暂无分享,去创建一个
[1] Elad Hazan,et al. Introduction to Online Convex Optimization , 2016, Found. Trends Optim..
[2] Jan Vondrák,et al. High probability generalization bounds for uniformly stable algorithms with nearly optimal rate , 2019, COLT.
[3] Elad Hazan,et al. Logarithmic regret algorithms for online convex optimization , 2006, Machine Learning.
[4] Ohad Shamir,et al. How Good is SGD with Random Shuffling? , 2019, COLT 2019.
[5] André Elisseeff,et al. Stability and Generalization , 2002, J. Mach. Learn. Res..
[6] Jeffrey Scott Vitter,et al. Random sampling with a reservoir , 1985, TOMS.
[7] Dimitris Papailiopoulos,et al. Closing the convergence gap of SGD without replacement , 2020, ICML.
[8] Elad Hazan,et al. An optimal algorithm for stochastic strongly-convex optimization , 2010, 1006.2425.
[9] Dan Garber,et al. Online Convex Optimization in the Random Order Model , 2020, ICML.
[10] Marten van Dijk,et al. A Unified Convergence Analysis for Shuffling-Type Gradient Methods , 2020, J. Mach. Learn. Res..
[11] Ohad Shamir,et al. Without-Replacement Sampling for Stochastic Gradient Methods , 2016, NIPS.
[12] Sébastien Bubeck,et al. Convex Optimization: Algorithms and Complexity , 2014, Found. Trends Mach. Learn..
[13] Luc Devroye,et al. Distribution-free performance bounds with the resubstitution error estimate (Corresp.) , 1979, IEEE Trans. Inf. Theory.
[14] Yoram Singer,et al. Train faster, generalize better: Stability of stochastic gradient descent , 2015, ICML.
[15] Ben London,et al. A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent , 2017, NIPS.
[16] Suvrit Sra,et al. Random Shuffling Beats SGD after Finite Epochs , 2018, ICML.
[17] Prateek Jain,et al. SGD without Replacement: Sharper Rates for General Smooth Convex Functions , 2019, ICML.
[18] Raef Bassily,et al. Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses , 2020, NeurIPS.
[19] Suvrit Sra,et al. SGD with shuffling: optimal rates without component convexity and large epoch requirements , 2020, NeurIPS.
[20] Ohad Shamir,et al. Learnability, Stability and Uniform Convergence , 2010, J. Mach. Learn. Res..
[21] W. Rogers,et al. A Finite Sample Distribution-Free Performance Bound for Local Discrimination Rules , 1978 .
[22] Luc Devroye,et al. Distribution-free inequalities for the deleted and holdout error estimates , 1979, IEEE Trans. Inf. Theory.
[23] Asuman E. Ozdaglar,et al. Why random reshuffling beats stochastic gradient descent , 2015, Mathematical Programming.
[24] Martin Zinkevich,et al. Online Convex Programming and Generalized Infinitesimal Gradient Ascent , 2003, ICML.