Selected Publications

Book

  • Gita Sukthankar, Robert P. Goldman, Christopher Geib, David Pynadath and Hung Hai Bui (Eds.) (2014) Plan, Activity, and Intent Recognition: Theory and Practice. Morgan Kaufmann. [Details]

Papers

  • Trung Le, Tuan Nguyen, Nhat Ho, Hung Bui, Dinh Phung (2021) LAMDA: Label Matching Deep Domain Adaptation. ICML 2021.

  • Tung Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon (2021) Temporal Predictive Coding For Model-Based Planning In Latent Space. ICML 2021.

  • Khai Nguyen, Nhat Ho, Tung Pham, Hung Bui (2021) Distributional Sliced-Wasserstein and Applications to Generative Modeling. ICLR 2021.

  • Khai Nguyen, Son Nguyen, Nhat Ho, Tung Pham, Hung Bui (2021) Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein. ICLR 2021.

  • Anh Tong , Toan Tran , Hung Bui , Jaesik Choi (2021) Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior. AAAI 2021.

  • C Nhan Duong, TD Truong, K Gia Quach, Hung Bui, K Roy, K Luu (2020) Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition. CVPR 2020. [Paper]

  • Zhe Dong , Bryan A. Seybold , Kevin P. Murphy , Hung Bui (2020) Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems. ICML 2020. [Paper]

  • Khiem Pham*, Khang Le*, Nhat Ho, Tung Pham, Hung Bui (2020) On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm. ICML 2020. [Paper]

  • Rui Shu*, Tung Nguyen*, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung Bui (2020) Predictive Coding for Locally-Linear Control. ICML 2020. [Paper] [Code]

  • Nir Levine , Yinlam Chow , Rui Shu , Ang Li , Mohammad Ghavamzadeh , Hung Bui (2020) Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control. ICLR 2020. [Paper] [Code]

  • Shu R., Bui H., Whang J. and Ermon S. (2019) Training Variational Autoencoders with Buffered Stochastic Variational Inference. AISTATS 2019. [Details]

  • Shu R., Bui H., Zhao S., Kochenderfer M. and Ermon S. (2018) Amortized inference regularization. NIPS 2018. [Details]

  • Tran Q H., Lai T., Zukerman I., Haffari G., Bui T. and Bui H. (2018) The Context-dependent Additive Recurrent Neural Net. NAACL HLT 2018. [PDF]

  • Shu R., Bui H., Narui H., and Ermon S. (2018) A DIRT-T approach to unsupervised domain adaptation. ICLR 2018. [Details]

  • Banijamali E., Shu R., Ghavamzadeh M., Bui H., Ghodsi A. Robust Locally-Linear Controllable Embedding. AISTAT 2018. [Details]

  • Ho N., Nguyen X., Yurochkin M., Bui H., Huynh V., and Phung, D. (2017) Multilevel clustering via Wasserstein means. ICML 2017. [Details]

  • Shu R., Bui H., and Ghavamzadeh, M. (2017) Bottleneck conditional density estimation. ICML 2017. [Details] [Demo]

  • Nguyen, V., Phung, D., Le, T., Venkatesh S., and Bui, H. (2017) Discriminative Bayesian nonparametric clustering. IJCAI 2017.

  • Nguyen, T., Le , T., Bui, H., and Phung, D. (2017) Large-­scale online kernel learning with random feature reparameterization. IJCAI 2017.

  • Dernoncourt F., Lee J Y., Bui T., and Bui H. (2016) Robust dialogue state tracking for large ontologies. International Workshop on Spoken Dialogue Systems Technology (IWSDS 2016). Winning entry at the 4th Dialogue State Tracking Challenge. [Details]

  • Huynh V., Phung D., Venkatesh S., Nguyen X., Hoffman M., and Bui H. (2016) Scalable nonparametric Bayesian multilevel clustering, in UAI 2016. [PDF] [Poster]

