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<h2><hr><a name="cd"></a>Conference (* Equal contribution)</h2>
<ul>
<li><p>
Identifiability Analysis of Linear ODE Systems with Hidden Confounders. [<A HREF="https://arxiv.org/pdf/2410.21917?">PDF</A>]<br>
Y. Wang, B. Huang, W. Huang, X. Geng, and <b>M. Gong</b>.<br>
In <a href="https://neurips.cc/Conferences/2024"> NeurIPS</a>, 2024.
</p></li>
<li><p>
In-N-Out: Lifting 2D Diffusion Prior for 3D Object Removal via Tuning-Free Latents Alignment. [<A HREF="https://openreview.net/forum?id=gffaYDu9mM">PDF</A>]<br>
D. Hu, H. Fu, J. Guo, L. Peng, T. Chu, F. Liu, T. Liu, and <b>M. Gong</b>.<br>
In <a href="https://neurips.cc/Conferences/2024"> NeurIPS</a>, 2024.
</p></li>
<li><p>
Discovery of the Hidden World with Large Language Models. [<A HREF="https://arxiv.org/pdf/2402.03941">PDF</A>]<br>
C. Liu*, Y. Chen*, T. Liu, <b>M. Gong</b>, J. Cheng, B. Han, K. Zhang.<br>
In <a href="https://neurips.cc/Conferences/2024"> NeurIPS</a>, 2024.
</p></li>
<li><p>
Neural Collapse Inspired Feature Alignment for Out-of-Distribution Generalization. [<A HREF="https://openreview.net/pdf?id=wQpNG9JnPK">PDF</A>]<br>
Z. Chen, M. Zhang, S. Cui, H. Li, G. Niu, <b>M. Gong</b>, C. Zhang, and K. Zhang.<br>
In <a href="https://neurips.cc/Conferences/2024"> NeurIPS</a>, 2024.
</p></li>
<li><p>
Physics-Informed Knowledge Transfer for Underwater Monocular Depth Estimation. [<A HREF="https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/09034.pdf">PDF</A>]<br>
J. Yang, <b>M. Gong</b>, and Y. Pu.<br>
In <a href="https://eccv.ecva.net/Conferences/2024"> ECCV</a>, 2024.
</p></li>
<li><p>
Self-Distilled Disentangled Learning for Counterfactual Prediction. [<A HREF="https://arxiv.org/abs/2406.05855">PDF</A>]<br>
X. Li, <b>M. Gong</b>, and L. Yao.<br>
In <a href="https://kdd2024.kdd.org/"> KDD</a>, 2024.
</p></li>
<li><p>
On the Recoverability of Causal Relations from Temporally Aggregated IID Data. [<A HREF="https://arxiv.org/pdf/2406.02191">PDF</A>]<br>
S. Fan, <b>M. Gong</b>, and K. Zhang.<br>
In <a href="https://icml.cc/Conferences/2024"> ICML</a>, 2024.
</p></li>
<li><p>
Optimal Kernel Choice for Score Function-based Causal Discovery. [<A HREF="https://arxiv.org/pdf/2407.10132">PDF</A>]<br>
W. Wang, B. Huang, F. Liu, X. You, T. Liu, K. Zhang, and <b>M. Gong</b>.<br>
In <a href="https://icml.cc/Conferences/2024"> ICML</a>, 2024.
</p></li>
<li><p>
Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition. [<A HREF="https://openaccess.thecvf.com/content/CVPR2024/papers/Zhu_Part-aware_Unified_Representation_of_Language_and_Skeleton_for_Zero-shot_Action_CVPR_2024_paper.pdf">PDF</A>]<br>
A. Zhu, Q. Ke, <b>M. Gong</b>, and J Bailey.<br>
In <a href="https://cvpr.thecvf.com/Conferences/2024"> CVPR</a>, 2024.
</p></li>
<li><p>
Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach. [<A HREF="https://openreview.net/pdf?id=SKulT2VX9p">PDF</A>]<br>
A. Zuo, Y. Li, S. Wei, and <b>M. Gong</b>.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2024.
</p></li>
<li><p>
A Variational Framework for Estimating Continuous Treatment Effects with Measurement Error. [<A HREF="https://openreview.net/pdf?id=S46Knicu56">PDF</A>]<br>
E. Gao, H. Bondell, W. Huang, and <b>M. Gong</b>.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2024.
</p></li>
<li><p>
Identifiable Latent Polynomial Causal Models through the Lens of Change. [<A HREF="https://openreview.net/pdf?id=ia9fKO1Vjq">PDF</A>]<br>
Y. Liu, Z. Zhang, D. Gong, <b>M. Gong</b>, B. Huang, A. Hengel, K. Zhang, and J. Shi.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2024.
</p></li>
<li><p>
Improving Non-Transferable Representation Learning by Harnessing Content and Style. [<A HREF="https://openreview.net/pdf?id=FYKVPOHCpE">PDF</A>]<br>
Z. Hong, Z. Wang, L. Shen, Y. Yao, Z. Huang, S. Chen, C. Yang, <b>M. Gong</b>, and T. Liu.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2024. (<font color="red">Spotlight</font>)
</p></li>
<li><p>
Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach. [<A HREF="">PDF</A>] <br>
W. Liu, B. Huang, E. Gao, Q. Ke, H. Bondell, and <b>M. Gong</b>.<br>
In <a href="https://cclear.cc/"> CLeaR</a>, 2024.
