Shifting Regret, Mirror Descent, and Matrices

By András György, Csaba Szepesvari. 2016

33 citations recorded by the source corpus.

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Citations in the corpus

Decentralized Online Learning: Take Benefits from Others’ Data without Sharing Your Own to Track Global Trend (2019, methodology): “…learning algorithms has been widely studied for decades [Zinkevich, 2003, Hall and Willett, 2015, 2013, Jadbabaie et al., 2015, Yang et al., 2016, Bedi et al., 2018, Zhang et al., 2017a, Mokhtari et al., 2016, Zhang et al., 2018b, Gy¨orgy and Szepesv´ari, 2016, Wei et al., 2016, Zhao et al., 2018].”
Bandit Algorithms (2020, methodology): “For Gaussian bandits with unknown mean and variance Thompson sampling is asymptotically optimal for some priors, but not others – even quite natural ones [Honda and Takemura, 2014]. The Bayesian analysis of Thompson sampling based on confidence intervals is due to Russo and Van Roy [2014b]. Recently”
Proximal Online Gradient is Optimum for Dynamic Regret (2018, background): “[38] studies a dynamic regret in a slightly more general setting than (3) by relaxing the distance metric ‖yt+1 − yt‖ to a general lp norm ‖yt+1 − yt‖p with p ∈ (1, 2].”
Understand Dynamic Regret with Switching Cost for Online Decision Making (2019, methodology): “In the worst case, if the environment provides an adversary loss function to maximize the operating cost based on the decision made by MD-OCO, MD-OCO has to lead to O (√TD + √T ) regret even for the case of no switching cost [20].”
Competing with Automata-based Expert Sequences (2018, background): “Other works that have recovered FIXED-SHARE (e.g. [Vovk, 1999, Cesa-Bianchi and Lugosi, 2006, Cesa-Bianchi et al., 2012, Koolen and de Rooij, 2013, György and Szepesvári, 2016]) have generally viewed the algorithm itself as the main focus and have not systematically derived it from first…”