Ph.D. in ICME, Stanford Univerisity Advisors: Chris R‌é and Michael Mahoney Email: pengx@amazon.com

About Me

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.

Research

[google scholar]

• Embedding-based Zero-shot Retrieval through Query Generation,
  Davis Liang*, Peng Xu*, Siamak Shakeri, Cicero Nogueira dos Santos, Ramesh Nallapati, Zhiheng Huang, Bing Xiang,
  arXiv preprint, 2020. [arXiv][code]
  

• Improve Transformer Models with Better Relative Position Embeddings,
  Zhiheng Huang, Davis Liang, Peng Xu, Bing Xiang,
  Findings of EMNLP, 2020. [arXiv]

• TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding,
  Zhiheng Huang, Peng Xu, Davis Liang, Ajay Mishra, Bing Xiang,
  arXiv preprint, 2020. [arXiv]
  

• Domain Adaptation with BERT-based Domain Classification and Data Selection,
  Xiaofei Ma, Peng Xu, Zhiguo Wang, Ramesh Nallapati, and Bing Xiang,
  Procs. of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo), 2019. [paper]

• Passage Ranking with Weak Supervision,
  Peng Xu, Xiaofei Ma, Ramesh Nallapati, and Bing Xiang,
  arXiv preprint, 2019, [arXiv]
  ICLR LLD workshop, 2019. [paper]

• Trust Region Based Adversarial Attack on Neural Networks,
  Zhewei Yao, Amir Gholami, Peng Xu, Kurt Keutzer, and Michael W. Mahoney,
  arXiv preprint, 2018. [arXiv]
  Computer Vision and Pattern Recognition (CVPR), 2019.

• Newton-MR: Newton's Method Without Smoothness or Convexity,
  Fred Roosta, Yang Liu, Peng Xu, and Michael W. Mahoney,
  arXiv preprint, 2018. [arXiv]

• Inexact Non-Convex Newton-Type Methods,
  Zhewei Yao, Peng Xu, Fred Roosta, and Michael W. Mahoney,
  arXiv preprint, 2018. [arXiv]

• GIANT: Globally Improved Approximate Newton Method for Distributed Optimization,
  Shusen Wang, Fred Roosta, 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, Fred Roosta, and Michael W. Mahoney,
  arXiv preprint, 2017. [arXiv][code]
  SIAM International Conference on Data Mining (ICDM), 2020. [paper]

• Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information,
  Peng Xu, Fred Roosta, and Michael W. Mahoney,
  arXiv preprint, 2017. [arXiv]
  Mathematical Programming, 2019. [paper]

• 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, Fred Roosta, Christopher Ré, and Michael W. Mahoney
  Neural Information Processing Systems (NeurIPS), 2016. [paper][arXiv][code]

Teaching

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

• RandNLA: Randomization in Numerical Linear Algebra, Aug. 2016