  • Sedhain S., Bui, H., Kawale, J., Vlassis, N., Kveton, B., Menon, A., Bui, T. and Scanner, S. (2016) Practical linear models for large-scale one-class collaborative filtering, in IJCAI 2016. [PDF][Slides]

  • Kveton, B., Bui, H., Ghavamzadeh, M., Theocharous, G., Muthukrishnan, S., and Sun S. (2016) Graphical model sketch, in ECML-PKDD 2016 (to appear). [Details]

  • Kawale, J., Bui, H., Kveton, B., Tran-Thanh, L., Chawla S. (2015) Efficient Thompson sampling for online matrix-factorization recommendation, in NIPS 2015. [Details]

  • Bui, H. and Huynh, T. and Sontag, D. (2014) Lifted tree-reweighted variational inference, in UAI 2014. [Details]

  • Nguyen, V., Phung, D., Nguyen, X., Venkatesh, S. and Bui, H. (2014) Bayesian nonparametric multilevel clustering with group-level contexts, in ICML 2014. [Details]

  • Bui, H. and Huynh, T. and Riedel, S. (2013) Automorphism groups of graphical models and lifted variational inference, in UAI 2013. (oral presentation) [PDF] [Slides][Earlier version with proofs]

  • Bui, H. and Huynh, T. and de Salvo Braz, R. (2012) Exact lifted inference with distinct soft evidence on every object, in AAAI, 2012. (oral presentation) [PDF, Details] [Slides]

  • Bui, H. and Huynh, T. and Riedel, S. (2012) Automorphism groups of graphical models and lifted variational inference, in 2nd Workshop on Statistical Relational AI at UAI-2012 (StaRAI-12), 2012. [PDF, Details]

  • Choi, J. and de Salvo Braz, R. and Bui, H. (2011) Efficient Methods for Lifted Inference with Aggregate Factors, in AAAI 2011, 2011. [PDF, Details]

  • Bui, H. H. and Yorke-Smith, N. (2010) Efficient Variable Elimination for Semi-Structured Simple Temporal Networks with Continuous Domains. Knowledge Engineering Review, vol. 25, no. 3, pp. 337-351, September 2010. [Details]

  • Pham, D-S. and Bui, H. and Venkatesh, S. (2010) Bayesian Minimax Estimation of the Normal Model with Incomplete Prior Covariance Matrix Specification. IEEE Transactions on Information Theory, vol. 56, pp. 6433 - 6449 , 2010. [PDF, Details]

  • de Salvo Braz, R. and Natarajan, S. and Bui, H. and Shavlik, J. and Russell, S. (2009). Anytime Lifted Belief Propagation. 6th International Workshop on Statistical Relational Learning, Leuven, Belgium. [PDF]

  • Duong, T. and Phung, D. and Bui, H. and Venkatesh, S. (2009) Efficient duration and hierarchical modeling for human activity recognition. Artificial Intelligence, vol. 173, pp. 830-856, May 2009. [PDF, Details]

  • Madani, O. and Bui, H. and Yeh, E. (2009) Efficient online learning and prediction of users' desktop actions, in IJCAI-2009, pp. 1457-1462, 2009. [PDF, Details]

  • Natarajan, S., Bui, H., Tadepalli, P., Kersting, K., and Wong, W.-K. (2008). Logical Hierarchical Hidden Markov Models for Modeling User Activities. In Proceedings of the Eighteenth International Conference on Inductive Logic Programming (ILP 2009), (pp. 192-209), Springer. [PDF]

  • Bui, H. H., Tyson, M. and Yorke-Smith, N. (2008) Efficient Message Passing and Propagation of Simple Temporal Constraints: Results on Semi-Structured Networks, in Proceedings of CP/ICAPS’08 Joint Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems, Sydney, Australia, Sep 2008. [PDF, Details]

  • Bui, H. H. and Phung, D. and Venkatesh, S. and Phan, H. (2008) The Hidden Permutation Model and Location-based Activity Recognition, in AAAI 2008, 2008. [PDF, Details][Slides]

