Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD
share
This work considers the problem of finding a first-order stationary point of a non-convex function with potentially unbounded smoothness constant using a stochastic gradient oracle. We focus on the class of (L_0,L_1)-smooth functions proposed by Zhang et al. (ICLR'20). Empirical evidence suggests that these functions more closely captures practical machine learning problems as compared to the pervasive L_0-smoothness. This class is rich enough to include highly non-smooth functions, such as exp(L_1 x) which is (0,𝒪(L_1))-smooth. Despite the richness, an emerging line of works achieves the 𝒪(1/√(T)) rate of convergence when the noise of the stochastic gradients is deterministically and uniformly bounded. This noise restriction is not required in the L_0-smooth setting, and in many practical settings is either not satisfied, or results in weaker convergence rates with respect to the noise scaling of the convergence rate. We develop a technique that allows us to prove 𝒪(polylog(T)/√(T)) convergence rates for (L_0,L_1)-smooth functions without assuming uniform bounds on the noise support. The key innovation behind our results is a carefully constructed stopping time τ which is simultaneously "large" on average, yet also allows us to treat the adaptive step sizes before τ as (roughly) independent of the gradients. For general (L_0,L_1)-smooth functions, our analysis requires the mild restriction that the multiplicative noise parameter σ_1 < 1. For a broad subclass of (L_0,L_1)-smooth functions, our convergence rate continues to hold when σ_1 ≥ 1. By contrast, we prove that many algorithms analyzed by prior works on (L_0,L_1)-smooth optimization diverge with constant probability even for smooth and strongly-convex functions when σ_1 > 1.
READ FULL TEXT
