PUBLICATION

Year of Publication :
[2022] [2021] [2020] [2019] [2018] [2017] [2016] [2015] [2014] [2013] [2012] [2011] [2010] [2009] [2008] [2007] [2006] [2005] [2004] [2003] [2002] [2001] [Pre-2000]

Type of Publication :
[Show all] [Theses] [Book & book chapters] [Journal] [Conference] [Tech reports] [Workshop] [arXiv manuscripts]

Broad Content :
[Show all] [Core Machine Learning] [Systems and ML] [Healthcare and Medicine] [Other Applications]

Research Area :
[AutoML] [SysML] [MetaML] [Trustworthy ML] [Algorithms and Methodologies]
[Healthcare] [Genomics] [Natural Language Processing] [Computer Vision]


  • Z. Hu, Z. Yang, X. Liang, R. Salakhutdinov and E. P. Xing, Controllable Text Generation, The 34th International Conference on Machine Learning. (ICML 2017).


    ALL Publications (in Chronical Order):


    2022

  • Z. Shen, Z. Liu and E. P. Xing, Sliced Recursive Transformer , Proceeding of the 18th European Conference of Computer Vision, 2022. (ECCV 22).

  • M. Zhou, Z. Li, B. Tan, G. Zeng, W. Yang, X. He, Z. Ju, S. Chakravorty, S. Chen, X. Yang, Y. Zhang, Q. Wu, Z. Yu, K. Xu, E. P. Xing, and P. Xie, On the Generation of Medical Dialogs for COVID-19, Proceedings of The 59th Annual Meeting of the Association for Computational Linguistics, 2021. (ACL '21).
  • B. Tan, Z. Yang, M. AI-Shedivat, E. P. Xing, Z. Hu, Progressive Generation of Long Text, The 2021 Conference of the North American Chapter of the Association for Computational Linguistics. (NAACL '21).

  • Z. Liu, G. Ding, A. Bukkittu, M. Gupta, P. Gao, A. Ahmed, S. Zhang, X. Gao, S. Singhavi, L. Li, W. Wei, Z. Hu, H. Shi, X. Liang, T. Mitamura, E. Xing and Z. Hu. A Data-Centric Framework for Composable NLP Workflows, Proceeding of the 2020 Conference on Empirical Methods on Natural Language Processing. (EMNLP 2020 Demo).
  • X. Zheng, C. Dan, B. Aragam, P. Ravikumar, and E. P. Xing Learning Sparse Nonparametric DAGs, Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020. (AISTATS 20)

  • Y. Li, X. Liang, Z. Hu, Y. Chen, and E. P. Xing, Graph Transformer, Proceedings of Seventh International Conference on Learning Representations (ICLR 2019).

  • J. Oliva, A. Dubey, M. Zaheer, B. Poczos, R. Salakhutdinov, E. P. Xing and J. Schneider Transformation Autoregressive Networks, Proceedings of the 35th International Conference on Machine Learning (ICML '18)
  • L. Lee, E. Parisotto, D. S. Chaplot, E. P. Xing and R. Salakhutdinov Gated Path Planning Networks, Proceedings of the 35th International Conference on Machine Learning (ICML '18)
  • Z. Hu, Z. Yang, X. Liang, R. Salakhutdinov, and E. P. Xing, On Unifying Deep Generative Models, Proceedings of 6th International Conference on Learning Representations (ICLR'18)

    2017

  • S. Lee, N. Gornitz, E. P. Xing, D. Heckerman, C. Lippert Ensembles of Lasso Screening Rules, IEEE Transaction on Pattern Analysis and Machine Intelligence, 2017 (10.1109/TPAMI.2017.2765321)
  • H. Zhang, Z. Deng, X. Liang, L. Yang, S. Xu, J. Zhu, and E. P. Xing, Structured Generative Adversarial Networks, Proceedings of Advances in Neural Information Processing Systems 31 (NIPS '17). (Recipient of the Nvidia Pioneering Research Award)
  • Z. Hu, Z. Yang, X. Liang, R. Salakhutdinov and E. P. Xing, Controllable Text Generation, The 34th International Conference on Machine Learning. (ICML 2017).
  • X. Liang, L. Lin, X. Shen, J. Feng, S. Yan and E. P. Xing, Interpretable Structure-Evolving LSTM, Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017).

