Chris Junchi Li
Cited by
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Spider: Near-optimal non-convex optimization via stochastic path integrated differential estimator
C Fang, CJ Li, Z Lin, T Zhang
arXiv preprint arXiv:1807.01695, 2018
Near-optimal stochastic approximation for online principal component estimation
CJ Li, M Wang, H Liu, T Zhang
Mathematical Programming 167 (1), 75-97, 2018
On the diffusion approximation of nonconvex stochastic gradient descent
W Hu, CJ Li, L Li, JG Liu
arXiv preprint arXiv:1705.07562, 2017
Statistical sparse online regression: A diffusion approximation perspective
J Fan, W Gong, CJ Li, Q Sun
International Conference on Artificial Intelligence and Statistics, 1017-1026, 2018
On linear stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration
W Mou, CJ Li, MJ Wainwright, PL Bartlett, MI Jordan
Conference on Learning Theory, 2947-2997, 2020
Hessian-aware zeroth-order optimization for black-box adversarial attack
H Ye, Z Huang, C Fang, CJ Li, T Zhang
arXiv preprint arXiv:1812.11377, 2018
Efficient smooth non-convex stochastic compositional optimization via stochastic recursive gradient descent
W Hu, CJ Li, X Lian, J Liu, H Yuan
Advances in Neural Information Processing Systems 32, 2019
Diffusion Approximations for Online Principal Component Estimation and Global Convergence
CJ Li, M Wang, T Zhang
Advances in Neural Information Processing Systems, 645-655, 2017
Root-sgd: Sharp nonasymptotics and asymptotic efficiency in a single algorithm
CJ Li, W Mou, MJ Wainwright, MI Jordan
arXiv preprint arXiv:2008.12690, 2020
A convergence analysis of the perturbed compositional gradient flow: Averaging principle and normal deviations
W Hu, CJ Li
Discrete & Continuous Dynamical Systems - A 38 (10), 4951-4977, 2018
On the convergence of stochastic extragradient for bilinear games with restarted iteration averaging
CJ Li, Y Yu, N Loizou, G Gidel, Y Ma, NL Roux, MI Jordan
arXiv preprint arXiv:2107.00464, 2021
Stochastic Approximation for Online Tensorial Independent Component Analysis
CJ Li, M Jordan
Conference on Learning Theory, 3051-3106, 2021
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