Bo Chang



I am a software engineer at Google DeepMind, based in Toronto, Canada. Prior to that, I was a machine learning researcher at Borealis AI. I finished my Ph.D. in statistics at the University of British Columbia.

Email:

Education

Employment

Recommender Systems

  • Investigating Action-Space Generalization in Reinforcement Learning for Recommendation Systems
    Abhishek Naik, Bo Chang, Alexandros Karatzoglou, Martin Mladenov, Ed H. Chi, Minmin Chen
    The Web Conference (WWW), Decision Making for Information Retrieval and Recommender Systems Workshop, 2023.
    [Proceedings] [BibTeX]

    @inproceedings{naik2023investigating,
      title={Investigating Action-Space Generalization in Reinforcement Learning for Recommendation Systems},
      author={Naik, Abhishek and Chang, Bo and Karatzoglou, Alexandros and Mladenov, Martin and Chi, Ed H and Chen, Minmin},
      booktitle={Companion Proceedings of the ACM Web Conference 2023},
      pages={966--972},
      year={2023}
    }

  • Latent User Intent Modeling for Sequential Recommenders
    Bo Chang, Alexandros Karatzoglou, Yuyan Wang, Can Xu, Ed H. Chi, Minmin Chen
    The Web Conference (WWW), Industry Track, 2023.
    [arXiv] [Proceedings] [BibTeX]

    @article{chang2022latent,
      title={Latent User Intent Modeling for Sequential Recommenders},
      author={Chang, Bo and Karatzoglou, Alexandros and Wang, Yuyan and Xu, Can and Chi, Ed H and Chen, Minmin},
      journal={arXiv preprint arXiv:2211.09832},
      year={2022}
    }

  • Recency Dropout for Recurrent Recommender Systems
    Bo Chang, Can Xu, Matthieu Lê, Jingchen Feng, Ya Le, Sriraj Badam, Ed H. Chi, Minmin Chen
    Preprint, 2022.
    [arXiv] [BibTeX]

    @article{chang2022recency,
      title={Recency Dropout for Recurrent Recommender Systems},
      author={Chang, Bo and Xu, Can and L{\^e}, Matthieu and Feng, Jingchen and Le, Ya and Badam, Sriraj and Chi, Ed and Chen, Minmin},
      journal={arXiv preprint arXiv:2201.11016},
      year={2022}
    }

  • Learning to Augment for Casual User Recommendation
    Jianling Wang, Ya Le, Bo Chang, Yuyan Wang, Ed H. Chi, Minmin Chen
    The Web Conference (WWW), 2022.
    [Proceedings] [BibTeX]

    @inproceedings{wang2022learning,
      title={Learning to Augment for Casual User Recommendation},
      author={Wang, Jianling and Le, Ya and Chang, Bo and Wang, Yuyan and Chi, Ed H and Chen, Minmin},
      booktitle={Proceedings of the ACM Web Conference 2022},
      pages={2183--2194},
      year={2022}
    }

  • User Response Models to Improve a REINFORCE Recommender System
    Minmin Chen, Bo Chang, Can Xu, Ed H. Chi
    ACM International Conference on Web Search and Data Mining (WSDM), 2021.
    [Proceedings] [BibTeX]

    @inproceedings{chen2021user,
      title={User Response Models to Improve a REINFORCE Recommender System},
      author={Chen, Minmin and Chang, Bo and Xu, Can and Chi, Ed H},
      booktitle={Proceedings of the 14th ACM International Conference on Web Search and Data Mining},
      pages={121--129},
      year={2021}
    }

Machine Learning

  • Convolutional Neural Networks Combined with Runge–Kutta Methods
    Mai Zhu, Bo Chang, Chong Fu
    Neural Computing and Applications, 2022.
    [arXiv] [Journal] [BibTeX]

    @article{zhu2022convolutional,
      title={Convolutional neural networks combined with {R}unge--{K}utta methods},
      author={Zhu, Mai and Chang, Bo and Fu, Chong},
      journal={Neural Computing and Applications},
      year={2022},
      publisher={Springer}
    }

  • CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks
    Jiaqi Ma, Bo Chang, Xuefei Zhang, Qiaozhu Mei
    International Conference on Learning Representations (ICLR), 2021.
    [arXiv] [OpenReview] [BibTeX]

    @inproceedings{ma2021copulagnn,
      title={Copula{GNN}: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks},
      author={Ma, Jiaqi and Chang, Bo and Zhang, Xuefei and Mei, Qiaozhu},
      booktitle={International Conference on Learning Representations},
      year={2021},
      url={https://openreview.net/forum?id=XI-OJ5yyse}
    }

  • Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
    Ruizhi Deng, Bo Chang, Marcus A. Brubaker, Greg Mori, Andreas Lehrmann
    Advances in Neural Information Processing Systems (NeurIPS), 2020.
    [arXiv] [Proceedings] [BibTeX]