  • A. Dubey, J. Oliva, A. Wilson, E. P. Xing, B. Poczos, and J. Schneider, Bayesian Nonparametric Kernel-Learning, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics. (AISTATS 2016).

  • A. Wilson, C. Lucas, C. Dann and E. P. Xing, The Human Kernel, Advances in Neural Information Processing Systems 29 (eds. Daniel Lee and Masashi Sugiyama), MIT Press, 2015. (NIPS 2015).
  • Z. Hu, P. Huang, Y. Deng, Y. Gao and E. P. Xing, Entity Hierarchy Embedding, 53rd Annual Meeting of the Association for Computational Linguistics. (ACL 2015).
  • J. Oliva, W. Neiswanger, B. Poczos, E. P. Xing and J. Schneider, Fast Function to Function Regression, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics. (AISTATS 2015).

  • J. B. Oliva, W. Neiswanger, B. Poczos, J. Schneider and E. P. Xing, Fast Distribution To Real Regression, Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS 2014).



  • J. Zhu and E. P. Xing, Sparse Topical Coding, Proceedings of the 27th International Conference on Conference on Uncertainty in Artificial Intelli- gence (UAI 2011).
  • A. Ahmed, Q. Ho, J. Eisenstein, E. P. Xing, A. Smola and C. H. Teo, Unified Analysis of Streaming News, Proceedings of the International World Wide Web Conference (WWW 2011).



  • A. F.T. Martins, D. Das, N. A. Smith, and E. P. Xing, Stacking Dependency Parser, Proceedings of Conference on Empirical Methods in Natural Language Processing, (EMNLP 2008).
  • A. Martins, M. Figueiredo, P. Aguiar, N. A. Smith and E. P. Xing, Nonextensive Entropic Kernels, Proceedings of the 25th International Conference on Machine Learning (ICML 2008). (A longer version is available soon in CMU-MLD Technical Report 08-106 with the same title.)
  • W. Wu and E. P. Xing, A Survey of cDNA Microarray Normalization and a Comparison by k-NN Classification, in Methods in Microarray Normalization (Ed. S. Phillip), CRC Press. p81-120, 2008.


    2006

  • F. Guo, W. Fu, Y. Shi and E. P. Xing, Reverse engineering temporally rewiring gene networks, The NIPS workshop on New Problems and Methods in Computational Biology (NIPS2006).
  • E.M. Airoldi, D.M. Blei, S.E. Fienberg, E.P. Xing, Latent mixed-membership allocation models of relational and multivariate attribute data, Valencia & ISBA Joint World Meeting on Bayesian Statistics (2006).


  • E.P. Xing, R. Sharan and M.I Jordan, Bayesian Haplotype Inference via the Dirichlet Process. Proceedings of the 21st International Conference on Machine Learning (ICML2004),  (eds. Greiner and Schuurmans), ACM Press, 879-886, [ps]. An earlier version of this paper also appeared as a book chapter in Lecture Notes in Bioinformatics, Special issue for 2nd RECOMB Satellite Workshop on Computational Methods for SNPs and Haplotypes, 2004. (ps).




    pre2000

  • E.P. Xing, C. Kulikowski, I. Muchnik, I. Dubchak, D. Wolf, S. Spengler and M. Zorn, Analysis of ribosomal RNA sequences by combinatorial clustering, Proceedings, The Seventh International Conference on Intelligence Systems for Molecular Biology (ISMB99), (Eds. T. Lengauer et al.) AAAI/MIT Press, Menlo Park, CA. P. 287-296, 1999.

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Last updated 08/25/2020