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# Constant regret for sequence prediction with limited advice

*Proceedings of The 34th International Conference on Algorithmic Learning Theory*, PMLR 201:1343-1386, 2023.

#### Abstract

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 regret bounds are of the order O(sqrt{KT}). We show that allowing the learner to play one additional expert per round and observe one additional feedback, improves substantially the guarantees on regret. We provide a strategy combining only p=2 experts per round for prediction and observing m \ge 2 experts’ losses. Its randomized regret (wrt. internal randomization of the learners’ strategy) is of order O((K/m) log(K delta^{-1})) with probability 1- delta, i.e., is independent of the horizon T (“constant” or “fast rate” regret) if (p \ge 2 and m \ge 3). We prove that this rate is optimal up to a logarithmic factor in K. In the case p=m=2, we provide an upper bound of order O(K^2 \log(K delta^{-1})), with probability 1-delta. Our strategies do not require any prior knowledge of the horizon T nor of the confidence parameter \delta. Finally, we show that if the learner is constrained to observe only one expert feedback per round, the worst-case regret is the “slow rate” Omega(sqrt{KT}), suggesting that synchronous observation of at least two experts per round is necessary to have a constant regret.