Weijie Su

Jinho Bok, Weijie Su, Jason Altschuler (2024), Shifted Interpolation for Differential Privacy, ICML.

Richard A. Berk, Andreas Buja, Lawrence D. Brown, Edward I. George, Arun Kumar Kuchibhotla, Weijie Su, Linda Zhao (2021), Assumption Lean Regression, American Statistician, 75 (1), pp. 76-84.

Matteo Sordello, Hangfeng He, Weijie Su (Working), Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic.

Abstract: This paper proposes SplitSGD, a new dynamic learning rate schedule for stochastic optimiza- tion. This method decreases the learning rate for better adaptation to the local geometry of the objective function whenever a stationary phase is detected, that is, the iterates are likely to bounce at around a vicinity of a local minimum. The detection is performed by splitting the single thread into two and using the inner product of the gradients from the two threads as a measure of stationarity. Owing to this simple yet provably valid stationarity detection, SplitSGD is easy-to-implement and essentially does not incur additional computational cost than standard SGD. Through a series of extensive experiments, we show that this method is appropriate for both convex problems and training (non-convex) neural networks, with performance compared favorably to other stochastic optimization methods. Importantly, this method is observed to be very robust with a set of default parameters for a wide range of problems and, moreover, yields better generalization performance than other adaptive gradient methods such as Adam.

Hangfeng He and Weijie Su (2020), The Local Elasticity of Neural Networks, International Conference on Learning Representations (ICLR), (to appear) ().

Zhiqi Bu, Jinshuo Dong, Qi Long, Weijie Su (Working), Deep Learning with Gaussian Differential Privacy.

Bin Shi, Simon S. Du, Weijie Su, Michael I. Jordan (2019), Acceleration via Symplectic Discretization of High-Resolution Differential Equations, Advances in Neural Information Processing Systems 32.

Zhiqi Bu, Jason Klusowski, Cynthia Rush, Weijie Su (2019), Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing, Advances in Neural Information Processing Systems 32.

Jinshuo Dong, Aaron Roth, Weijie Su (Working), Gaussian Differential Privacy.

Qingyuan Zhao, Dylan Small, Weijie Su (2019), Multiple Testing When Many p-Values are Uniformly Conservative, with Application to Testing Qualitative Interaction in Educational Interventions, Journal of the American Statistical Association, 114 (527), pp. 1291-1304.

Damian Brzyski, Alexej Gossmann, Weijie Su, Malgorzata Bogdan (2019), Group SLOPE – Adaptive Selection of Groups of Predictors, Journal of the American Statistical Association, 114 (525), pp. 419-433.