2 citations · 2 across the 7 of their papers we have counts for
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Stochastic Saddle Avoidance Beyond Unit Excitation and Smoothness: A Pathwise Lyapunov-Perron Framework
Junwen Qiu, Bohao Ma, Andre Milzarek +1
Unit excitation (UE) is a common assumption in stochastic saddle avoidance: the stochastic error must have a uniformly positive component along every direction, in expectation. Thi…
Random Reshuffling with Momentum: Complexity Bounds and Last-iterate Convergence
Junwen Qiu, Bohao Ma, Andre Milzarek
Random reshuffling with momentum (RRM) corresponds to the SGD optimizer with the 'momentum' option enabled, as found in many machine learning libraries such as PyTorch and TensorFl…
Shuffling the Stochastic Mirror Descent via Dual Lipschitz Continuity and Kernel Conditioning
Junwen Qiu, Leilei Mei, Junyu Zhang
The global Lipschitz smoothness condition underlies most convergence and complexity analyses via two key consequences: the descent lemma and the gradient Lipschitz continuity. How…
A New Kernel Regularity Condition for Distributed Mirror Descent: Broader Coverage and Simpler Analysis
Junwen Qiu, Ziyang Zeng, Leilei Mei +1
Existing convergence of distributed optimization methods in non-Euclidean geometries typically rely on kernel assumptions: (i) global Lipschitz smoothness and (ii) bi-convexity of…
A Normal Map-Based Proximal Stochastic Gradient Method: Convergence and Identification Properties
Junwen Qiu, Li Jiang, Andre Milzarek
The proximal stochastic gradient method (PSGD) is one of the state-of-the-art approaches for stochastic composite-type problems. In contrast to its deterministic counterpart, PSGD…
A Generalized Version of Chung's Lemma and its Applications
Li Jiang, Xiao Li, Andre Milzarek +1
Chung's Lemma is a classical tool for establishing asymptotic convergence rates of (stochastic) optimization methods under strong convexity-type assumptions and appropriate polynom…