Dynamic Regret of Strongly Adaptive Methods
By Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou. 2017
127 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].”
- Proximal Online Gradient is Optimum for Dynamic Regret (2018, background): “Online learning [Zinkevich, 2003, Shalev-Shwartz, 2012, Hazan, 2016, Mohri and Yang, 2016, Zhang et al., 2018, Jun et al., 2017, Jain et al., 2012, Zhang et al., 2017b] is a hot research topic for the last decade of years, due to its application in practices such as online recommendation [Chaudhuri…”
- Adaptive Online Learning in Dynamic Environments (2018, background): “The relation between adaptive regret and restricted dynamic regret is investigated by Zhang et al. [2018b].”
- Understand Dynamic Regret with Switching Cost for Online Decision Making (2019, background): “Many other previous researches investigate the regret under dierent denitions of dynamics such as parameter variation [19, 34, 44, 47], functional variation [7, 25, 46], gradient variation [17], and the mixed regularity [16, 24].”
- Proximal Online Gradient Is Optimum for Dynamic Regret: A General Lower Bound (2021, background): “Other regularities including the functional variation [5], [32]–[34], the gradient variation [35], and the mixed regularity [28], [36], [37] have been investigated to bound the dynamic regret.”