Cho-Jui Hsieh

I am an assistant professor of Computer Science and Statistics at UC Davis. I was a Ph.D. student at UT Austin working with Prof. Inderjit Dhillon. I also work closely with Prof. Pradeep Ravikumar and Dr. Peder Olsen. I received my master degree in National Taiwan University under supervision of Prof. Chih-Jen Lin. My research interests are machine learning and optimization for big data.

Email: chohsieh AT ucdavis.edu Office: MSB 4232 Curriculum Vitae Google Scholar Profile


Teaching

Machine Learning

ECS 171, Winter 2018.

Course Page
Optimization

STA 250, Fall 2017.

Course Page
Advanced Statistical Computing

STA 141C, Spring 2017.

Course Page
Scalable Machine Learning

ECS 289G, Fall 2016.

Course Page

Optimization

STA 250, Winter 2016.

Course Page
Scalable Machine Learning

ECS 289G, Fall 2015.

Course Page

News

  • I'm looking for motivated students to join my group.

Software

Asynchronous Kernel SVM

A multicore parallel kernel SVM solver.

Software Page
Distributed Kernel SVM

A distributed kernel SVM solver.

Software Page

LIBLINEAR

A library for large-scale linear classification.

Software Page
DCSVM

A large-scale kernel SVM solver.

Software Page
QUIC

A package for sparse inverse covariance estimation.

Software Page

LIBPMF

A parallel matrix factorization library.

Software Page
NMF CD

Fast coordinate descent methods for non-negative matrix factorization.

Software Page
AMD

An Automatic matrix differentiation library.

Software Page

Preprint

ImageNet Training in Minutes , Yang You, Zhao Zhang, James Demmel, Kurt Keutzer, Cho-Jui Hsieh. 2017.

An inexact subsampled proximal Newton-type method for large-scale machine learning , Xuanqing Liu, Cho-Jui Hsieh, Jason D. Lee, Yuekai Sun. 2017.

GPU-acceleration for Large-scale Tree Boosting , Huan Zhang, Si Si, Cho-Jui Hsieh. 2017.



Publications

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples , Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh In AAAI Conference on Artificial Intelligence (AAAI), 2018.


ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models , Pin-Yu Chen*, Huan Zhang*, Yash Sharma, Jinfeng Yi, Cho-Jui Hsieh (* Equal contribution). ACM Conference on Computer and Communications Security (CCS) Workshop on Artificial Intelligence and Security (AISec), 2017.


Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent , Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, Ji Liu. To appear in Advances in Neural Information Processing Systems (NIPS), 2017


Scalable Demand-Aware Recommendation , Jinfeng Yi, Cho-Jui Hsieh, Kush Varshney, Lijun Zhang, Yao Li. To appear in Advances in Neural Information Processing Systems (NIPS), 2017


A Greedy Approach for Budgeted Maximum Inner Product Search , Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit S. Dhillon. To appear in Advances in Neural Information Processing Systems (NIPS), 2017


A Hyperplane-based Algorithm for Semi-supervised Dimension Reduction , Huang Fang, Minhao Cheng, Cho-Jui Hsieh. To appear in IEEE International Conference on Data Mining (ICDM), 2017.


Large-scale Collaborative Ranking in Near-Linear Time , Liwei Wu, Cho-Jui Hsieh, James Sharpnack. To appear in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017.


Communication-Efficient Distributed Block Minimization for Nonlinear Kernel Machines , Cho-Jui Hsieh, Si Si, Inderjit Dhillon. To appear in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017.


Gradient Boosted Decision Trees for High Dimensional Sparse Output , Si Si, Huan Zhang, Sathiya Keerthi, Dhruv Mahajan, Inderjit Dhillon, Cho-Jui Hsieh. To appear in International Conference on Machine Learning (ICML) 34, 2017.


Improved Bounded Matrix Completion for Large-scale Recommender Systems , Huang Fang, Zhang Zhen, Yiqun Shao, Cho-Jui Hsieh. To appear in International Joint Conference on Artificial Intelligence (IJCAI), 2017.


Rank Aggregation and Prediction with Item Features , Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit Dhillon. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.


Memory Efficient Kernel Approximation , Si Si, Cho-Jui Hsieh, Inderjit Dhillon. Journal of Machine Learning Research (JMLR), 2017.


Machine Learning Meliorates Computing and Robustness in Discrete Combinatorial Optimization Problems , Fushing Hsieh, Kevin Fuji, Cho-Jui Hsieh. Frontiers in Applied Mathematics and Statistics, 2016.


Fixing the Convergence Problems in Parallel Asynchronous Dual Coordinate Descent , Huan Zhang, Cho-Jui Hsieh. In IEEE International Conference on Data Mining (ICDM), 2016.


HogWild++: A New Mechanism for Decentralized Asynchronous Stochastic Gradient Descent , Huan Zhang, Cho-Jui Hsieh, Venkatesh Akella. In IEEE International Conference on Data Mining (ICDM), 2016.


Asynchronous Parallel Greedy Coordinate Descent , Yang You, Xiangru Lian, Ji Liu, Hsiang-Fu Yu, Inderjit Dhillon, James Demmel, Cho-Jui Hsieh. In Advances in Neural Information Processing Systems (NIPS), 2016.


A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order , Xiangru Lian, Huan Zhang, Cho-Jui Hsieh, Yijun Huang, Ji Liu, In Advances in Neural Information Processing Systems (NIPS), 2016.


Goal-Directed Inductive Matrix Completion, Si Si, Kai-Yang Chiang, Cho-Jui Hsieh, Nikhil Rao, Inderjit S. Dhillon. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016.


Nomadic Computing for Big Data Analytics, Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S.V.N. Vishwanathan, Inderjit S. Dhillon. To appear at IEEE Computer, 2016.


