Corpus ID: 220496455

Relaxing the I.I.D. Assumption: Adaptive Minimax Optimal Sequential Prediction with Expert Advice

@article{Bilodeau2020RelaxingTI,
  title={Relaxing the I.I.D. Assumption: Adaptive Minimax Optimal Sequential Prediction with Expert Advice},
  author={Blair Bilodeau and Jeffrey Negrea and Daniel M. Roy},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.06552}
}
We consider sequential prediction with expert advice when the data are generated stochastically, but the distributions generating the data may vary arbitrarily among some constraint set. We quantify relaxations of the classical I.I.D. assumption in terms of possible constraint sets, with I.I.D. at one extreme, and an adversarial mechanism at the other. The Hedge algorithm, long known to be minimax optimal in the adversarial regime, has recently been shown to also be minimax optimal in the I.I.D… Expand

References

SHOWING 1-10 OF 38 REFERENCES
Adaptation to Easy Data in Prediction with Limited Advice
On the optimality of the Hedge algorithm in the stochastic regime
An Optimal Algorithm for Stochastic and Adversarial Bandits
The Best of Both Worlds: Stochastic and Adversarial Bandits
Prediction with Expert Advice by Following the Perturbed Leader for General Weights
A second-order bound with excess losses
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