University of Melbourne

Causal Learning & Reasoning Group


2022


  • Counterfactual Fairness with Partially Known Causal Graph. [PDF]
    A. Zuo, S. Wei, T. Liu, B. Han, K. Zhang, M. Gong.
    In NeurIPS, 2022.

  • MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. [PDF]
    E. Gao*, I. Ng*, M. Gong, L. Shen, W. Huang, T. Liu, K. Zhang, H. Bondell.
    In NeurIPS, 2022.

  • Truncated Matrix Power Iteration for Differentiable DAG Learning. [PDF]
    Z. Zhang, I. Ng, D. Gong, Y. Liu, E.M. Abbasnejad, M. Gong, K. Zhang, J.Q. Shi.
    In NeurIPS, 2022.

  • Fair Classification with Instance-dependent Label Noise. [PDF]
    S. Wu, M. Gong, B. Han, Y. Liu, T. Liu
    In CLeaR, 2022.

  • A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning. [PDF]
    J. Guo, M. Gong, D. Tao
    In ICLR, 2022.

  • Adversarial Robustness Through the Lens of Causality. [PDF] [CODE]
    Y. Zhang, M. Gong, T. Liu, G. Niu, X. Tian, B. Han, B. Schölkopf, and K. Zhang
    In ICLR, 2022.


2021


  • A unified framework for specification tests of continuous treatment effect models. [PDF]
    W. Huang, O. Linton, Z. Zhang
    Journal of Business and Economic Statistics, 2021.

  • Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?. [PDF] [CODE]
    P. Stojanov, Z. Li, M. Gong, R. Cai, J.G. Carbonell, and K. Zhang
    In NeurIPS, 2021.

  • Instance-dependent Label-noise Learning under a Structural Causal Model. [PDF] [CODE]
    Y. Yao, T. Liu, M. Gong, B. Han, G. Niu, and K. Zhang.
    In NeurIPS, 2021.

  • Unaligned Image-to-Image Translation by Learning to Reweight. [PDF] [CODE]
    S. Xie, M. Gong, Y. Xu, and K. Zhang
    In ICCV, 2021.


2020


  • Domain Adaptation As a Problem of Inference on Graphical Models. [PDF][CODE]
    K. Zhang*, M. Gong*, P. Stojanov, B. Huang, Qingsong Liu, and C. Glymour.
    In NeurIPS, 2020.

  • Domain Generalization via Entropy Regularization. [PDF][CODE]
    S. Zhao, M. Gong, T. Liu, H. Fu, and D. Tao.
    In NeurIPS, 2020.

  • Label-Noise Robust Domain Adaptation. [PDF]
    X. Yu, T. Liu, M. Gong, K. Zhang, K. Batmanghelich, and D. Tao.
    In ICML, 2020.

  • LTF: A Label Transformation Framework for Correcting Target Shift. [PDF][CODE]
    J. Guo, M. Gong, T. Liu, K. Zhang, and D. Tao.
    In ICML, 2020.

  • Causal Discovery from Non-Identical Variable Sets. [PDF]
    B. Huang, K. Zhang, M. Gong, and C. Glymour.
    In AAAI, 2020.


2019


  • Likelihood-Free Overcomplete ICA and Applications in Causal Discovery. [PDF][CODE]
    C. Ding, M. Gong, K. Zhang, and D. Tao.
    In NeurIPS, 2019. (Spotlight, acceptance rate 2.4%)

  • Specific and Shared Causal Relation Modeling and Mechanism-based Clustering. [PDF]
    B. Huang, K. Zhang, P. Xie, M. Gong, E. P. Xing, and C. Glymour.
    In NeurIPS, 2019.

  • Discovery and Forecasting in Nonstationary Environments with State-Space Models. [PDF][SUPP][CODE]
    B. Huang, K. Zhang, M. Gong, and C. Glymour.
    In ICML, 2019.

  • Data-Driven Approach to Multiple-Source Domain Adaptation. [PDF]
    P. Stojanov, M. Gong, J. G. Carbonell, and K. Zhang.
    In AISTATS, 2019.


2018


  • Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. [PDF]
    K. Zhang, M. Gong, J. Ramsey, K. Batmanghelich, P. Spirtes, and C. Glymour​.
    In UAI, 2018. (Oral, acceptance rate 8.9%)

  • Deep Domain Generalization via Conditional Invariant Adversarial Networks. [PDF][CODE]
    Y. Li, X. Tian, M. Gong, Y. Liu, T. Liu, K. Zhang, and D. Tao.
    In ECCV, 2018.

  • Domain Generalization via Conditional Invariant Representations. [PDF][CODE]
    Y. Li, M. Gong, X. Tian, T. Liu, and D. Tao.
    In AAAI, 2018 (Oral, acceptance rate 11.0%)


Before 2018


  • Causal Discovery from Temporally Aggregated Time Series. [PDF]
    M. Gong, K. Zhang, B. Schölkopf, C. Glymour, and D. Tao.
    In UAI, 2017.

  • Domain Adaptation with Conditional Transferable Components. [PDF][CODE]
    M. Gong, K. Zhang, T. Liu, D. Tao, C. Glymour, and B. Schölkopf.
    In ICML, 2016.

  • Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components. [PDF][CODE]
    P. Geiger, K. Zhang, M. Gong, B. Schölkopf, and D. Janzing.
    In ICML, 2015.

  • Discovering Temporal Causal Relations from Subsampled Data. [PDF][CODE]
    M. Gong*, K. Zhang*, B. Schölkopf, D. Tao, and P. Geiger.
    In ICML, 2015.

  • Multi-Source Domain Adaptation: A Causal View. [PDF][CODE]
    K. Zhang, M. Gong, and B. Schölkopf.
    In AAAI, 2015.