128 citations · 144 across the 12 of their papers we have counts for
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The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence
Daogao Liu, Zhou Lu
Stochastic Gradient Descent (SGD) is among the simplest and most popular methods in optimization. The convergence rate for SGD has been extensively studied and tight analyses have…
The Power of Sampling: Dimension-free Risk Bounds in Private ERM
Yin Tat Lee, Daogao Liu, Zhou Lu
Differentially private empirical risk minimization (DP-ERM) is a fundamental problem in private optimization. While the theory of DP-ERM is well-studied, as large-scale models beco…
Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons
Bohang Zhang, Tianle Cai, Zhou Lu +2
It is well-known that standard neural networks, even with a high classification accuracy, are vulnerable to small -norm bounded adversarial perturbations. Although man…
A Note on the Representation Power of GHHs
Zhou Lu
In this note we prove a sharp lower bound on the necessary number of nestings of nested absolute-value functions of generalized hinging hyperplanes (GHH) to represent arbitrary CPW…
A Tight Lower Bound for Uniformly Stable Algorithms
Qinghua Liu, Zhou Lu
Leveraging algorithmic stability to derive sharp generalization bounds is a classic and powerful approach in learning theory. Since Vapnik and Chervonenkis [1974] first formalized…