Computationally Efficient Nystrom Approximation using Fast Transforms, Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon. In International Conference on Machine Learning (ICML) 33, 2016.


Robust Principal Component Analysis with Side Information, Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon. In International Conference on Machine Learning (ICML) 33, 2016.


Matrix Completion with Noisy Side Information, Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon. In Advances in Neural Information Processing Systems (NIPS), 2015.


Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent, Ian E.H. Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon. In Advances in Neural Information Processing Systems (NIPS), 2015.


PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent, Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon. In International Conference on Machine Learning (ICML) 32, 2015.


PU Learning for Matrix Completion, Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit S. Dhillon. In International Conference on Machine Learning (ICML) 32, 2015.


A Scalable Asynchronous Distributed Algorithm for Topic Modeling, Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S.V.N Vishwanathan, Inderjit S. Dhillon. In ACM WWW International conference on World Wide Web (WWW), 2015.


Fast Prediction for Large-Scale Kernel Machines, Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon. In Advances in Neural Information Processing Systems (NIPS), 2014.


QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models, Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar, Stephen Becker, Peder A. Olsen. In Advances in Neural Information Processing Systems (NIPS), 2014.


Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings, Ian E.H. Yen, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon. In Advances in Neural Information Processing Systems (NIPS), 2014.


NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion, Hyokun Yun, Hsiang-Fu Yu, Cho-Jui Hsieh, S.V.N Vishwanathan, Inderjit S. Dhillon. In Prceedings of Very Large Databases (VLDB), 2014.


QUIC: Quadratic Approximation for Sparse Inverse Covariance Estimation, Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar. Journal of Machine Learning Research (JMLR), 2014.


Prediction and Clustering in Signed Networks: A Local to Global Perspective, Kai-Yang Chiang, Cho-Jui Hsieh, Nagarajan Natarajan, Ambuj Tewari, and Inderjit S. Dhillon. Journal of Machine Learning Research (JMLR), 2014.


A Divide-and-Conquer Solver for Kernel Support Vector Machines, Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon. In International Conference on Machine Learning (ICML), 2014.


Memory Efficient Kernel Approximation, Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon. In International Conference on Machine Learning (ICML), 2014. Recommended for JMLR Fast Track (18 out of 1260+).


Nuclear Norm Minimization via Active Subspace Selection, Cho-Jui Hsieh, Peder A. Olsen. In International Conference on Machine Learning (ICML), 2014.


BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables, Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar, Russell A. Poldrack. In Advances in Neural Information Processing Systems (NIPS), 2013. Oral presentation, 1.4% acceptance rate.


Large Scale Distributed Sparse Precision Estimation, Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon. In Advances in Neural Information Processing Systems (NIPS), 2013.


Parallel matrix factorization for recommender systems, Hsiang-Fu Yu, Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon, Knowledge and Information Systems (KAIS), Sept, 2013.


Organizational Overlap on Social Networks and its Applications, Cho-Jui Hsieh, Mitul Tiwari, Deepack Agarwal, Xinyi Huang, Sam Shah. In ACM WWW International conference on World Wide Web (WWW), 2013.


Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems, Hsiang-Fu Yu, Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon. In IEEE International Conference on Data Mining(ICDM), 2012. ICDM best paper award


A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation, Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar, Arindam Banerjee. In Advances in Neural Information Processing Systems (NIPS) 25, 2012.


Low-Rank Modeling of Signed Networks, Cho-Jui Hsieh, Kai-Yang Chiang, Inderjit S. Dhillon. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2012.


Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation, Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar. In Advances in Neural Information Processing Systems (NIPS) 24, 2011.


Fast Coordinate Descent Methods with Variable Selection for Non-negative Matrix Factorization, Cho-Jui Hsieh, Inderjit S. Dhillon, In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2011).


Large linear classification when data cannot fit in memory, Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin. In ACM SIGKDD International Conference on Kowledge Discovery and Data Mining (KDD 2010). KDD best research paper award.

A short version appears in IJCAI 2011. A journal version appears in ACM Transactions on Knowledge Discovery from Data (TKDD), Volumn 5 Issue 4 (2012).


A Comparison of Optimization methods and software for large-scale L1-regularized linear classification, Guo-Xun Yuan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin. Journal of Machine Learning Research (JMLR), 11(2010), 3183-3234.


Training and Testing Low-degree Polynomial Data Mappings via Linear SVM, Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, Chih-Jen Lin. Journal of Machine Learning Research (JMLR), 11(2010), 1471--1490.


An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes, H.-Y. Lo, K.-W. Chang, S.-T. Chen, T.-H. Chiang, C.-S. Ferng, C.-J. Hsieh, Y.-K. Ko, T.-T. Kuo, H.-C. Lai, K.-Y. Lin, C.-H. Wang, H.-F. Yu, C.-J. Lin, H.-T. Lin and S.-d. Lin, JMLR Workshop and Conference Proceedings, V.7, 57-64, 2009 (Third Place of the KDDCup'09 Slow Track).


Iterative scaling and coordinate descent methods for maximum entropy models, Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, and Chih-Jen Lin. Journal of Machine Learning Research (JMLR), 11(2010), 581-614.


LIBLINEAR: A library for large linear classification, Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin. Journal of Machine Learning Research (JMLR), 9(2008), 1871-1874.


A sequential dual method for large scale multi-class linear SVMs, S. Sathiya Keerthi, S. Sundararajan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2008.


A dual coordinate descent method for large-scale linear SVM, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, S. Sathiya Keerthi, and S. Sundararajan. In International Conference on Machine Learning (ICML) 25, 2008.


Coordinate descent method for large-scale L2-loss linear SVM, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin. Journal of Machine Learning Research (JMLR), 9(2008), 1369-1398.