Publications

Back to staff profile

  • Yanwu Xu, M. GONG, Junxiang Chen, T. Liu, Kun Zhang, Kayhan Batmanghelich. Generative-Discriminative Complementary Learning. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI; CORE Rank A*), 6526-6533, 2020.

  • Biwei Huang, Kun Zhang, M. GONG, Clark Glymour. Causal Discovery from Non-Identical Variable Sets. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI; CORE Rank A*), 10153-10161, 2020.

  • Chen Ziye, M. GONG, Yanwu Xu, Chaohui Wang, Kun Zhang, Bo Du. Compressed Self-Attention for Deep Metric Learning. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI; CORE Rank A*), 3561-3568, 2020.

  • Shanshan Zhao, Huan Fu, M. GONG, Dacheng Tao. Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR, CORE Rank A*), 9788-9798, 2019.

  • Petar Stojanov, M. GONG, Jaime Carbonell, Kun Zhang. Data-Driven Approach to Multiple-Source Domain Adaptation. The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS, CORE Rank A), 3487-3496, 2019.

  • Petar Stojanov, M. GONG, Jaime Carbonell, Kun Zhang. Low-Dimensional Density Ratio Estimation for Covariate Shift Correction. The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS, CORE Rank A), 3449-3458, 2019.

  • Biwei Huang, Kun Zhang, M. GONG, Clark Glymour. Causal discovery and forecasting in nonstationary environments with state-space models. International Conference on Machine Learning (ICML; CORE Rank A*), 2901-2910, 2019.

  • Huan Fu, M. GONG, Chaohui Wang, Kayhan Batmanghelich, Kun Zhang, Dacheng Tao. Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR; CORE Rank A*), 2427-2436, 2019.

  • M. GONG, Yanwu Xu, C. Li, Kun Zhang, Kayhan Batmanghelich. Twin Auxilary Classifiers GAN. Advances in Neural Information Processing Systems (NeurIPS; CORE Rank A*), 1328-1337, 2019.

  • Xiyu Yu, T. Liu, M. GONG, Dacheng Tao. Learning with biased complementary labels. Proceedings of the European Conference on Computer Vision (ECCV; CORE Rank A), 68-83, 2018.

  • Xiyu Yu, T. Liu, M. GONG, Kayhan Batmanghelich, Dacheng Tao. An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR; CORE Rank A*), 4480-4489, 2018.

  • H Fu, M. GONG, C Wang, D Tao. MoE-SPNet: A mixture-of-experts scene parsing network. Pattern Recognition (PR; ERA Rank A*), 84, 226-236, 2018. doi: 10.1016/j.patcog.2018.07.020.

  • Sumedha Singla, M. GONG, Siamak Ravanbakhsh, Frank Sciurba, Barnabas Poczos, Kayhan N Batmanghelich. Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI; CORE Rank A), 502-510, 2018.

  • Ya Li, M. GONG, Xinmei Tian, T. Liu, Dacheng Tao. Domain generalization via conditional invariant representations. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI; CORE Rank A*), 2018.

  • Baosheng Yu, T. Liu, M. GONG, Changxing Ding, Dacheng Tao. Correcting the Triplet Selection Bias for Triplet Loss. Proceedings of the European Conference on Computer Vision (ECCV, CORE Rank A), 71-87, 2018.

  • Ya Li, Xinmei Tian, M. GONG, Yajing Liu, Kun Zhang, Dacheng Tao. Deep Domain Generalization via Conditional Invariant Adversarial Networks. Proceedings of the European Conference on Computer Vision (ECCV, CORE Rank A), 624-639, 2018.

  • Menghan Wang, M. GONG, Xiaolin Zheng, Kun Zhang. Modeling Dynamic Missingness of Implicit Feedback for Recommendation. Advances in Neural Information Processing Systems (NeurIPS; CORE Rank A*), 6669-6678, 2018.

  • Yanwu Xu, M. GONG, T. Liu, Kayhan Batmanghelich, Chaohui Wang. Robust Angular Local Descriptor Learning. Asian Conference on Computer Vision (ACCV), 420-435, 2018.

  • Kun Zhang, M. GONG, Joseph Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour. Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results.. Conference on Uncertainty in Artificial Intelligence (UAI; CORE Rank A*), 1063-1072, 2018.

  • M. GONG, Kun Zhang, Bernhard Schölkopf, Clark Glymour, Dacheng Tao. Causal discovery from temporally aggregated time series. Conference on Uncertainty in Artificial Intelligence (UAI; CORE Rank A*), 2017, 2017.

  • Shaoli Huang, M. GONG, Dacheng Tao. A coarse-fine network for keypoint localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV; CORE Rank A*), 3028-3037, 2017.

  • T. Liu, M. GONG, Dacheng Tao. Large-cone nonnegative matrix factorization. IEEE transactions on neural networks and learning systems (TNNLS; ERA Rank A*), 28, 2129-2142, 2016.

  • Dawei Weng, Yunhong Wang, M. GONG, Dacheng Tao, Hui Wei, Di Huang. DERF: distinctive efficient robust features from the biological modeling of the P ganglion cells.. IEEE Transactions on Image Processing (TIP; ERA Rank A*), 24, 2287-2302, 2015. doi: 10.1109/TIP.2015.2409739.

  • Kun Zhang, M. GONG, Bernhard Schölkopf. Multi-source domain adaptation: A causal view. Twenty-ninth AAAI conference on artificial intelligence (AAAI; CORE Rank A*), 2015.

  • M. GONG, Kun Zhang, Bernhard Schoelkopf, Dacheng Tao, Philipp Geiger. Discovering Temporal Causal Relations from Subsampled Data.. International Conference on Machine Learning (ICML; CORE Rank A*), 1898-1906, 2015.

  • Philipp Geiger, Kun Zhang, M. GONG, Bernhard Schoelkopf, Dominik Janzing. Causal inference by identification of vector autoregressive processes with hidden components. International Conference on Machine Learning (ICML; CORE Rank A*), 1917-1925, 2015.