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The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure., , , and . CoRR, (2019)Open Problem: Do Good Algorithms Necessarily Query Bad Points?, , , , , and . COLT, volume 99 of Proceedings of Machine Learning Research, page 3190-3193. PMLR, (2019)Learning Planar Ising Models., , , and . J. Mach. Learn. Res., (2016)Making the Last Iterate of SGD Information Theoretically Optimal., , and . SIAM J. Optim., 31 (2): 1108-1130 (2021)All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP)., , , and . CoRR, (2022)Sample Efficient Linear Meta-Learning by Alternating Minimization., , , and . CoRR, (2021)Computing Matrix Squareroot via Non Convex Local Search., , , and . CoRR, (2015)Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot., , , and . AISTATS, volume 54 of Proceedings of Machine Learning Research, page 479-488. PMLR, (2017)One-Bit Compressed Sensing: Provable Support and Vector Recovery., , , and . ICML (3), volume 28 of JMLR Workshop and Conference Proceedings, page 154-162. JMLR.org, (2013)Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent., , and . COLT, volume 75 of Proceedings of Machine Learning Research, page 1042-1085. PMLR, (2018)