</p></li>
<li><p>
HuTuMotion: Human-Tuned Navigation of Latent Motion Diffusion Models with Minimal Feedback. [<A HREF="https://arxiv.org/pdf/2312.12227.pdf">PDF</A>]<br>
G. Han, S. Huang, <b>M. Gong</b>, and J. Tang.<br>
In <a href="https://aaai.org/aaai-conference/"> AAAI</a>, 2024.
</p></li>
<li><p>
Freetalker: Controllable Speech and Text-Driven Gesture Generation Based on Diffusion Models for Enhanced Speaker Naturalness. [<A HREF="https://arxiv.org/pdf/2401.03476.pdf">PDF</A>]<br>
S. Yang, Z. Xu, H. Xue, Y. Cheng, S. Huang, <b>M. Gong</b>, and Z. Wu.<br>
In <a href="https://2024.ieeeicassp.org/"> ICASSP</a>, 2024.
</p></li>
<li><p>
Generator Identification for Linear SDEs with Additive and Multiplicative Noise. [<A HREF="https://openreview.net/pdf?id=zsOOqjaj2z">PDF</A>]<br>
Y. Wang, X. Geng, W. Huang, B. Huang, and <b>M. Gong</b>.<br>
In <a href="https://neurips.cc/"> NeurIPS</a>, 2023.
</p></li>
<li><p>
Semi-Implicit Denoising Diffusion Models (SIDDMs). [<A HREF="https://openreview.net/pdf?id=gaktiSjatl">PDF</A>]<br>
Y. Xu, <b>M. Gong</b>, S. Xie, W. Wei, M. Grundmann, K. Batmanghelich*, and T. Hou*.<br>
In <a href="https://neurips.cc/"> NeurIPS</a>, 2023. <br>
<font color="red">High-quality image generation in a few diffusion steps, an extension to text-to-image generation is <a target="_blank" href="https://arxiv.org/abs/2311.09257">here</a>.</font>
</p></li>
<li><p>
Learning World Models with Identifiable Factorization. [<A HREF="https://arxiv.org/abs/2306.06561">PDF</A>]<br>
Y. Liu, B. Huang, Z. Zhu, H. Tian, <b>M. Gong</b>, Y. Yu, and K. Zhang.<br>
In <a href="https://neurips.cc/"> NeurIPS</a>, 2023.
</p></li>
<li><p>
CS-Isolate: Extracting Hard Confident Examples by Content and Style Isolation. [<A HREF="https://openreview.net/pdf?id=Lkc0KjsDFv">PDF</A>]<br>
Y. Lin, Y. Yao, X. Shi, <b>M. Gong</b>, X. Shen, D. Xu, and T. Liu.<br>
In <a href="https://neurips.cc/"> NeurIPS</a>, 2023.
</p></li>
<li><p>
ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding. [<A HREF="https://openreview.net/pdf?id=kKXJkiniOx">PDF</A>]<br>
L. Duan, S. Zhao, N. Xue, <b>M. Gong</b>, G. Xia, and D. Tao.<br>
In <a href="https://neurips.cc/"> NeurIPS</a>, 2023.
</p></li>
<li><p>
Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering. [<A HREF="https://arxiv.org/abs/2304.10075">PDF</A>]<br>
D. Hu, Z. Zhang, T. Hou, T. Liu, H. Fu*, and <b>M. Gong*</b>.<br>
In <a href="https://iccv2023.thecvf.com/"> ICCV</a>, 2023.
</p></li>
<li><p>
Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples. [<A >PDF</A>]<br>
X. Xia, B. Han, Y, Zhan, J. Yu, <b>M. Gong</b>, C. Gong, and T. Liu.<br>
In <a href="https://iccv2023.thecvf.com/"> ICCV</a>, 2023.
</p></li>
<li><p>
Generating Dynamic Kernels via Transformers for Lane Detection. [<A >PDF</A>]<br>
Z. Chen, Y. Liu, <b>M. Gong</b>, B. Du, G. Qian and K. Smith-Miles.<br>
In <a href="https://iccv2023.thecvf.com/"> ICCV</a>, 2023.
</p></li>
<li><p>
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. [<A HREF="https://openreview.net/forum?id=_apb5VI2_0o" >PDF</A>]<br>
R. Dong, F. Liu, H. Chi, T. Liu, <b>M. Gong</b>, G. Niu, M. Sugiyama, and B. Han.<br>
In <a href="https://icml.cc/Conferences/2023"> ICML</a>, 2023.
</p></li>
<li><p>
Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?. [<A HREF="https://openreview.net/forum?id=sDCMrYnXNGY" >PDF</A>]<br>
Y. Yao, <b>M. Gong</b>, Y. Du, J. Yu, B. Han, K. Zhang, and T. Liu.<br>
In <a href="https://icml.cc/Conferences/2023"> ICML</a>, 2023.
</p></li>
<li><p>
Unpaired Image-to-Image Translation with Shortest Path Regularization. [<A HREF="https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_Unpaired_Image-to-Image_Translation_With_Shortest_Path_Regularization_CVPR_2023_paper.pdf">PDF</A>]<br>
S. Xie, Y. Xu, <b>M. Gong</b>, and K. Zhang.<br>
In <a href="https://cvpr2023.thecvf.com/"> CVPR</a>, 2023.
