samples are presented sequentially and adversarially. Our upper bounds are established by applying Bayesian averaging over a novel “smooth truncated covering” of the expert class. This allows us to obtain tight (minimax) upper bounds that subsume the best known non-constructive bounds in an algorithmic fashion. For lower bounds, we reduce the problem to analyzing the fixed design regret via a novel application of Shtarkov sum adapted to online learning. We demonstrate the effectiveness of our approach by establishing tight regret bounds for a wide range of expert classes. In particular, we fully characterize the regret of generalized linear function with worst Lipschitz transform functions when the parameters are restricted to a unit norm $\ell _{s}$ ( $s\ge 2$ ) ball of dimension $d$ . We show that the regret grows as $\Theta (d\log T)$ when $d\le O(T^{s/(s+1)-\epsilon })$ for all $\epsilon >0$ (with precise constant 1 when $d\le e^{o(\log T)}$ ) and $\tilde {O}(T^{s/(s+1)})$ when $d\ge \Omega (T^{s/(s+1)})$ . Finally, we show that the Bayesian approach may not always be optimal if the support of the prior is included in the reference class itself.