Improved Second-Order Bounds for Prediction with Expert Advice

Fast rates for prediction with limited expert advice

We investigate the problem of minimizing the excess generalization error with respect to the best expert prediction in a finite family in the stochastic setting, under limited access to information. We assume that the learner only has access to a limited number of expert advices per training round, as well as for prediction. Assuming that the loss function is Lipschitz and strongly convex, we show that if we are allowed to see the advice of only one expert per round for T rounds in the training phase, or to use the advice of only one expert for prediction in the test phase, the worst-case exce…

Constant regret for sequence prediction with limited advice

We investigate the problem of cumulative regret minimization for individual sequence prediction with respect to the best expert in a finite family of size K under limited access to information. We assume that in each round, the learner can predict using a convex combination of at most p experts for prediction, then they can observe a posteriori the losses of at most m experts. We assume that the loss function is range-bounded and exp-concave. In the standard multi-armed bandits setting, when the learner is allowed to play only one expert per round and observe only its feedback, known optimal r…

聚合专家误差的紧致界限

Sharp bounds on aggregate expert error

研究条件独立专家二元建议聚合的最优决策错误概率，在一般非对称情况下提出精确上下界，并证明其等价于估计两个乘积分布间的总变差距离。