Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise and H" o lder Smoothness

Classical convergence guarantees for stochastic gradient methods typically assume Lipschitz-smooth objectives and finite-variance gradient noise, both frequently violated in practice. In contrast, we study nonconvex stochastic optimization under the joint relaxation of these assumptions: objectives with (L,s) -Hölder continuous gradients, s in(0,1], and gradient noise satisfying only a bounded α-th moment condition for α in(1,2]. We establish three convergence results. Firstly, that standard SGD converges at rate O(T-s/(1+s)) whenever α 1+s, extending the classical nonconvex SGD rate to heavy-tailed noise and Hölder smoothness simultaneously. Secondly, we analyze δ-regularized gradient clipping (δ-GClip), a provable trainer of wide and deep nets, and establish a stationarity rate of O(T-2s(α-1)/[(1+s)(2α-1)]) under the same condition. Thirdly, we analyze standard gradient clipping (G-Clip) and show that it recovers the above rate for α 1+s while in the very heavy-tailed regime α<1+s, it has a convergence rate O(T-2s(α-1)/[(α-1)+s(2α-1)]) --- the first convergence guarantee in this regime for any stochastic gradient based method.