Ph.D. in ICME, Stanford Univerisity Email: pengxu@stanford.edu |
I am currently an applied scientist in Amazon Web Services AI Lab. I received my Ph.D. in Institute for Computational and Mathematical Engineering(ICME) from Stanford Univeristy, where I was co-advised by Chris Ré and Michael Mahoney. My research interest lies in randomized linear algebra, optimization and machine learning. In particular I am interested in leveraging tools from randomized linear algebra to provide efficient and scalable solutions for large-scale optimization and learning problems. Before joining Stanford, I recieved my B.S. in Mathematics from Fudan University.
Trust Region Based Adversarial Attack on Neural Networks,
Zhewei Yao, Amir Gholami, Peng Xu, Kurt Keutzer, and Michael W. Mahoney,
arXiv preprint, 2018. [arXiv]
Newton-MR: Newton's Method Without Smoothness or Convexity,
Farbod Roosta-Khorasani, Yang Liu, Peng Xu, and Michael W. Mahoney,
arXiv preprint, 2018. [arXiv]
Inexact Non-Convex Newton-Type Methods,
Zhewei Yao, Peng Xu, Farbod Roosta-Khorasani, and Michael W. Mahoney,
arXiv preprint, 2018. [arXiv]
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization,
Shusen Wang, Farbod Roosta-Khorasani, Peng Xu, and Michael W. Mahoney,
Neural Information Processing Systems (NeurIPS), 2018. [arXiv][spark code]
Second-Order Optimization for Non-Convex Machine Learning: An Empirical Study,
Peng Xu, Farbod Roosta-Khorasani, and Michael W. Mahoney,
arXiv preprint, 2017. [arXiv][code]
Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information,
Peng Xu, Farbod Roosta-Khorasani, and Michael W. Mahoney,
arXiv preprint, 2017. [arXiv]
Accelerated Stochastic Power Iteration,
Peng Xu, Bryan He, Christopher De Sa, Ioannis Mitliagkas, and Christopher Ré.
arXiv preprint, 2017. [arXiv][code][blog]
International Conference on Artificial Intelligence and Statistics (AISTATS), 2018. [full paper]
Socratic Learning: Correcting Misspecified Generative Models using Discriminative Models,
Paroma Varma, Bryan He, Dan Iter, Peng Xu, Rose Yu, Christopher De Sa, and Christopher Ré.
arXiv preprint, 2017. [arXiv]
Sub-sampled Newton Methods with Non-uniform Sampling,
Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani, Christopher Ré, and Michael W. Mahoney
Neural Information Processing Systems (NeurIPS), 2016. [paper][arXiv][code]
Teaching assistant at Stanford University for:
Statistical Learning Theory (CS229T), Winter 2015
Stochastic Methods in Engineering (CME 308), Spring 2015
Linear Algebra with Application to Engineering Computations (CME 200), Autumn 2015
Teaching assistant at PCMI Graduate Summer School 2016: The Mathematics of Data