</p></li>
<li><p>
Multi-Domain Image Generation and Translation with Identifiability Guarantees. [<A HREF="https://openreview.net/forum?id=U2g8OGONA_V">PDF</A>][<A HREF="https://github.com/Mid-Push/i-stylegan">CODE</A>]<br>
S. Xie, L. Kong, <b>M. Gong</b>, and K. Zhang.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2023. (<font color="red">Spotlight</font>)
</p></li>
<li><p>
Mosaic Representation Learning for Self-supervised Visual Pre-training. [<A HREF="https://openreview.net/forum?id=JAezPMehaUu">PDF</A>][<A HREF="https://github.com/DerrickWang005/MosRep">CODE</A>]<br>
Z. Wang, Z. Chen, Y. Li, Y. Guo, J. Yu, <b>M. Gong*</b>, and T. Liu*.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2023. (<font color="red">Spotlight</font>)
</p></li>
<li><p>
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style. [<A HREF="https://arxiv.org/abs/2207.03162">PDF</A>]<br>
Z. Huang, X. Xia, L. Shen, B. Han, <b>M. Gong</b>, C. Gong, and T. Liu.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2023.
</p></li>
<li><p>
Knowledge Distillation for Feature Extraction in Underwater VSLAM. [<A HREF="https://arxiv.org/abs/2303.17981">PDF</A>] <br>
J. Yang, <b>M. Gong.</b>, G. Nair, J.H. Lee, J. Monty, and Y. Pu.<br>
In <a href="https://www.icra2023.org/"> ICRA</a>, 2023.
</p></li>
<li><p>
Progressive Video Summarization via Multimodal Self-supervised Learning. [<A HREF="https://openaccess.thecvf.com/content/WACV2023/papers/Li_Progressive_Video_Summarization_via_Multimodal_Self-Supervised_Learning_WACV_2023_paper.pdf">PDF</A>]<br>
H. Li, Q. Ke, <b>M. Gong</b>, and T. Drummond.<br>
In <a href="https://wacv2023.thecvf.com/"> WACV</a>, 2023.
</p></li>
<li><p>
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition. [<A HREF="https://openaccess.thecvf.com/content/WACV2023/papers/Zhu_Adaptive_Local-Component-Aware_Graph_Convolutional_Network_for_One-Shot_Skeleton-Based_Action_Recognition_WACV_2023_paper.pdf">PDF</A>]<br>
A. Zhu, Q. Ke, <b>M. Gong</b>, and J. Bailey.<br>
In <a href="https://wacv2023.thecvf.com/"> WACV</a>, 2023.
</p></li>
<li><p>
Counterfactual Fairness with Partially Known Causal Graph. [<A HREF="https://arxiv.org/pdf/2205.13972">PDF</A>][<A HREF="https://github.com/aoqiz/Counterfactual-Fairness-with-Partially-Known-Causal-Graph">CODE</A>]<br>
A. Zuo, S. Wei, T. Liu, B. Han, K. Zhang, and <b>M. Gong</b>.<br>
In <a href="https://neurips.cc/"> NeurIPS</a>, 2022.
</p></li>
<li><p>
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. [<A HREF="https://arxiv.org/pdf/2205.13869.pdf">PDF</A>][<A HREF="https://github.com/ErdunGAO/MissDAG">CODE</A>]<br>
E. Gao*, I. Ng*, <b>M. Gong</b>, L. Shen, W. Huang, T. Liu, K. Zhang, and H. Bondell.<br>
In <a href="https://neurips.cc/"> NeurIPS</a>, 2022.
</p></li>
<li><p>
Truncated Matrix Power Iteration for Differentiable DAG Learning. [<A HREF="https://arxiv.org/pdf/2208.14571">PDF</A>]<br>
Z. Zhang, I. Ng, D. Gong, Y. Liu, E.M. Abbasnejad, <b>M. Gong</b>, K. Zhang, and J.Q. Shi.<br>
In <a href="https://neurips.cc/"> NeurIPS</a>, 2022.
</p></li>
<li><p>
Uncertainty Quantification in Depth Estimation via Constrained Ordinal Regression. [<A HREF="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136620229.pdf" >PDF</A>][<A HREF="https://github.com/timmy11hu/ConOR" >CODE</A>] <br>
D. Hu, L. Peng, T. Chu, X. Zhang, Y. Mao, H. Bondell, and <b>M. Gong</b>.<br>
In <a href="https://eccv2022.ecva.net/"> ECCV</a>, 2022.
</p></li>
<li><p>
Digging into Radiance Grid for Real-Time View Synthesis with Detail Preservation. [<A HREF="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136750722.pdf" >PDF</A>] <br>
J. Zhang*, J. Huang*, B. Cai*, <b>M. Gong</b>, C. Wang, B. Cai, J. Wang, H. Luo, R. Jia, B. Zhao, X. Tang, and H. Fu.<br>
In <a href="https://eccv2022.ecva.net/"> ECCV</a>, 2022.
</p></li>
<li><p>
Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation. [<A HREF="https://arxiv.org/pdf/2206.13737.pdf" >PDF</A>] <br>
Y. Xu, S. Xie, M. Reynolds, M. Ragoza, <b>M. Gong*</b>, and K. Batmanghelich*.<br>
In <a href="https://conferences.miccai.org/2022/en/"> MICCAI</a>, 2022.
</p></li>
<li><p>
Understanding Robust Overfitting of Adversarial Training and Beyond. [<A HREF="https://arxiv.org/abs/2206.08675" >PDF</A>][<A HREF="https://github.com/ChaojianYu/Understanding-Robust-Overfitting">CODE</A>] <br>
C. Yu, B. Han, L. Shen, J. Yu, C. Gong, <b>M. Gong</b>, and T. Liu.<br>
In <a href="https://icml.cc/Conferences/2022"> ICML</a>, 2022.
