Hi, this is Yatao (). I received my Doctor of Sciences degree from the Institute for Machine Learning at ETH Zürich Computer Science. I was lucky to be supervised by Prof. Joachim M. Buhmann and also worked closely with Prof. Andreas Krause (PhD co-examiner). Before that I obtained both of my M.Sc.Eng. and B.Sc.Eng. from Shanghai Jiao Tong University.

I am committed to advancing scientific intelligence through the development of principled and reliable algorithms in graph representation learning. By addressing practical challenges such as molecular modelling, my work lies at the intersection of artificial intelligence and scientific discovery. Some of my research topics include: learning molecular graphs under label scarcity, large graph language models, interpretable subgraph based robust graph learning, and neural sets. I like various sports, e.g., swimming, hiking & skiing.

Contact:          Twitter@yataobian
Research:      Google Scholar     ORCID

News

Academic Activities

Tutorials

• Co-tutoring a tutorial on Trustworthy Graph Learning at KDD 2022: Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection.
• Co-tutoring a tutorial on Advanced Deep Graph Learning at WWW 2021: Advanced Deep Graph Learning: Deeper, Faster, Robuster, and Unsupervised.

Conference Reviewing/PC

• Area Chair of ICLR
• NeurIPS 2024, ICML 2024, NeurIPS 2023, ICLR 2023, NeurIPS 2022, ICML 2022, ICLR 2022, CVPR 2022, AAAI 2022, AISTATS 2022, NeurIPS 2021, ICCV 2021, CVPR 2021, AAAI 2021, NeurIPS 2020, AAAI 2020, NeurIPS 2019, ICML 2019, AISTATS 2019, STOC 2018, ITCS 2017, NIPS 2016

Journal Reviewing

• JMLR, T-PAMI

Selected Papers & Code

• Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning
  ICLR 2022. Bian, Y. Rong, T. Xu, J. Wu, A. Krause, J. Huang [pdf] [project page] [video] [poster] [slides] [bib]

      @inproceedings{bian2022energybased,
      title={Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning},
      author={Yatao Bian and Yu Rong and Tingyang Xu and Jiaxiang Wu and Andreas Krause and Junzhou Huang},
      booktitle={International Conference on Learning Representations},
      year={2022},
      url={https://openreview.net/forum?id=xLfAgCroImw}
      }
    

• Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking
  ICLR 2022 Spotlight. O. Ganea, X. Huang, C. Bunne, Bian, R. Barzilay, T. Jaakkola, A. Krause [pdf] [code] [bib]

      @inproceedings{ganea2022independent,
      title={Independent {SE}(3)-Equivariant Models for End-to-End Rigid Protein Docking},
      author={Octavian-Eugen Ganea and Xinyuan Huang and Charlotte Bunne and Yatao Bian and Regina Barzilay and Tommi S. Jaakkola and Andreas Krause},
      booktitle={International Conference on Learning Representations},
      year={2022},
      url={https://openreview.net/forum?id=GQjaI9mLet}
      }
    

• DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery--A Focus on Affinity Prediction Problems with Noise Annotations
  Preprint 2022. Ji*, Zhang*, Wu, Wu, Huang, Xu, Rong, Li, Ren, Xue, Lai, Xu, Feng, Liu, Luo, Zhou, Huang, Zhao, Bian [pdf] [project page] [bib]

        @ARTICLE{2022arXiv220109637J,
         author = {{Ji}, Yuanfeng and {Zhang}, Lu and {Wu}, Jiaxiang and {Wu}, Bingzhe and {Huang}, Long-Kai and {Xu}, Tingyang and {Rong}, Yu and {Li}, Lanqing and {Ren}, Jie and {Xue}, Ding and {Lai}, Houtim and {Xu}, Shaoyong and {Feng}, Jing and {Liu}, Wei and {Luo}, Ping and {Zhou}, Shuigeng and {Huang}, Junzhou and {Zhao}, Peilin and {Bian}, Yatao},
          title = "{DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations}",
        journal = {arXiv e-prints},
       keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Quantitative Biology - Quantitative Methods},
           year = 2022,
          month = jan,
            eid = {arXiv:2201.09637},
          pages = {arXiv:2201.09637},
  archivePrefix = {arXiv},
         eprint = {2201.09637},
   primaryClass = {cs.LG},
         adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220109637J},
        adsnote = {Provided by the SAO/NASA Astrophysics Data System}
            }
      

• On Self-Distilling Graph Neural Network
  IJCAI 2021. Chen, Bian, Xiao, Rong, Xu, J. Huang [pdf]
• Graph Information Bottleneck for Subgraph Recognition
  ICLR 2021. Yu, Xu, Rong, Bian, J. Huang, Ran [pdf]
• Self-Supervised Graph Transformer on Large-Scale Molecular Data
  NeurIPS 2020. Rong*, Bian*, Xu, Xie, Wei, W. Huang, J. Huang [pdf] [code] [video] [bib]

