Peer-reviewed Publications

  1. Counterfactual Language Model Adaptation for Suggesting Phrases

    Kenneth Arnold, Kai-Wei Chang, Adam T. Kalai
    IJCNLP 2017 (pdf, details)
    A short version appears in AAAI17 workshop on Human-Aware AI

  2. Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

    Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, Kai-Wei Chang
    EMNLP 2017 (Best Long Paper Award, pdf, details)

  3. Structured Prediction with Test-time Budget Constraints

    Tolga Bolukbasi, Kai-Wei Chang, Joseph Wang, Venkatesh Saligrama
    AAAI 2017 (pdf, details)

  4. Beyond Bilingual: Multi-senseWord Embeddings using Multilingual Context

    Shyam Upadhyay, Kai-Wei Chang, Matt Taddy, Adam Kalai, James Zou
    ACL RepL4NLP Workshop 2017 (Best Paper Award, pdf, details)

  5. Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems

    Shyam Upadhyay, Ming-Wei Chang, Kai-Wei Chang, Wen-tau Yih
    EMNLP 2016 (pdf, details)

  6. EMNLP 16 Workshop on Structured Prediction for NLP

    Kai-Wei Chang, Ming-Wei Chang, Vivek Srikumar, Alexander M. Rush
    EMNLP 2016 (pdf, details)

  7. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

    Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, Adam Kalai
    NIPS 2016 (reported by NPR and MIT Tech Review, pdf, details)
    A short version appears in ICML16 Workshop on Data4Good

  8. A Credit Assignment Compiler for Joint Prediction

    Kai-Wei Chang, He He, Hal Daume III, John Langford, Stephane Ross
    NIPS 2016 (pdf, details)
    An earlier version appears in ML system workshop at ICML 16

  9. Distributed Training of Structured SVM

    Ching-pei Lee, Kai-Wei Chang, Shyam Upadhyay, Dan Roth
    OPT workshop at NIPS 2015 (pdf, details)

  10. Learning to Search Better Than Your Teacher

    Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daume; III, John Langford
    ICML 2015 (pdf, details)

  11. A Joint Framework for Coreference Resolution and Mention Head Detection

    Haoruo Peng, Kai-Wei Chang, Dan Roth
    CoNLL 2015 (pdf, details)

  12. Structural Learning with Amortized Inference

    Kai-Wei Chang, Shyam Upadhyay, Gourab Kundu, Dan Roth
    AAAI 2015 (pdf, details)

  13. Selective Algorithms for Large-Scale Classification and Structured Learning

    Kai-Wei Chang
    UIUC Phd Thesis 2015 (pdf, details)

  14. A Discriminative Latent Variable Model for Online Clustering

    Rajhans Samdani, Kai-Wei Chang, Dan Roth
    ICML 2014 (pdf, details)

  15. Typed Tensor Decomposition of Knowledge Bases for Relation Extraction

    Kai-Wei Chang, Wen-tau Yih, Bishan Yang, Chris Meek
    EMNLP 2014 (pdf, details)

  16. The Illinois-Columbia System in the CoNLL-2014 Shared Task

    Alla Rozovskaya, Kai-Wei Chang, Mark Sammons, Dan Roth, Nizar Habash
    CoNLL Shared Task 2014 (pdf, details)

  17. A Constrained Latent Variable Model for Coreference Resolution

    Kai-Wei Chang, Rajhans Samdani, Dan Roth
    EMNLP 2013 (pdf, details)

  18. Multi-Relational Latent Semantic Analysis

    Kai-Wei Chang, Wen-tau Yih, Chris Meek
    EMNLP 2013 (pdf, details)

  19. Tractable Semi-Supervised Learning of Complex Structured Prediction Models

    Kai-wei Chang, S. Sundararajan, S. Sathiya Keerthi
    ECML 2013 (pdf, details)

  20. Multi-core Structural SVM Training

    Kai-Wei Chang, Vivek Srikumar, Dan Roth
    ECML 2013 (details)

  21. The University of Illinois System in the CoNLL-2013 Shared Task

    Alla Rozovskaya, Kai-Wei Chang, Mark Sammons, Dan Roth
    CoNLL Shared Task 2013 (pdf, details)

  22. Efficient Pattern-Based Time Series Classification on GPU

    Kai-Wei Chang, Baplab Deka, W.-M. W. Hwu, Dan Roth
    ICDM 2012 (pdf, details)

  23. Illinois-Coref: The UI System in the CoNLL-2012 Shared Task

    Kai-Wei Chang, Rajhans Samdani, Alla Rozovskaya, Mark Sammons, Dan Roth
    CoNLL Shared Task 2012 (pdf, details)

  24. Large Linear Classification When Data Cannot Fit In Memory

    Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin
    TKDD 2012 (Best Paper Award, KDD 10, pdf, details)
    (An earlier version appears in KDD 2010, an short version is published in IJCAI 2011 best paper track)

  25. Selective Block Minimization for Faster Convergence of Limited Memory Large-scale Linear Models

    Kai-Wei Chang, Dan Roth
    KDD 2011 (pdf, details)

  26. Inference Protocols for Coreference Resolution

    Kai-Wei Chang, Rajhans Samdani, Alla Rozovskaya, Nick Rizzolo, Mark Sammons, Dan Roth
    CoNLL Shared Task 2011 (pdf, details)

  27. Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models

    Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin
    JMLR 2010 (pdf, details)

  28. 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
    JMLR 2010 (pdf, details)

  29. 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
    JMLR 2010 (pdf, details)

  30. An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Na─▒ve Bayes

    Hung-Yi Lo, Kai-Wei Chang, Shang-Tse Chen, Tsung-Hsien Chiang, ChunSung Ferng, Cho-Jui Hsieh, Yi-Kuang Ko, Tsung-Ting Kuo, Hung-Che Lai, Ken-Yi Lin, Chia-Hsuan Wang, Hsiang-Fu Yu, Chih-Jen Lin, Hsuan-Tien Lin, Shou-de Lin
    KDD Cup 2009 (pdf, details)

  31. 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
    KDD 2008 (pdf, details)

  32. A Dual Coordinate Descent Method for Large-Scale Linear SVM

    Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, Sathia S. Keerthi, S. Sundararajan
    ICML 2008 (pdf, details)

  33. LIBLINEAR: A Library for Large Linear Classification

    Rong En Fan, Kai-Wei Chang, Cho-Jui Hsieh, X.-R. Wang, Chih-Jen Lin
    JMLR 2008 (pdf, details)

  34. Coordinate Descent Method for Large-scale L2-loss Linear SVM

    Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin
    JMLR 2008 (pdf, details)

Pre-Prints

  1. Learning to Search for Dependencies

    Kai-Wei Chang, He He, Hal Daume; III, John Lanford
    Arxiv 2015 (pdf, details)

  2. IllinoisSL: A JAVA Library for Structured Prediction

    Kai-Wei Chang, Shyam Upadhyay, Ming-Wei Chang, Vivek Srikumar, Dan Roth
    Arxiv 2015 (pdf, details)

  3. Distributed Block-diagonal Approximation Methods for Regularized Empirical Risk Minimization

    Ching-pei Lee, Kai-Wei Chang
    Preprint -- optimization online 2017 (pdf, details)