</p></li>
<li><p>
Sample-Efficient Kernel Mean Estimator with Marginalized Corrupted Data. [<A HREF="https://arxiv.org/pdf/2107.04855.pdf" >PDF</A>] <br>
X. Xia*, S. Shan*, <b>M. Gong</b>, N. Wang, F. Gao, H. Wei, and T. Liu.<br>
In <a href="https://kdd.org/kdd2022"> KDD</a>, 2022.
</p></li>
<li><p>
Robust Weight Perturbation for Adversarial Training. [<A href="https://arxiv.org/abs/2205.14826">PDF</A>] [<A HREF="https://github.com/ChaojianYu/Robust-Weight-Perturbation">CODE</A>] <br>
C. Yu, B. Han, <b>M. Gong</b>, L. Shen, S. Ge, B. Du, and T. Liu.<br>
In <a href="https://ijcai-22.org/"> IJCAI</a>, 2022.
</p></li>
<li><p>
MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning. [<A HREF="https://arxiv.org/pdf/2204.08326.pdf" >PDF</A>] <br>
M. Wang, Y. Guo, Z. Zhao, G. Hu, Y. Shen, <b>M. Gong</b>, and P. Torr.<br>
In <a href="https://sigir.org/sigir2022/"> SIGIR</a>, 2022.
</p></li>
<li><p>
Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint. [<A href="https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_Alleviating_Semantics_Distortion_in_Unsupervised_Low-Level_Image-to-Image_Translation_via_Structure_CVPR_2022_paper.pdf">PDF</A>][<A href="https://github.com/CR-Gjx/SCC">CODE</A>] <br>
J. Guo, J. Li, H. Fu, <b>M. Gong</b>, K. Zhang, and D. Tao.<br>
In <a href="http://cvpr2022.thecvf.com/"> CVPR</a>, 2022.
</p></li>
<li><p>
Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation. [<A href="https://arxiv.org/abs/2203.12707">PDF</A>][<A href="https://github.com/batmanlab/MSPC">CODE</A>] <br>
Y. Xu, S. Xie, W. Wu, K. Zhang, <b>M. Gong*</b>, and K. Batmanghelich*.<br>
In <a href="http://cvpr2022.thecvf.com/"> CVPR</a>, 2022.
</p></li>
<li><p>
Few-Shot Font Generation by Learning Fine-Grained Local Styles. [<A href="https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Few-Shot_Font_Generation_by_Learning_Fine-Grained_Local_Styles_CVPR_2022_paper.pdf" >PDF</A>] <br>
L. Tang, Y. Cai, J. Liu, Z. Hong, <b>M. Gong</b>, M. Fan, J. Han, J. L, E. Ding, and J. Wang.<br>
In <a href="http://cvpr2022.thecvf.com/"> CVPR</a>, 2022.
</p></li>
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CRIS: CLIP-Driven Referring Image Segmentation. [<A href="https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_CRIS_CLIP-Driven_Referring_Image_Segmentation_CVPR_2022_paper.pdf" >PDF</A>][<A href="https://github.com/DerrickWang005/CRIS.pytorch">CODE</A>] <br>
Z. Wang, Y. Lu, Q. Li, X. Tao, Y. Guo, <b>M. Gong</b>, and T. Liu.<br>
In <a href="http://cvpr2022.thecvf.com/"> CVPR</a>, 2022.
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<li><p>
Exploring Set Similarity for Dense Self-supervised Representation Learning. [<A href="https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Exploring_Set_Similarity_for_Dense_Self-Supervised_Representation_Learning_CVPR_2022_paper.pdf">PDF</A>] <br>
Z. Wang, Q. Li, G. Zhang, P. Wan, W. Zheng, N. Wang, <b>M. Gong</b>, and T. Liu.<br>
In <a href="http://cvpr2022.thecvf.com/"> CVPR</a>, 2022.
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<li><p>
Fair Classification with Instance-dependent Label Noise. [<A HREF="https://openreview.net/forum?id=s-pcpETLpY">PDF</A>] <br>
S. Wu, <b>M. Gong</b>, B. Han, Y. Liu, and T. Liu.<br>
In <a href="https://cclear.cc/"> CLeaR</a>, 2022.
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<li><p>
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning. [<A HREF="https://openreview.net/forum?id=YRq0ZUnzKoZ">PDF</A>][<A href="https://github.com/CR-Gjx/RIA">CODE</A>] <br>
J. Guo, <b>M. Gong</b>, and D. Tao.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2022.
</p></li>
<li><p>
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. [<A HREF="https://openreview.net/pdf?id=aYAA-XHKyk">PDF</A>] <br>
Y. Yao, T. Liu, B. Han, <b>M. Gong</b>, G. Niu, M. Sugiyama, and D. Tao.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2022.
</p></li>
<li><p>
CausalAdv: Adversarial Robustness Through the Lens of Causality. [<A HREF="https://openreview.net/forum?id=cZAi1yWpiXQ">PDF</A>][<A href="https://github.com/YonggangZhangUSTC/CausalAdv">CODE</A>] <br>
Y. Zhang, <b>M. Gong</b>, T. Liu, G. Niu, X. Tian, B. Han, B. Schölkopf, and K. Zhang.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2022.
</p></li>
<li><p>
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. [<A HREF="https://openreview.net/pdf?id=xENf4QUL4LW">PDF</A>]<br>
X. Xia, T. Liu, B. Han, <b>M. Gong</b>, J. Yu, G. Niu, and M. Sugiyama.<br>
In <a href="https://iclr.cc/"> ICLR</a>, 2022.