    @inproceedings{deng2020modeling,
      title={Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows},
      author={Deng, Ruizhi and Chang, Bo and Brubaker, Marcus A and Mori, Greg and Lehrmann, Andreas},
      booktitle={Advances in Neural Information Processing Systems},
      year={2020}
    }

  • Variational Hyper RNN for Sequence Modeling
    Ruizhi Deng, Yanshuai Cao, Bo Chang, Leonid Sigal, Greg Mori, Marcus A. Brubaker
    Preprint, 2020.
    [arXiv] [BibTeX]

    @article{deng2020variational,
      title={Variational Hyper {RNN} for Sequence Modeling},
      author={Deng, Ruizhi and Cao, Yanshuai and Chang, Bo and Sigal, Leonid and Mori, Greg and Brubaker, Marcus A},
      journal={arXiv preprint arXiv:2002.10501},
      year={2020}
    }

  • Point Process Flows
    Nazanin Mehrasa*, Ruizhi Deng*, Mohamed Osama Ahmed, Bo Chang, Jiawei He, Thibaut Durand, Marcus A. Brubaker, Greg Mori
    Temporal Point Processes (TPP) Workshop at NeurIPS, 2019.
    [arXiv] [BibTeX]

    @article{mehrasa2019point,
      title={Point Process Flows},
      author={Mehrasa, Nazanin and Deng, Ruizhi and Ahmed, Mohamed Osama and Chang, Bo and He, Jiawei and Durand, Thibaut and Brubaker, Marcus and Mori, Greg},
      journal={arXiv preprint arXiv:1910.08281},
      year={2019}.
    }

  • AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
    Bo Chang, Minmin Chen, Eldad Haber, Ed H. Chi
    International Conference on Learning Representations (ICLR), 2019.
    [arXiv] [OpenReview] [BibTeX]

    @inproceedings{chang2019antisymmetric,
      title={Antisymmetric{RNN}: a dynamical system view on recurrent neural networks},
      author={Chang, Bo and Chen, Minmin and Haber, Eldad and Chi, Ed H},
      booktitle={International Conference on Learning Representations},
      year={2019},
      url={https://openreview.net/forum?id=ryxepo0cFX}
    }

  • Vine Copula Structure Learning via Monte Carlo Tree Search
    Bo Chang, Shenyi Pan, Harry Joe
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
    [Proceedings] [GitHub] [BibTeX]

    @inproceedings{chang2019vine,
      title={Vine copula structure learning via {M}onte {C}arlo tree search},
      author={Chang, Bo and Pan, Shenyi and Joe, Harry},
      booktitle={International Conference on Artificial Intelligence and Statistics},
      year={2019}
    }

  • Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs
    Dar Gilboa, Bo Chang, Minmin Chen, Greg Yang, Samuel S. Schoenholz, Ed H. Chi, Jeffrey Pennington
    Preprint, 2019.
    [arXiv] [BibTeX]

    @article{gilboa2019dynamical,
      title={Dynamical isometry and a mean field theory of {LSTM}s and {GRU}s},
      author={Gilboa, Dar and Chang, Bo and Chen, Minmin and Yang, Greg and Schoenholz, Samuel S and Chi, Ed H and Pennington, Jeffrey},
      journal={arXiv preprint arXiv:1901.08987},
      year={2019}
    }

  • Multi-level Residual Networks from Dynamical Systems View
    Bo Chang*, Lili Meng*, Eldad Haber, Frederick Tung, David Begert
    International Conference on Learning Representations (ICLR), 2018.
    [arXiv] [OpenReview] [BibTeX]

    @inproceedings{chang2018multilevel,
      title={Multi-level residual networks from dynamical systems view},
      author={Chang, Bo and Meng, Lili and Haber, Eldad and Tung, Frederick and Begert, David},
      booktitle={International Conference on Learning Representations},
      year={2018},
      url={https://openreview.net/forum?id=SyJS-OgR-}
    }

  • Reversible Architectures for Arbitrarily Deep Residual Neural Networks
    Bo Chang*, Lili Meng*, Eldad Haber, Lars Ruthotto, David Begert, Elliot Holtham
    AAAI Conference on Artificial Intelligence (AAAI), 2018.
    [arXiv] [Proceedings] [BibTeX]

    @inproceedings{chang2018reversible,
      title={Reversible architectures for arbitrarily deep residual neural networks},
      author={Chang, Bo and Meng, Lili and Haber, Eldad and Ruthotto, Lars and Begert, David and Holtham, Elliot},
      booktitle={AAAI Conference on Artificial Intelligence},
      year={2018}
    }

Computer Vision

  • Interpretable Spatio-Temporal Attention for Video Action Recognition
    Lili Meng, Bo Zhao, Bo Chang, Gao Huang, Wei Sun, Frederick Tung, Leonid Sigal
    IEEE International Conference on Computer Vision (ICCV) Workshops, 2019.
    [arXiv] [Proceedings] [BibTeX]

