Mingming Gong


Selected Publications (* co-first author)


Preprints

  • Twin Auxiliary Classifiers GAN. [PDF]
    Mingming Gong*, Yanwu Xu*, Chunyuan Li, Kun Zhang, and Kayhan Batmanghelich.
    arXiv:1907.02690.

  • Generative-Discriminative Complementary Learning. [PDF]
    Yanwu Xu*, Mingming Gong*, Junxiang Chen, Tongliang Liu, Kun Zhang, and Kayhan Batmanghelich.
    arXiv:1904.01612.

  • Causal Generative Domain Adaptation Networks. [PDF]
    Mingming Gong*, Kun Zhang*, Biwei Huang, Clark Glymour, Dacheng Tao, and Kayhan Batmanghelich.
    arXiv:1804.04333.

  • Transfer Learning with Label Noise. [PDF]
    Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, and Dacheng Tao.
    arXiv:1707.09724.


2019

  1. Discovery and Forecasting in Nonstationary Environments with State-Space Models. [PDF][SUPP][CODE]
    Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour.
    In Proceedings of the 36th International Conference on Machine Learning (ICML 2019).

  2. Geometry-Consistent Adversarial Networks for Unsupervised Domain Mapping. [PDF][CODE]
    Huan Fu*, Mingming Gong*, Chaohui Wang, Kayhan Batmanghelich, Kun Zhang, and Dacheng Tao.
    In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019). (Oral, acceptance rate 5.6%)
    This paper was a finalist for the best paper award.

  3. Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation. [PDF][CODE]
    Shanshan Zhao, Huan Fu, Mingming Gong, and Dacheng Tao.
    In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019).

  4. Low-Dimensional Density Ratio Estimation for Covariate Shift Correction. [PDF]
    Petar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang.
    In Proceedings of 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019).

  5. Data-Driven Approach to Multiple-Source Domain Adaptation. [PDF]
    Petar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang.
    In Proceedings of 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019).


2018

  1. Robust Angular Loss for Local Descriptor Learning. [PDF][CODE]
    Yanwu Xu, Mingming Gong, Tongliang Liu, Kayhan Batmanghelich, and Chaohui Wang.
    In Proceedings of Asian Conference on Computer Vision (ACCV 2018).

  2. Modeling Dynamic Missingness of Implicit Feedback for Recommendation. [PDF]
    Menghan Wang, Mingming Gong, Xiaolin Zheng, and Kun Zhang.
    In Proceedings of 32nd Conference on Neural Information Processing Systems (NeurIPS 2018).

  3. Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. [PDF]
    Kun Zhang, Mingming Gong, Joseph Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour​.
    In Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI 2018). (Oral, acceptance rate 8.9%)

  4. Generative-Discriminative Approach from a Bag of Image Patches to a Vector. [PDF]
    Sumedha Singla, Mingming Gong, Siamak Ravanbakhsh, Barnabas Poczos, and Kayhan Batmanghelich.
    In Proceedings of the 21th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018).

  5. Learning with Biased Complementary Labels. [PDF]
    Xiyu Yu, Tongliang Liu, Mingming Gong, and Dacheng Tao.
    In Proceedings of 2018 European Conference on Computer Vision (ECCV 2018). (Oral, acceptance rate 2.4%)

  6. Correcting the Triplet Selection Bias for Triplet Loss. [PDF]
    Baosheng Yu, Tongliang Liu, Mingming Gong, Changxing Ding, and Dacheng Tao.
    In Proceedings of European Conference on Computer Vision (ECCV 2018).

  7. Deep Domain Generalization via Conditional Invariant Adversarial Networks. [PDF][CODE]
    Ya Li, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, and Dacheng Tao.
    In Proceedings of European Conference on Computer Vision (ECCV 2018).

  8. Deep Ordinal Regression Network for Monocular Depth Estimation. [PDF][CODE]
    Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao.
    In Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018).
    This algorithm won the 1st prize in single image depth prediction competition, Robust Vision Challenge 2018.

  9. An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption. [PDF]
    Xiyu Yu, Tongliang Liu, Mingming Gong, Kayhan Batmanghelich, and Dacheng Tao.
    In Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018).

  10. Domain Generalization via Conditional Invariant Representations. [PDF][CODE]
    Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, and Dacheng Tao.
    In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI 2018). (Oral, acceptance rate 11.0%)


2017

  1. Causal Discovery from Temporally Aggregated Time Series. [PDF]
    Mingming Gong, Kun Zhang, Bernhard Schölkopf, Clark Glymour, and Dacheng Tao.
    In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI 2017).

  2. A Coarse-Fine Network for Keypoint Localization. [PDF]
    Shaoli Huang, Mingming Gong, and Dacheng Tao.
    In Proceedings of IEEE International Conference on Computer Vision (ICCV 2017). (Spotlight, acceptance rate 2.6%)

  3. Large Cone Non-negative Matrix Factorization. [PDF]
    Tongliang Liu, Mingming Gong, and Dacheng Tao.
    IEEE Transactions on Neural Networks and Learning Systems 28(9): 2129-2142 (2017).


2016

  1. Domain Adaptation with Conditional Transferable Components. [PDF][CODE]
    Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, and Bernhard Schölkopf.
    In Proceedings of the 33rd International Conference on Machine Learning (ICML 2016).


2015

  1. Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components. [PDF][CODE]
    Philipp Geiger, Kun Zhang, Mingming Gong, Bernhard Schölkopf, and Dominik Janzing.
    In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015).

  2. Discovering Temporal Causal Relations from Subsampled Data. [PDF][CODE]
    Mingming Gong*, Kun Zhang*, Bernhard Schölkopf, Dacheng Tao, and Philipp Geiger.
    In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015).

  3. Multi-Source Domain Adaptation: A Causal View. [PDF][CODE]
    Kun Zhang, Mingming Gong, and Bernhard Schölkopf.
    In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI 2015).