</p></li>
<li><p>
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?. [<A HREF="https://openreview.net/forum?id=zdmF437BCB">PDF</A>][<a href="https://github.com/DMIRLAB-Group/DSAN">CODE</a>]<br>
P. Stojanov, Z. Li, <b>M. Gong</b>, R. Cai, J.G. Carbonell, and K. Zhang.<br>
In <a href="https://nips.cc/"> NeurIPS</a>, 2021.
</p></li>
<li><p>
Instance-dependent Label-noise Learning under a Structural Causal Model. [<A HREF="https://arxiv.org/abs/2109.02986">PDF</A>][<a href="https://github.com/a5507203/IDLN">CODE</a>]<br>
Y. Yao, T. Liu, <b>M. Gong</b>, B. Han, G. Niu, and K. Zhang.<br>
In <a href="https://nips.cc/"> NeurIPS</a>, 2021.
</p></li>
<li><p>
Unaligned Image-to-Image Translation by Learning to Reweight. [<A HREF="https://arxiv.org/abs/2109.11736">PDF</A>][<a href="https://github.com/Mid-Push/IrwGAN">CODE</a>]<br>
S. Xie, <b>M. Gong</b>, Y. Xu, and K. Zhang.<br>
In <a href="http://iccv2021.thecvf.com/"> ICCV</a>, 2021.
</p></li>
<li><p>
Not All Operations Contribute Equally: Hierarchical Operation-adaptive Predictor for Neural Architecture Search. [<A HREF="https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Not_All_Operations_Contribute_Equally_Hierarchical_Operation-Adaptive_Predictor_for_Neural_ICCV_2021_paper.pdf">PDF</A>]<br>
Z. Chen, Y. Zhan, B. Yu, <b>M. Gong*</b>, and B. Du*.<br>
In <a href="http://iccv2021.thecvf.com/"> ICCV</a>, 2021.
</p></li>
<li><p>
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels [<A HREF="http://proceedings.mlr.press/v139/wu21f/wu21f.pdf">PDF</A>] [<A HREF="https://github.com/scifancier/Class2Simi">CODE</A>]<br>
S. Wu*, X. Xia*, T. Liu, B. Han, <b>M. Gong</b>, N. Wang, H. Liu, and G. Niu. <br>
In <a href="https://icml.cc/Conferences/2021"> ICML</a>, 2021.
</p></li>
<li><p>
Learning with Group Noise. [<A HREF="https://arxiv.org/abs/2103.09468">PDF</A>][<A HREF="https://github.com/QizhouWang/Max-Matching">CODE</A>]<br>
Q. Wang, J. Yao, C. Gong, T. Liu, <b>M. Gong</b>, H. Yang, and B. Han.<br>
In <a href="https://aaai.org/Conferences/AAAI-21/"> AAAI</a>, 2021.
</p></li>
<li><p>
VecNet: A Spectral and Multi-scale Spatial Fusion Deep Network for Pixel-level Cloud Type Classification in Himawari-8 Imagery. [<A HREF="https://ieeexplore.ieee.org/document/9554737">PDF</A>] <br>
Z. Wang, X. Kong, Z. Cui, M. Wu, C. Zhang, <b>M. Gong</b>, and T. Liu <br>
In <a href="https://igarss2021.com/"> IGARSS</a>, 2021.
</p></li>
<li><p>
Domain Adaptation As a Problem of Inference on Graphical Models. [<A HREF="https://arxiv.org/abs/2002.03278">PDF</A>][<A HREF="https://github.com/mgong2/DA_Infer">CODE</A>]<br>
K. Zhang*, <b>M. Gong*</b>, P. Stojanov, B. Huang, Qingsong Liu, and C. Glymour.<br>
In <a href="https://nips.cc/">NeurIPS</a></i>, 2020.
</p></li>
<li><p>
Domain Generalization via Entropy Regularization. [<A HREF="https://papers.nips.cc/paper/2020/file/b98249b38337c5088bbc660d8f872d6a-Paper.pdf">PDF</A>][<A HREF="https://github.com/sshan-zhao/DG_via_ER">CODE</A>]<br>
S. Zhao, <b>M. Gong</b>, T. Liu, H. Fu, and D. Tao.<br>
In <a href="https://nips.cc/"> NeurIPS</a>, 2020.
</p></li>
<li><p>
Parts-dependent Label Noise: Towards Instance-dependent Label Noise. [<A HREF="https://arxiv.org/abs/2006.07836">PDF</A>][<A HREF="https://github.com/xiaoboxia/Part-dependent-label-noise">CODE</A>]<br>
X. Xia, T. Liu, B. Han, N. Wang, <b>M. Gong</b>, H. Liu, G. Niu, D. Tao, and M. Sugiyama.<br>
In <a href="https://nips.cc/"> NeurIPS</a>, 2020. (<font color="red">Spotlight</font>)
</p></li>
<li><p>
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. [<A HREF="https://arxiv.org/abs/2006.07805">PDF</A>][<A HREF="https://github.com/a5507203/dual-T-Estimator">CODE</A>]<br>
Y. Yao, T. Liu, B. Han, <b>M. Gong</b>, J. Deng, G. Niu, and M. Sugiyama.<br>
In <a href="https://nips.cc/"> NeurIPS</a>, 2020.
</p></li>
<li><p>
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning. [<A HREF="https://proceedings.neurips.cc/paper/2020/hash/a87d27f712df362cd22c7a8ef823e987-Abstract.html">PDF</A>]<br>
H. Fu*, S. Li*, R. Jia, <b>M. Gong</b>, B. Zhao, and D. Tao.<br>
In <a href="https://nips.cc/"> NeurIPS</a>, 2020.
