Interior-point methods for full-information and bandit online learning, IEEE Transactions on Information Theory, vol.58, issue.7, pp.4164-4175, 2012. ,
Hannan consistency in on-line learning in case of unbounded losses under partial monitoring, International Conference on Algorithmic Learning Theory, pp.229-243, 2006. ,
Online learning for adversaries with memory: price of past mistakes, Advances in Neural Information Processing Systems, pp.784-792, 2015. ,
Online bandit learning against an adaptive adversary: from regret to policy regret, Proc. 29th ICML, 2012. ,
The nonstochastic multiarmed bandit problem, SIAM Journal on Computing, vol.32, issue.1, pp.48-77, 2002. ,
Towards minimax policies for online linear optimization with bandit feedback, Annual Conference on Learning Theory, vol.23, pp.41-42, 2012. ,
Combinatorial bandits, Journal of Computer and System Sciences, vol.78, issue.5, pp.1404-1422, 2012. ,
Delay and cooperation in nonstochastic bandits, Conference on Learning Theory, pp.605-622, 2016. ,
The price of bandit information for online optimization, Advances in Neural Information Processing Systems, pp.345-352, 2008. ,
Online learning with composite loss functions, Conference on Learning Theory, pp.1214-1231, 2014. ,
The blinded bandit: Learning with adaptive feedback, Advances in Neural Information Processing Systems, pp.1610-1618, 2014. ,
Asymptotic convergence in online learning with unbounded delays, 2016. ,
Introduction to online convex optimization, Foundations and Trends R in Optimization, vol.2, issue.3-4, pp.157-325, 2016. ,
Online learning under delayed feedback, International Conference on Machine Learning, pp.1453-1461, 2013. ,
Delay-tolerant online convex optimization: Unified analysis and adaptive-gradient algorithms, AAAI, vol.16, pp.1744-1750, 2016. ,
Appendix B) it is shown how lower bounds for Gaussian losses can be converted into lower bounds for losses in, Adversarial delays in online stronglyconvex optimization, 2015. ,