    @inproceedings{meng2018interpretable,
      title = {Interpretable Spatio-Temporal Attention for Video Action Recognition},
      author = {Meng, Lili and Zhao, Bo and Chang, Bo and Huang, Gao and Sun, Wei and Tung, Frederick and Sigal, Leonid},
      booktitle = {International Conference on Computer Vision (ICCV) Workshops},
      year={2019}
    }

  • Modular Generative Adversarial Networks
    Bo Zhao, Bo Chang, Zequn Jie, Leonid Sigal
    European Conference on Computer Vision (ECCV), 2018.
    [arXiv] [Proceedings] [Media Coverage (in Chinese)] [BibTeX]

    @inproceedings{zhao2018modular,
      title={Modular generative adversarial networks},
      author={Zhao, Bo and Chang, Bo and Jie, Zequn and Sigal, Leonid},
      booktitle={European Conference on Computer Vision},
      pages={150--165},
      year={2018}
    }

  • Generating Handwritten Chinese Characters Using CycleGAN
    Bo Chang*, Qiong Zhang*, Shenyi Pan, Lili Meng
    IEEE Winter Conference on Applications of Computer Vision (WACV), 2018.
    [arXiv] [Proceedings] [GitHub] [BibTeX]

    @inproceedings{chang2018generating,
      title={Generating handwritten {C}hinese characters using {C}ycle{GAN}},
      author={Chang, Bo and Zhang, Qiong and Pan, Shenyi and Meng, Lili},
      booktitle={Winter Conference on Applications of Computer Vision},
      pages={199--207},
      year={2018}
    }

Statistics

  • Vine Regression with Bayes Nets: A Critical Comparison with Traditional Approaches Based on a Case Study on the Effects of Breastfeeding on IQ
    Roger M. Cooke, Harry Joe, Bo Chang
    Risk Analysis, 42(6), 1294–1305, 2022.
    [Journal] [BibTeX]

    @article{cooke2022vine,
      title={Vine Regression with Bayes Nets: A Critical Comparison with Traditional Approaches Based on a Case Study on the Effects of Breastfeeding on IQ},
      author={Cooke, Roger M and Joe, Harry and Chang, Bo},
      journal={Risk Analysis},
      volume = {42},
      number = {6},
      pages = {1294-1305},
      year = {2022},
      publisher={Wiley Online Library}
    }

  • Copula Diagnostics for Asymmetries and Conditional Dependence
    Bo Chang, Harry Joe
    Journal of Applied Statistics 47(9): 1587–1615, 2020.
    [Journal] [BibTeX]

    @article{chang2020copula,
      title = {Copula diagnostics for asymmetries and conditional dependence},
      author={Chang, Bo and Joe, Harry},
      journal={Journal of Applied Statistics},
      volume={47},
      number={9},
      pages={1587--1615},
      year={2020},
      publisher={Taylor & Francis}
    }

  • Vine Copula Regression for Observational Studies
    Roger M. Cooke, Harry Joe, Bo Chang
    AStA Advances in Statistical Analysis 104: 141–167, 2020.
    [Journal] [BibTeX]

    @article{cooke2020vine,
      title={Vine copula regression for observational studies},
      author={Cooke, Roger M and Joe, Harry and Chang, Bo},
      journal={AStA Advances in Statistical Analysis},
      volume={104},
      pages={141--167},
      year={2020},
      publisher={Springer}
    }

  • Prediction Based on Conditional Distributions of Vine Copulas
    Bo Chang, Harry Joe
    Computational Statistics & Data Analysis 139: 45–63, 2019.
    [arXiv] [Journal] [BibTeX]

    @article{chang2019prediction,
      title={Prediction based on conditional distributions of vine copulas},
      author={Chang, Bo and Joe, Harry},
      journal={Computational Statistics \& Data Analysis},
      volume={139},
      pages={45--63},
      year={2019},
      publisher={Elsevier}
    }

  • Vine Copulas: Dependence Structure Learning, Diagnostics, and Applications to Regression Analysis
    Bo Chang
    Ph.D. Thesis, University of British Columbia, 2019.
    [UBC Library] [BibTeX]

    @phdthesis{chang2019vinecopulas,
      series={Electronic Theses and Dissertations (ETDs) 2008+},
      title={Vine copulas: dependence structure learning, diagnostics, and applications to regression analysis},
      url={https://open.library.ubc.ca/collections/ubctheses/24/items/1.0379699},
      DOI={http://dx.doi.org/10.14288/1.0379699},
      school={University of British Columbia},
      author={Chang, Bo},
      year={2019},
      collection={Electronic Theses and Dissertations (ETDs) 2008+}
    }

  • Vine Regression
    Roger M. Cooke, Harry Joe, Bo Chang
    Resources for the Future Discussion Paper 15-52, 2015.
    [SSRN] [BibTeX]

    @article{cooke2015vine,
      title={Vine regression},
      author={Cooke, Roger M and Joe, Harry and Chang, Bo},
      journal={Resources for the Future Discussion Paper},
      volume={15-52},
      year={2015}
    }

* Equal Contribution