• From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models
  ICML 2020. Sahin, Bian, Buhmann, Krause [pdf] [bib]

        @inproceedings{sahin2020sets,
          title={From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models},
          author={Sahin, Aytunc and Bian, Yatao and Buhmann, Joachim M and Krause, Andreas},
          booktitle={Proceedings of the 37th International Conference on Machine Learning},
          year={2020},
          publisher={PMLR},
        }
      

• Provable Non-Convex Optimization and Algorithm Validation via Submodularity
  Doctoral thesis, ETH Zurich. Bian [pdf] [slides] [bib]

      @phdthesis{bian2019provable,
        title={Provable Non-Convex Optimization and Algorithm Validation via Submodularity},
        author={Bian, Yatao An},
        year={2019},
        school={ETH Zurich}
      }
    

• Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference
  ICML 2019. Bian, Buhmann, Krause [pdf] [code] [poster] [bib]

      @inproceedings{bian2019optimalmeanfield,
        title={Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference},
        author={Bian, Yatao A. and Buhmann, Joachim M. and Krause, Andreas},
        booktitle={Proceedings of the 36th International Conference on Machine Learning},
        pages={644--653},
        year={2019},
        volume={97},
        series={Proceedings of Machine Learning Research},
        address={Long Beach, California, USA},
        month={09--15 Jun},
        publisher={PMLR},
      }
    

• CoLA: Decentralized Linear Learning
  NeurIPS 2018. He*, Bian*, Jaggi [arXiv] [code] [poster] [bib]

      @inproceedings{he2018cola,
      title={COLA: Communication-Efficient Decentralized Linear Learning},
      author={He, Lie and Bian, An and Jaggi, Martin},
      booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
      pages = {4537--4547},
      year={2018}
      }
      

• A Distributed Second-Order Algorithm You Can Trust
  ICML 2018. Duenner, Lucchi, Gargiani, Bian, Hofmann, Jaggi [paper] [bib]

    @inproceedings{Celestine2018trust,
      title={A Distributed Second-Order Algorithm You Can Trust},
      author={D{\"u}nner, Celestine and Lucchi, Aurelien and Gargiani, Matilde and Bian, An and Hofmann, Thomas and Jaggi, Martin},
      booktitle={ICML},
      pages={1357--1365},
      year={2018}
    }
    

• Continuous DR-submodular Maximization: Structure and Algorithms
  NIPS 2017. Bian, Levy, Krause, Buhmann [arXiv] [code] [poster] [video] [bib]

  @inproceedings{biannips2017nonmonotone,
    title={Continuous DR-submodular Maximization: Structure and Algorithms},
    author={Bian, An and Levy, Kfir Y. and Krause, Andreas and Buhmann, Joachim M.},
    booktitle={Advances in Neural Information Processing Systems (NIPS)},
    pages={486--496},
    year={2017}
  }
  

• Guarantees for Greedy Maximization of Non-submodular Functions with Applications
  ICML 2017. Bian, Buhmann, Krause, Tschiatschek [arXiv] [code] [poster] [bib]

  @inproceedings{bianicml2017guarantees,
    title={Guarantees for Greedy Maximization of Non-submodular Functions with Applications},
    author={Bian, Andrew An and Buhmann, Joachim M. and Krause, Andreas and Tschiatschek, Sebastian},
    booktitle={International Conference on Machine Learning (ICML)},
    pages={498--507},
    year={2017}
  }
  

• Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains
  AISTATS 2017. Bian, Mirzasoleiman, Buhmann, Krause [full version][introductory post] [poster] [bib]

  @inproceedings{bian2017guaranteed,
    title={Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains},
    author={Bian, Andrew An and Mirzasoleiman, Baharan and Buhmann, Joachim M. and Krause, Andreas},
    booktitle={International Conference on Artificial Intelligence and Statistics (AISTATS)},
    pages={111--120},
    year={2017}
  }
  

• Model Selection for Gaussian Process Regression
  GCPR 2017. *Gorbach, *Bian, Fischer, Bauer, Buhmann [paper] [bib]

  @inproceedings{gorbach2017model,
    title={Model Selection for Gaussian Process Regression},
    author={Gorbach, Nico S and Bian, Andrew An and Fischer, Benjamin and Bauer, Stefan and Buhmann, Joachim M},
    booktitle={German Conference on Pattern Recognition},
    pages={306--318},
    year={2017}
  }
  

• Information-Theoretic Analysis of MAXCUT Algorithms
  ITA 2016. Bian, Gronskiy, Buhmann [pdf] [bib]