  • Connolly, C. I. and Burns, J. B. and Bui, H. H. (2008) Recovering Social Networks From Massive Track Datasets, in IEEE International Workshop on Applications of Computer Vision, IEEE, January 2008. [PDF, Details]

  • Tran The Truyen and Dinh Q. Phung and Hung H. Bui and Svetha Venkatesh. (2008) Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data, in Neural Information Processing Systems (NIPS), 2008. [PDF, Details]

  • Connolly, C. I. and Burns, J. B. and Bui, H. H. (2007) Sampling Stable Properties of Massive Track Datasets, in Proceedings of the 2007 Workshop on Massive Datasets, November 2007. [PDF, Details]

  • Connolly, C. I. and Burns, J. B. and Bui, H. H. (2007) Recovering Social Networks From Massive Track Datasets, Technical Note 564. AI Center, SRI International, 333 Ravenswood Ave., Menlo Park, CA 94025, October 2007. [PDF,Details]

  • Bui, H. H. and Tyson, M. and Yorke-Smith, N. (2007) Efficient Message Passing and Propagation of Simple Temporal Constraints, in Proceedings of AAAI 2007 Workshop on Spatial and Temporal Reasoning, Vancouver, Canada, pp. 9–15, Jul 2007. [PDF, Details]

  • Truyen, T. T. and Phung, D. and Bui, H. H. and Venkatesh, S. (2006) AdaBoost.MRF: Boosted Markov random forests and application to multilevel activity recognition, in IEEE International Conference on Computer Vision and Pattern Recognition, CVPR 2006. [PDF]

  • Duong, T. and Bui, H. and Phung, D. and Vekatesh, S. (2005) Activity recognition and abnormality detection with the switching hidden semi-Markov model, in IEEE International Conference on Computer Vision and Pattern Recognition, CVPR 2005. [PDF, Details]

  • Nguyen, N. and Phung, D. and Venkatesh, S. and Bui, H. (2005) Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model, in IEEE International Conference on Computer Vision and Pattern Recognition, CVPR 2005. [PDF, Details]

  • Bui, H. and Phung, D. and Venkatesh, S. (2004) Hierarchical Hidden Markov Models with General State Hierarchy, in AAAI 2004, 2004. [PDF, Details]

  • Bui, H. (2003) A general model for online probabilistic plan recognition, in IJCAI 2003, 2003. [PDF, Details]

  • Nguyen, N. and Bui, H. and Venkatesh, S. and West, G. (2003) Recognising and monitoring high-level behaviours in complex spatial environments, in CVPR 2003, 2003. [PDF, Details]

  • Bui, H. and Venkatesh, S. and West, G. (2002) Policy Recognition in the Abstract Hidden Markov Model. Journal of Artificial Intelligence Research, vol. 17, pp. 451-499, 2002. [PDF, Details]

  • Bui, H. and Venkatesh, S. and West, G. (2000) On the recognition of abstract Markov policies, in AAAI 2000, 2000. [PS, Details]

  • Bui, H. H. and Venkatesh, S. and Kieronska, D. (1999) Learning other agents' preferences in multi-agent negotiation using the Bayesian classifier. International Journal of Cooperative Information Systems, vol. 8, no. 4, pp. 275-294, 1999. [Details]

  • Bui, H. H. and Kieronska, D. and Venkatesh, S. (1996) Learning other agents’ preferences in multiagent negotiation, in Proceedings of the National Conference on Artificial Intelligence (AAAI-96), pp. 114-119, 1996. [PS, Details]

Demonstrations

  • Bui, H. H., Cesari, F., Elenius, D., House, N., Morley, D., Myers, K. M., Natarajan, S., Saadati, S., Yeh, E. and Yorke-Smith, N. (2008) CALO Workflow Recognition and Proactive Assistance, in AAAI-08 AI Video Competition, Chicago, IL, Jul 2008. [Video]

Tutorials and technical notes

  • AAAI 2014 tutorial on Lifted Approximate Inference (with F. Hadiji , K. Kersting, M. Mladenov and S. Natarajan) [link]

  • Tutorial on exponential family and statistical inference [slides]