</p></li>
<li><p>
Short-Term and Long-Term Context Aggregation Network for Video Inpainting. [<A HREF="https://arxiv.org/abs/2009.05721">PDF</A>] <br>
A. Li, S. Zhao, X. Ma, <b>M. Gong</b>, J. Qi, R. Zhang, D. Tao, and R. Kotagiri.<br>
In <a href="https://eccv2020.eu/"> ECCV</a>, 2020. (<font color="red">Spotlight</font>)
</p></li>
<li><p>
Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces. [<A HREF="https://ibug.doc.ic.ac.uk/media/uploads/documents/eccv_1445.pdf">PDF</A>][<A HREF="https://github.com/deepinsight/insightface/tree/master/recognition/subcenter_arcface">CODE</A>]<br>
J. Deng, J. Guo, T. Liu, <b>M. Gong</b>, and S. Zafeiriou.<br>
In <a href="https://eccv2020.eu/"> ECCV</a>, 2020.
</p></li>
<li><p>
Label-Noise Robust Domain Adaptation. [<A HREF="https://proceedings.icml.cc/static/paper_files/icml/2020/1942-Paper.pdf">PDF</A>]<br>
X. Yu, T. Liu, <b>M. Gong</b>, K. Zhang, K. Batmanghelich, and D. Tao.<br>
In <a href="https://icml.cc/"> ICML</a>, 2020.
</p></li>
<li><p>
LTF: A Label Transformation Framework for Correcting Target Shift. [<A HREF="https://proceedings.icml.cc/static/paper_files/icml/2020/1262-Paper.pdf">PDF</A>][<A HREF="https://github.com/CR-Gjx/LTF-Label-Transformation-Framework">CODE</A>] <br>
J. Guo, <b>M. Gong</b>, T. Liu, K. Zhang, and D. Tao.<br>
In <a href="https://icml.cc/"> ICML</a>, 2020.
</p></li>
<li><p>
Compressed Self-Attention for Deep Metric Learning with Low-Rank Approximation. [<A HREF="https://www.ijcai.org/proceedings/2020/0285.pdf">PDF</A>]<br>
Z. Chen, <b>M. Gong*</b>, L. Ge, B. Du*.<br>
In <a href="https://ijcai20.org/"> IJCAI</a>, 2020.
</p></li>
<li><p>
Causal Discovery from Non-Identical Variable Sets. [<A HREF="https://www.aaai.org/Papers/AAAI/2020GB/AAAI-HuangB.6175.pdf">PDF</A>]<br>
B. Huang, K. Zhang, <b>M. Gong</b>, and C. Glymour.<br>
In <a href="http://www.aaai.org/Conferences/AAAI/aaai20.php"> AAAI</a>, 2020.
</p></li>
<li><p>
Generative-Discriminative Complementary Learning. [<A HREF="https://arxiv.org/abs/1904.01612">PDF</A>][<A HREF="https://github.com/xuyanwu/Complementary-GAN">CODE</A>]<br>
Y. Xu*, <b>M. Gong*</b>, J. Chen, T. Liu, K. Zhang, and K. Batmanghelich.<br>
In <a href="http://www.aaai.org/Conferences/AAAI/aaai20.php"> AAAI</a>, 2020.
</p></li>
<li><p>
Compressed Self-Attention for Deep Metric Learning. [<A HREF="https://aaai.org/Papers/AAAI/2020GB/AAAI-ChenZ.5756.pdf">PDF</A>]<br>
Z. Chen, <b>M. Gong</b>, Y. Xu, C. Wang, K. Zhang, and B. Du.<br>
In <a href="http://www.aaai.org/Conferences/AAAI/aaai20.php"> AAAI</a>, 2020.
</p></li>
<li><p>
Twin Auxiliary Classifiers GAN. [<A HREF="https://arxiv.org/abs/1907.02690">PDF</A>][<A HREF="https://github.com/batmanlab/twin_ac">CODE</A>]<br>
<b>M. Gong*</b>, Y. Xu*, C. Li, K. Zhang, and K. Batmanghelich.<br>
In <a href="https://nips.cc/Conferences/2019">NeurIPS</a></i>, 2019. (<font color="red">Spotlight, acceptance rate 2.4%</font>)
</p></li>
<li><p>
Likelihood-Free Overcomplete ICA and Applications in Causal Discovery. [<A HREF="https://papers.nips.cc/paper/8912-likelihood-free-overcomplete-ica-and-applications-in-causal-discovery.pdf">PDF</A>][<A HREF="https://github.com/dingchenwei/Likelihood-free_OICA">CODE</A>] <br>
C. Ding, <b>M. Gong</b>, K. Zhang, and D. Tao.<br>
In <a href="https://nips.cc/Conferences/2019">NeurIPS</a></i>, 2019. (<font color="red">Spotlight, acceptance rate 2.4%</font>)
</p></li>
<li><p>
Specific and Shared Causal Relation Modeling and Mechanism-based Clustering. [<A HREF="https://papers.nips.cc/paper/9506-specific-and-shared-causal-relation-modeling-and-mechanism-based-clustering.pdf">PDF</A>]<br>
B. Huang, K. Zhang, P. Xie, <b>M. Gong</b>, E. P. Xing, and C. Glymour.<br>
In <a href="https://nips.cc/Conferences/2019">NeurIPS</a></i>, 2019.
</p></li>
<li><p>
Discovery and Forecasting in Nonstationary Environments with State-Space Models. [<A HREF="http://proceedings.mlr.press/v97/huang19g/huang19g.pdf">PDF</A>][<A HREF="http://proceedings.mlr.press/v97/huang19g/huang19g-supp.pdf">SUPP</A>][<A HREF="https://github.com/Biwei-Huang/Causal-discovery-and-forecasting-in-nonstationary-environments">CODE</A>]<br>
B. Huang, K. Zhang, <b>M. Gong</b>, and C. Glymour.<br>
In <a href="https://icml.cc/Conferences/2019">ICML</a></i>, 2019.
