Seal     

Xi Wu

I joined Google in 2016. I received my Ph.D. in Computer Science from University of Wisconsin-Madison, advised by Jeffrey F. Naughton and Somesh Jha. I was awarded a Google PhD Fellowship in 2016 (Privacy and Security).

Publications

Concise Explanations for Neural Networks using Adversarial Training
Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Somesh Jha, Xi Wu
ICML 2020, arXiv 2018
Robust Attribution Regularization
Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha.
NeurIPS 2019, arXiv 2019
(slides, poster, Alta Cognita)
Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks
Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha.
EuroS&P 2019, arXiv 2018
DIFF: A Relational Interface to Large-Scale Data Explanation
Firas Abuzaid, Peter Kraft, Sahhana Suri, Edward Gan, Eric Xu, Atul Shenoy, Avsin Anathanarayan, John Sheu, Erik Meijer, Xi Wu, Jeffrey F. Naughton, Peter Bailis, Matei Zaharia.
VLDB 2019 (Invited to "Best of VLDB 2019" Special Issue)
Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent
Fengan Li, Lingjiao Chen, Yijing Zeng, Arun Kumar, Xi Wu, Jeffrey F. Naughton, Jignesh M. Patel.
SIGMOD 2019, arXiv 2017
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
Xi Wu, Uyeong Jang, Jiefeng Chen, Lingjiao Chen, Somesh Jha.
ICML 2018, arXiv 2017
(slides)
Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics
Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton.
SIGMOD 2017, arXiv 2016
(slides)
Objective Metrics and Gradient Descent Algorithms for Adversarial Examples in Machine Learning
Uyeong Jang, Xi Wu, Somesh Jha.
ACSAC 2017
A Study of Stability in Data Privacy
Advisors: Jeffrey F. Naughton, Somesh Jha.
Ph.D. Thesis, UW-Madison, August 2016
A Methodology for Modeling Model-Inversion Attacks
Xi Wu, Matthew Fredrikson, Somesh Jha, Jeffrey F. Naughton.
CSF 2016
(slides)
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, Ananthram Swami.
S&P (Oakland) 2016, arXiv 2015
A Completeness Theory for Polynomial (Turing) Kernelization
with Danny Hermelin, Stephan Kratsch, Karolina Soltys, Magnus Wahlstrom.
Algorithmica 2015, IPEC 2013
Uncertainty Aware Query Execution Time Prediction
Wentao Wu, Xi Wu, Hakan Hacigümüs, Jeffrey F. Naughton.
VLDB 2014
Weak Compositions and Their Applications to Polynomial Lower Bounds for Kernelization
with Danny Hermelin.
SODA 2012, ECCC 2011
(slides)
COREMU: A Scalable and Portable Parallel Full-system Emulator
Zhaoguo Wang, Ran Liu, Yufei Chen, Xi Wu, Haibo Chen, Binyu Zang.
PPoPP 2011
Extended Islands of Tractability for Parsimony Haplotyping
with Rudolf Fleischer, Jiong Guo, Rolf Niedermeier, Johannes Uhlmann, Yihui Wang, Mathias Weller.
CPM 2010
Experimental Study of FPT Algorithms for the Directed Feedback Vertex Set Problem
with Rudolf Fleischer, Liwei Yuan.
ESA 2009
(slides, code)
Control Flow Obfuscation with Information Flow Tracking
Haibo Chen, Liwei Yuan, Xi Wu, Bo Huang, Pen-chung Yew, Binyu Zang.
MICRO 2009
From Speculation to Security: Practical and Efficient Information Flow Tracking using Speculative Hardware
Haibo Chen, Xi Wu, Liwei Yuan, Binyu Zang, Pen-chung Yew, Frederic T. Chong.
ISCA 2008

Manuscripts

Robust Out-of-distribution Detection via Informative Outlier Mining
Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha
ICML UDL 2020, arXiv 2020
Robust Out-of-distribution Detection for Neural Networks
Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha
arXiv 2020
Representation Bayesian Risk Decompositions and Multi-Source Domain Adaptation
Xi Wu, Yang Guo, Jiefeng Chen, Yingyu Liang, Somesh Jha, Prasad Chalasani
arXiv 2020
Rearchitecting Classification Frameworks For Increased Robustness
Varun Chandrasekaran, Brian Tang, Nicolas Papernot, Kassem Fawaz, Somesh Jha, Xi Wu
arXiv 2019