Dynamic regret of convex and smooth functions
WebWe investigate online convex optimization in non-stationary environments and choose the dynamic regret as the performance measure, defined as the difference between cumulative loss incurred by the online algorithm and that of any feasible comparator sequence. WebJul 7, 2024 · Specifically, we propose novel online algorithms that are capable of leveraging smoothness and replace the dependence on T in the dynamic regret by problem-dependent quantities: the variation in gradients of loss functions, and the cumulative loss of the comparator sequence.
Dynamic regret of convex and smooth functions
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WebT) small-loss regret bound when the online convex functions are smooth and non-negative, where F∗ T is the cumulative loss of the best decision in hindsight, namely, F∗ T = PT t=1 ft(x ∗) with x∗ chosen as the offline minimizer. The key ingredient in the analysis is to exploit the self-bounding properties of smooth functions. WebJun 10, 2024 · When multiple gradients are accessible to the learner, we first demonstrate that the dynamic regret of strongly convex functions can be upper bounded by the …
WebJun 10, 2024 · In this paper, we present an improved analysis for dynamic regret of strongly convex and smooth functions. Specifically, we investigate the Online Multiple Gradient Descent (OMGD) algorithm proposed by Zhang et al. (2024). Webthe dynamic regret R∗ T can be upper bounded by O(p TP∗ T) [Yang et al., 2016]. If all the functions are strongly convex and smooth, the upper bound of R∗ T can be improved to O(P∗ T) [Mokhtari et al., 2016]. The O(P∗ T) rate is also achievable when all the functions are convex and smooth, and all the minimizers x∗
WebJun 10, 2024 · When multiple gradients are accessible to the learner, we first demonstrate that the dynamic regret of strongly convex functions can be upper bounded by the minimum of the path-length and the ... WebDynamic Local Regret for Non-convex Online Forecasting Sergul Aydore, Tianhao Zhu, Dean P. Foster; NAOMI: Non-Autoregressive Multiresolution Sequence Imputation Yukai Liu, ... Variance Reduced Policy Evaluation with Smooth Function Approximation Hoi-To Wai, Mingyi Hong, Zhuoran Yang, Zhaoran Wang, Kexin Tang;
WebApr 26, 2024 · Different from previous works that only utilize the convexity condition, this paper further exploits smoothness to improve the adaptive regret. To this end, we develop novel adaptive algorithms... sl-t1673fw 드라이버http://www.lamda.nju.edu.cn/zhaop/publication/arXiv_Sword.pdf sls your partner for lifehttp://proceedings.mlr.press/v144/zhao21a/zhao21a.pdf#:~:text=To%20minimize%20the%20dynamic%20regret%20of%20strongly%20convex,following%20dynamic%20regret%20ft%28xt%29%20t%3D1%20ft%28x%03t%29%14%20O%28minfPT%3BSTg%29%3A%20%283%29t%3D1 slsy tricycle companyWebTg) dynamic regret.Yang et al.(2016) disclose that the O(P T) rate is also attainable for convex and smooth functions, provided that all the minimizers x t’s lie in the interior of the feasible set X. Besides,Besbes et al.(2015) show that OGD with a restarting strategy attains an O(T2=3V1=3 T) dynamic regret when the function variation V soil finisher with rolling basketWebJun 10, 2024 · 06/10/20 - In this paper, we present an improved analysis for dynamic regret of strongly convex and smooth functions. Specifically, we invest... soil finisher vs vertical tillageWebdynamic regret of convex cost functions [3], [10], [11], which can be improved to O(p TC T) when prior knowledge of C and T is available [12]. The path length has also been recently used in the study of online convex optimization with constraint violation [13], where upper bounds of O(p T(1+C T)) and O(p T) are derived on the dynamic regret and ... soil finisher horsepower per footWebApr 10, 2024 · on the dynamic regret of the algorithm when the regular part of the cost is convex and smooth. If the Bregman distance is given by the Euclidean distance, our result also im- soilfish co. ltd