Adversarial Online Learning with Temporal Feedback Graphs
<|reference_start|>Adversarial Online Learning with Temporal Feedback Graphs: We study a variant of prediction with expert advice where the learner's action at round $t$ is only allowed to depend on losses on a specific subset of the rounds (where the structure of which rounds' losses are visible at time $t$ is provided by a directed "feedback graph" known to the learner). We present a novel learning algorithm for this setting based on a strategy of partitioning the losses across sub-cliques of this graph. We complement this with a lower bound that is tight in many practical settings, and which we conjecture to be within a constant factor of optimal. For the important class of transitive feedback graphs, we prove that this algorithm is efficiently implementable and obtains the optimal regret bound (up to a universal constant).<|reference_end|>
@article{gatmiry2024adversarial,
    title={Adversarial Online Learning with Temporal Feedback Graphs},
    author={Khashayar Gatmiry, Jon Schneider},
    journal={arXiv preprint arXiv:2407.00571},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00571},
    primaryClass={cs.LG}
}