</p></li>
<li><p>
Geometry-Consistent Adversarial Networks for Unsupervised Domain Mapping. [<A HREF="https://arxiv.org/abs/1809.05852">PDF</A>][<A HREF="https://github.com/hufu6371/GcGAN">CODE</A>]<br>
H. Fu*, <b>M. Gong*</b>, C. Wang, K. Batmanghelich, K. Zhang, and D. Tao.<br>
In <a href="http://cvpr2019.thecvf.com/">CVPR</a></i>, 2019. (<font color="red">best paper finalist, top 1%</font>)<br>
</p></li>
<li><p>
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation. [<A HREF="https://arxiv.org/pdf/1904.01870">PDF</A>][<A HREF="https://github.com/sshan-zhao/GASDA">CODE</A>]<br>
S. Zhao, H. Fu, <b>M. Gong</b>, and D. Tao.<br>
In <a href="http://cvpr2019.thecvf.com/">CVPR</a></i>, 2019. <br>
</p></li>
<li><p>
Low-Dimensional Density Ratio Estimation for Covariate Shift Correction. [<A HREF="http://proceedings.mlr.press/v89/stojanov19a/stojanov19a.pdf">PDF</A>]<br>
P. Stojanov, <b>M. Gong</b>, J. G. Carbonell, and K. Zhang.<br>
In <a href="https://www.aistats.org/aistats2019/">AISTATS</a></i>, 2019. <br>
</p></li>
<li><p>
Data-Driven Approach to Multiple-Source Domain Adaptation. [<A HREF="http://proceedings.mlr.press/v89/stojanov19b/stojanov19b.pdf">PDF</A>]<br>
P. Stojanov, <b>M. Gong</b>, J. G. Carbonell, and K. Zhang.<br>
In <a href="https://www.aistats.org/aistats2019/">AISTATS</a></i>, 2019. <br>
</p></li>
<li><p>
Modeling Dynamic Missingness of Implicit Feedback for Recommendation. [<A HREF="https://papers.nips.cc/paper/7901-modeling-dynamic-missingness-of-implicit-feedback-for-recommendation.pdf">PDF</A>] <br>
M. Wang, <b>M. Gong</b>, X. Zheng, and K. Zhang.<br>
In <a href="https://nips.cc/Conferences/2018">NeurIPS</a></i>, 2018.
</p></li>
<li><p>
Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. [<A HREF="http://auai.org/uai2018/proceedings/papers/372.pdf">PDF</A>]<br>
K. Zhang, <b>M. Gong</b>, J. Ramsey, K. Batmanghelich, P. Spirtes, and C. Glymour.<br>
In <a href="http://auai.org/uai2018/">UAI</a></i>, 2018. (<font color="red">Oral, acceptance rate 8.9%</font>)
</p></li>
<li><p>
Generative-Discriminative Approach from a Bag of Image Patches to a Vector. [<A HREF="https://arxiv.org/pdf/1806.11217.pdf">PDF</A>]<br>
S. Singla, <b>M. Gong</b>, S. Ravanbakhsh, B. Poczos, and K. Batmanghelich.<br>
In <a href="https://link.springer.com/book/10.1007/978-3-030-00928-1"> MICCAI</a>, 2018.
</p></li>
<li><p>
Learning with Biased Complementary Labels. [<A HREF="http://openaccess.thecvf.com/content_ECCV_2018/papers/Xiyu_Yu_Learning_with_Biased_ECCV_2018_paper.pdf">PDF</A>]<br>
X. Yu, T. Liu, <b>M. Gong</b>, and D. Tao. <br>
In <a href="https://eccv2018.org/"> ECCV</a>, 2018. (<font color="red">Oral, acceptance rate 2.4%</font>)
</p></li>
<li><p>
Correcting the Triplet Selection Bias for Triplet Loss. [<A HREF="http://openaccess.thecvf.com/content_ECCV_2018/papers/Baosheng_Yu_Correcting_the_Triplet_ECCV_2018_paper.pdf">PDF</A>][<A HREF="https://tongliang-liu.github.io/code/Xiyu_eccv2018_backup.tar.gz">CODE</A>]<br>
B. Yu, T. Liu, <b>M. Gong</b>, C. Ding, and D. Tao. <br>
In <a href="https://eccv2018.org/"> ECCV</a>, 2018.
</p></li>
<li><p>
Deep Domain Generalization via Conditional Invariant Adversarial Networks. [<A HREF="http://openaccess.thecvf.com/content_ECCV_2018/papers/Ya_Li_Deep_Domain_Generalization_ECCV_2018_paper.pdf">PDF</A>][<A HREF="http://staff.ustc.edu.cn/~xinmei/publications_pdf/2018/code-YaLi.zip">CODE</A>] <br>
Y. Li, X. Tian, <b>M. Gong</b>, Y. Liu, T. Liu, K. Zhang, and D. Tao. <br>
In <a href="https://eccv2018.org/"> ECCV</a>, 2018.
