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Title: The importance of better models in stochastic optimization
Standard stochastic optimization methods are brittle, sensitive to stepsize choice and other algorithmic parameters, and they exhibit instability outside of well-behaved families of objectives. To address these challenges, we investigate models for stochastic optimization and learning problems that exhibit better robustness to problem families and algorithmic parameters. With appropriately accurate models—which we call the aprox family—stochastic methods can be made stable, provably convergent, and asymptotically optimal; even modeling that the objective is nonnegative is sufficient for this stability. We extend these results beyond convexity to weakly convex objectives, which include compositions of convex losses with smooth functions common in modern machine learning. We highlight the importance of robustness and accurate modeling with experimental evaluation of convergence time and algorithm sensitivity.  more » « less
Award ID(s):
1553086
PAR ID:
10383072
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the National Academy of Sciences
Volume:
116
Issue:
46
ISSN:
0027-8424
Page Range / eLocation ID:
22924 to 22930
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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