AIDrugX@NeurIPS’24. “Correlational Lagrangian Schrödinger Bridge: Learning Dynamics with Population-Level Regularization”, Y. You, R. Zhou, Y. Shen, AI for New Drug Modalities Workshop, Conference on Neural Information Processing Systems. [paper]
HUGO’24. “Critical Assessment of Variant Prioritization Methods for Rare Disease Diagnosis within the Rare Genomes Project”, …, Y. You, …, Human Genomics. (Impact Factor 4.50, Outcome of CAGI6 RGP) [paper]
MLGenX@ICLR’24. “Multi-Modal Contrastive Learning for Proteins by Combining Domain-Informed Views”, H. Xu, Y. You, Y. Shen, Machine Learning for Genomics Explorations Workshop, International Conference on Learning Representations. [poster] [paper]
ICLR’24. “Latent 3D Graph Diffusion”, Y. You, R. Zhou, J. Park, H. Xu, C. Tian, Z. Wang, Y. Shen, International Conference on Learning Representations. (Acceptance Rate 31.00%) [poster] [paper] [code]
NeurIPS’23. “Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling”, H. Wang, Z. Jiang, Y. You, Y. Han, G. Liu, J. Srinivasa, R. Kompella, Z. Wang, Conference on Neural Information Processing Systems. (Acceptance Rate 26.10%) [paper] [code]
IEEE Data Engineering Bulletin’23. “Graph Contrastive Learning: An Odyssey towards Generalizable, Scalable and Principled Representation Learning on Graphs”, Y. Han, Y. You, W. Zheng, S. Hoang, T. Wei, M. Hassan, T. Chen, Y. Ding, Y. Shen, Z. Wang. (Invited Article) [paper]
ICLR’23. “Graph Domain Adaptation via Theory-Grounded Spectral Regularization”, Y. You, T. Chen, Z. Wang, Y. Shen, International Conference on Learning Representations. (Acceptance Rate 31.80%) [poster] [paper] [code]
MLSB@NeurIPS’22. “Does Inter-Protein Contact Prediction Benefit from Multi-Modal Data and Auxiliary Tasks?”, A. Talukder, R. Yin, Y. Sun, Y. Shen, Y. You, Machine Learning for Structural Biology Workshop, Conference on Neural Information Processing Systems. [poster] [paper]
NeurIPS’22. “Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative”, T. Wei*, Y. You*, T. Chen, Y. Shen, J. He, Z. Wang, Conference on Neural Information Processing Systems. (*Equal Contribution, Acceptance Rate 25.60%) [poster] [paper] [appendix] [code]
Bioinformatics’22. “Cross-Modality and Self-Supervised Protein Embedding for Compound-Protein Affinity and Contact Prediction”, Y. You, Y. Shen, Bioinformatics. (Impact Factor 6.93, MoML’22, ECCB’22 with Acceptance Rate 17.40%, 3DSIG COSI@ISMB/ECCB’21, MLSB@NeurIPS’20) [poster] [paper] [appendix] [code]
ICLR’22. “Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How”, Y. You, Y. Cao, T. Chen, Z. Wang, Y. Shen, International Conference on Learning Representations. (Acceptance Rate 32.29%) [poster] [paper] [code]
WSDM’22. “Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations”, Y. You, T. Chen, Z. Wang, Y. Shen, ACM International Conference on Web Search and Data Mining. (Acceptance Rate 20.22%) [poster] [paper] [code]
ICML’21 Long Presentation. “Graph Contrastive Learning Automated”, Y. You, T. Chen, Y. Shen, Z. Wang, International Conference on Machine Learning. (Acceptance Rate 3.01%) [video] [poster] [paper] [appendix] [code]
TVT’21. “Probabilistic Constructive Interference Precoding for Imperfect CSIT”, G. Lyu, Y. You, A. Li, X. Liao, C. Masouros, IEEE Transactions on Vehicular Technology. (Impact Factor 5.97) [paper]
KDF@AAAI’21 Oral. “AR-Stock: Deep Augmented Relational Stock Prediction”, T. Wei, Y. You, T. Chen, Knowledge Discovery from Unstructured Data in Financial Services Workshop, Association for the Advancement of Artificial Intelligence Conference. [paper]
NeurIPS’20. “Graph Contrastive Learning with Augmentations”, Y. You*, T. Chen*, Y. Sui, T. Chen, Z. Wang, Y. Shen, Conference on Neural Information Processing Systems. (*Equal Contribution, Acceptance Rate 20.09%) [poster] [paper] [appendix] [code]
ICML’20. “When Does Self-Supervision Help Graph Convolutional Networks?”, Y. You*, T. Chen*, Z. Wang, Y. Shen, International Conference on Machine Learning. (*Equal Contribution, Acceptance Rate 21.80%) [paper] [appendix] [code]
CVPR’20. “L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks”, Y. You*, T. Chen*, Z. Wang, Y. Shen, IEEE/CVF Conference on Computer Vision and Pattern Recognition. (*Equal Contribution, Acceptance Rate 22.08%) [paper] [appendix] [code]
IPAS’18. “An Optimization Approach of Compressive Sensing Recovery Using Split Quadratic Bregman Iteration with Smoothed l0 Norm”, G. Yang, Y. You, Z. Lu, J. Yang, Y. Wang, IEEE International Conference on Image Processing, Applications and Systems. [paper]