</p></li>
<li><p>
Deep Ordinal Regression Network for Monocular Depth Estimation. [<A HREF="https://hal.archives-ouvertes.fr/hal-01741163/file/CVPR18_DepthEstimation.pdf">PDF</A>][<A HREF="https://github.com/hufu6371/DORN">CODE</A>]<br>
H. Fu, <b>M. Gong</b>, C. Wang, K. Batmanghelich, and D. Tao. <br>
In <a href="http://cvpr2018.thecvf.com/">CVPR</a></i>, 2018.<br>
<font color="red">This algorithm won the 1st prize in single image depth prediction competition, <a target="_blank" href="http://www.robustvision.net/leaderboard.php?benchmark=depth">Robust Vision Challenge 2018</a>.</font>
</p></li>
<li><p>
Robust Angular Local Descriptor Learning. [<A HREF="https://arxiv.org/abs/1901.07076">PDF</A>][<A HREF="https://github.com/xuyanwu/RAL-Net">CODE</A>]<br>
Y. Xu, <b>M. Gong</b>, T. Liu, K. Batmanghelich, and C. Wang.<br>
In <a href="http://accv2018.net/"> ACCV</a>, 2018.
</p></li>
<li><p>
An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption. [<A HREF="http://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_An_Efficient_and_CVPR_2018_paper.pdf">PDF</A>][<A HREF="https://tongliang-liu.github.io/code/cvpr18_backup.zip">CODE</A>]<br>
X. Yu, T. Liu, <b>M. Gong</b>, K. Batmanghelich, and D. Tao. <br>
In <a href="http://cvpr2018.thecvf.com/">CVPR</a></i>, 2018.
</p></li>
<li><p>
Domain Generalization via Conditional Invariant Representations. [<A HREF="https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16595/16558">PDF</A>][<A HREF="papers/CIDG.zip">CODE</A>]<br>
Y. Li, <b> M. Gong</b>, X. Tian, T. Liu, and D. Tao. <br>
In <a href="http://www.aaai.org/Conferences/AAAI/aaai18.php"> AAAI</a>, 2018 (<font color="red">Oral, acceptance rate 11.0%</font>)
</p></li>
<li><p>
Causal Discovery from Temporally Aggregated Time Series. [<A HREF="papers/UAI_CDTA.pdf">PDF</A>]<br>
<b>M. Gong</b>, K. Zhang, B. Schölkopf, C. Glymour, and D. Tao.<br>
In <a href="http://auai.org/uai2017/">UAI</a></i>, 2017.
</p></li>
<li><p>
A Coarse-Fine Network for Keypoint Localization. [<A HREF="http://openaccess.thecvf.com/content_ICCV_2017/papers/Huang_A_Coarse-Fine_Network_ICCV_2017_paper.pdf">PDF</A>]<br>
S. Huang, <b>M. Gong</b>, and D. Tao.<br>
In <a href="http://iccv2017.thecvf.com/">ICCV</a></i>, 2017. (<font color="red">Spotlight, acceptance rate 2.6%</font>)
</p></li>
<li><p>
Domain Adaptation with Conditional Transferable Components. [<A HREF="papers/ICML_CTC.pdf">PDF</A>][<A HREF="papers/CTC.zip">CODE</A>]<br>
<b>M. Gong</b>, K. Zhang, T. Liu, D. Tao, C. Glymour, and B. Schölkopf.<br>
In <a href="https://icml.cc/2016/index.html">ICML</a></i>, 2016.
</p></li>
<li><p>
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components. [<A HREF="https://arxiv.org/pdf/1411.3972.pdf">PDF</A>][<A HREF="http://pgeiger.org/dl/software/CLH.zip">CODE</A>]<br>
P. Geiger, K. Zhang, <b> M. Gong</b>, B. Schölkopf, and D. Janzing.<br>
In <a href="https://icml.cc/2015/index.html">ICML</a></i>, 2015.
</p></li>
<li><p>
Discovering Temporal Causal Relations from Subsampled Data. [<A HREF="papers/ICML_SUBSAMPLE.pdf">PDF</A>][<A HREF="papers/CRSD.zip">CODE</A>] <br>
<b>M. Gong</b>*, K. Zhang*, B. Schölkopf, D. Tao, and P. Geiger.<br>
In <a href="https://icml.cc/2015/index.html">ICML</a></i>, 2015.
</p></li>
<li><p>
Multi-Source Domain Adaptation: A Causal View. [<A HREF="papers/AAAI_MULTI.pdf">PDF</A>][<A HREF="papers/MDAC.zip">CODE</A>]<br>
K. Zhang, <b>M. Gong</b>, and B. Schölkopf.<br>
In <a href="https://www.aaai.org/Press/Proceedings/aaai15.php">AAAI</a></i>, 2015.
</p></li>
</ul>
<h2><hr><a name="cd"></a> Journal</h2>
<ul>
<li><p>
Convex–Concave Tensor Robust Principal Component Analysis. [<A HREF="https://link.springer.com/article/10.1007/s11263-023-01960-1">PDF</A>]<br>
Y. Liu, B. Du, Y. CHen, L. Zhang, <b>M. Gong</b>, and D. Tao.<br>
<i><a target="_blank" href="https://www.springer.com/journal/11263">IJCV,</a></i> (2023).
</p></li>
<li><p>
Deep Corner. [<A HREF="https://link.springer.com/article/10.1007/s11263-023-01837-3">PDF</A>]<br>
S. Zhao, <b>M. Gong</b>, H. Zhao, J. Zhang and D. Tao.<br>
<i><a target="_blank" href="https://www.springer.com/journal/11263">IJCV,</a></i> (2023).
</p></li>
<li><p>
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FedDAG: Federated DAG Structure Learning. [<A HREF="https://openreview.net/pdf?id=MzWgBjZ6Le">PDF</A>][<A HREF="https://github.com/ErdunGAO/FedDAG">CODE</A>]<br>
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