  @inproceedings{bian2016information,
      title={Information-theoretic analysis of MaxCut algorithms},
      author={Bian, Yatao and Gronskiy, Alexey and Buhmann, Joachim M},
      booktitle={IEEE Information Theory and Applications Workshop (ITA)},
      pages={1--5},
      url={http://people.inf.ethz.ch/ybian/docs/pa.pdf},
      year={2016}
  }
  

• Greedy MAXCUT Algorithms and their Information Content
  ITW 2015. Bian, Gronskiy, Buhmann [short version] [full version][slides][bib]

  @inproceedings{ITW15_BianGB,
    author    = {Yatao Bian and Alexey Gronskiy and Joachim M. Buhmann},
    title     = {Greedy MaxCut algorithms and their information content},
    booktitle = {IEEE Information Theory Workshop (ITW) 2015, Jerusalem, Israel},
    pages     = {1--5},
    year      = {2015}
  }
  

• Parallel Coordinate Descent Newton for Efficient L1-Regularized Minimization
  Technical report 2013. Bian, Li, Liu, Yang [pdf][code][bib]

  @article{bian2013parallel,
    title={Parallel Coordinate Descent Newton Method for Efficient L1-Regularized Minimization},
    author={Bian, An and Li, Xiong and Liu, Yuncai and Yang, Ming-Hsuan},
    journal={arXiv preprint arXiv:1306.4080},
    year={2013}
  }
  

• Bundle CDN: A Highly Parallelized Approach for Large-scale L1-regularized Logistic Regression
  ECML 2013. Bian, Li, Cao, Liu [pdf][bib]

  @inproceedings{ecmlBian13,
    author    = {Yatao Bian and Xiong Li and Mingqi Cao and Yuncai Liu},
    title     = {Bundle CDN: A Highly Parallelized Approach for Large-Scale L1-Regularized
      Logistic Regression},
    booktitle = {ECML/PKDD},
    year      = {2013},
    pages     = {81-95}
  }
  

• Parallelized Annealed Particle Filter for Real-Time Marker-Less Motion Tracking Via Heterogeneous Computing
  ICPR 2012. Bian, Zhao, Song, Liu [pdf][ bib]

  @inproceedings{icprbian12,
    author    = {Yatao Bian and Xu Zhao and Jian Song and Yuncai Liu},
    title     = {Parallelized Annealed Particle Filter for real-time marker-less motion tracking via heterogeneous computing},
    booktitle = {ICPR},
    year      = {2012},
    pages     = {2444-2447}
  }
  

• Digitize Your Body and Action in 3-D at Over 10 FPS: Real Time Dense Voxel Reconstruction and Marker-less Motion Tracking via GPU Acceleration
  Champion technical report of AMD China Accelerated Computing Contest, 2011 [demo].
  Song*, Bian*, Yan, Zhao, Liu [pdf][bib]

  @article{songbian2013digitize,
    title={Digitize Your Body and Action in 3-D at Over 10 FPS: Real Time Dense Voxel
      Reconstruction and Marker-less Motion Tracking via GPU Acceleration},
    author={Song, Jian and Bian, Yatao and Yan, Junchi and Zhao, Xu and Liu, Yuncai},
    journal={arXiv preprint arXiv:1311.6811},
    year={2013}
  }
  

Research Projects

Ongoing Projects:

◾ (Graph) Out-of-distribution (OOD) learning in drug AI
The problem of distribution shift is prevalent in various tasks of AI-aided drug discovery. For example, for the task of structure-based virtual screening, the models are often trained on data of known protein targets but have to be tested on unknown targets. Meanwhile, the current model backbone of Drug AI is the graph neural networks (GNNs).

We have built an OOD Dataset Curator and Benchmark for AI-aided Drug Discovery, for details please refer to the project page.

The main research directions include, but not limited to the following:
- Design OOD learning algorithms and theory, such as algorithms for domain generalization and domain adaptation scenarios, so that Drug AI algorithms could work efficiently in the scenarios of distribution shift.
- The combination of OOD learning and deep graph learning. On one hand, one could improve the generalization ability of deep graph learning algorithms in the OOD scenarios (graph OOD learning); on the other hand, one could utilize the strong modeling capabilities to design better OOD algorithms.

Previous Projects:

Fast Human Motion Tracking (National Champion in China Accelerated Computing Contest, 2011)
In charge of PAPF design, GPU implementation and optimization.
Proposed Parallelized Annealed Particle Filter (PAPF) algorithm via heterogeneous computing and built a real-time marker-less motion tracking system. Achieved 399 times speedup.

3D Monocular Human Upper Body Pose Estimation (Samsung Corp. Cooperate)
Team leader. Algorithm design, implementation and optimization.
Conducted human upper body pose estimation via generative algorithms . Incorporating generative models and discriminative algorithms to pursue better performance.

Links