Publications

Thesis:

C. Zhang, Structured Representation Using Latent Variable Models, PhD Thesis, 2016
[PDF]

Publications:

  • C. Zhang, C. Öztireli, S. Mandt, G. Salvi,
    Active Mini-Batch Sampling using Repulsive Point Processes,
    The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019 (accepted; acceptance rate 16.2%)
    [arXiv]
  • M. Klasson, C. Zhang, and H. Kjellström,
    A hierarchical grocery store image dataset with visual and semantic labels.
    IEEE Winter Conference on Applications of Computer Vision (WACV), 2019.
  • C. Zhang, J. Butepage, H. Kjellstrom, and S. Mandt,
    Advances in Variational Inference,
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI, accepted)
    [ArXiv pdf]
  • C. Ma, S. Tschiatschek, K. Palla, J.M.H. Lobato, S. Nowozin, C. Zhang,
    EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE,
    under review, 2018
    [PDF]
  • J. Butepage, J. He, C. Zhang, L.Sigal, G. Mori, S. Mandt,
    Informed Priors for Deep Representation Learning,
    Symposium on Advances in Approximate Bayesian Inference, 2018
  • T.F. Fu, C. ZhangS. Mandt,
    Continuous Word Embedding Fusion via Spectral Decomposition,
    The SIGNLL Conference on Computational Natural Language Learning  (CoNLL), 2018
    [Preprint] [Appendix]
  • T. Ding, C. Zhang, M. Bos,
    Causal Feature Selection for Individual Characteristics Prediction,
    International Conference on Tools with Artificial Intelligence (ICTAI), 2018
    [arXiv]
  • R.  Tu*,  C. Zhang*,  P. Ackermann, K. Mohan, H. Kjellström, K. Zhang*,
    Causal discovery in the presence of missing data,
    under review, 2018
    [arXiv]
  • C.  Hamesse,  P.  Ackermann, H. Kjellstrom, and C. Zhang,
    Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation,
    ICML IJCAI Joint Workshop on Artificial Intelligence in Health (AIH 2018)
    [pdf]
  • C. Zhang, C. Oztireli and S. Mandt,
    Diversified Mini-Batch Sampling using Repulsive Point Processes,
    NIPS Workshop on Advances in Approximate Bayesian Inference, 2017
    [PDF]
  • M. Klasson, K. Zhang, B. Bertilson, C. Zhang and H. Kjellström,
    Causality Refined Diagnostic Prediction,
    NIPS Workshop on Machine Learning for Health (ML4H), 2017
    [PDF]
  • R. Bamler*, C. Zhang*, M. Opper and S. Mandt,
    Perturbative Black Box Variational Inference,
    Conference on Neural Information Processing Systems  (NIPS 2017)(*Joint first authorship; order decided by coin flip.) [pdf]
  • C. Zhang, H. Kjellström, and S. Mandt,
    Determinantal Point Processes for Mini-batch Diversification,
    Uncertainty in Artificial Intelligence (UAI 2017). Plenary talk
    [pdf]
  • C. Zhang, S. Mandt and H. Kjellström,
    Balanced Population Stochastic Variational Inference,
    NIPS Workshop on Advances in Approximate Bayesian Inference, 2016
    [pdf]
  • A. Qu, C. Zhang, P. Ackermann and H. Kjellström,
    Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation,
    NIPS Workshop on Machine Learning for Health, 2016
    [pdf]
  • C. Zhang, H. Kjellström and B. C. Bertilson,
    Diagnostic Prediction Using Discomfort Drawings,
    NIPS Workshop on Machine Learning for Health, 2016 (This is an extension of the MLHC conference paper)
    [pdf]
  • C. Zhang, H. Kjellström and C.H. Ek,
    Inter-Battery Topic Representation Learning
    European Conference on Computer Vision (ECCV), 2016
    [pdf] [Supplement]
  • C. Zhang, H. Kjellström, C.H. Ek and B. C. Bertilson,
    Diagnostic Prediction Using Discomfort Drawing with IBTM,
    Machine Learning in Health Care Conference, 2016
    [pdf]
  • K. Zhang, J. Karlgren, C. Zhang, J. Lagergren.
    Viewpoint and Topic Modeling of Current Events,
    arXiv, 2016  [arXiv]
  • C. Zhang, M. Gartrell, T. P. Minka, Y. Zaykov and J. Guiver,
    GroupBox: A generative model for group recommendation,
    Microsoft Research technical report; MSR-TR-2015-61
    [PDF]
  • C. Zhang and H. Kjellström,
    How to Supervise Topic Models,
    ECCV workshop on Graphical Models in Computer Vision (ECCVws 2014, GMCV) (oral)
    [PDF][Supplement][Slides]
  • C. Zhang, C.H. Ek, X. Gratal, F. T. Pokorny and H. Kjellström,
    Supervised Hierarchical Dirichlet Processes with Variational Inference,
    IEEE ICCV workshop on Inference for probabilistic graphical models (ICCVws 2013, Infer PGM) (oral)
    [PDF] [Supplement][Slides][Poster][Code]
  • C. Zhang, D. Song and H. Kjellström,
    Contextual Modeling with Labeled Multi-LDA,
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013)
    [PDF][Bibtex] [Slides]
  • C. Zhang, C.H. Ek, A. Damianou, H. Kjellström,
    Factorized Topic Models.
    International Conference on Learning Representations 2013, (ICLR 2013)
    [PDF] [Bibtex][Poster]
  • C. Zhang and H. Kjellström,
    Multi-Class Detection and Segmentation of Objects in Depth
    [PDF] [Bibtex]
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