inconferenceon learning theory
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction
Difan Zou, Pan Xu, Quanquan Gu
Figure 3: Ex represents (c)-(d) ConB.
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A Bandit Approach to Sequential Experimental Design with False Discovery Control
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MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
Sumeet Katariya, Ardhendu Tripathy, Robert Nowak
Letusfocus understand Figure 1, which onthemeanst.
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Noise-tolerant fair classification
Alex Lamy, Ziyuan Zhong, Aditya K. Menon, Nakul Verma
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Alternating Mirror Descent for Constrained Min-Max Games
Notef = andg = are functions. Theorem 4.4 withrespecttok k1-norm; see Example A.3 in Section A.2.1.
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Pareto_frontier_for_model_selection_in_Contextual_bandits_main
In contextual anditisunknoFoster[2020b] stated contextual incontextual baseline.
Robustand Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization
Lastly, [orkdoesnot heavy-tailedsetting. and Notations thespectralnormof( trace.
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An Empirical Process Approachtothe Union Bound: Practical Algorithmsfor Combinatorialand Linear Bandits
Theorem 7 impliesthatifT O( log ( 1/ )[ + ] log ( (Z)) log ( log ( (Z)))), then Algorithm 3 returnsz withprobabilityatleast1 . Then, O(' log ( log (d))).
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