Tianyi Zhou

Tianyi Zhou

Assistant Professor of Computer Science

Publications

Preprints can be found on my Google Scholar page or Research

A more organized list of (selected) publications can be found here.

Conference

2024

  1. Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, Anima Anandkumar, Furong Huang, “Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization”, Advances in Neural Information Processing Systems 38 (NeurIPS Datasets and Benchmarks Track), 2024. PDF, DATASET
  2. Maharshi Gor, Hal Daumé III, Tianyi Zhou, Jordan Lee Boyd-Graber, “Do great minds think alike? Investigating Human-AI Complementarity for Question Answering”, The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024. PDF
  3. Hanchi Sun, Tianyi Zhou, Xun Chen, Lichao Sun, “SpecHub: Provable Acceleration to Multi-Draft Speculative Decoding”, The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024. PDF, CODE
  4. Xiyang Wu*, Tianrui Guan*, Dianqi Li, Shuaiyi Huang, Xiaoyu Liu, Xijun Wang, Ruiqi Xian, Abhinav Shrivastava, Furong Huang, Jordan Lee Boyd-Graber, Tianyi Zhou, Dinesh Manocha, “AUTOHALLUSION: Automatic Generation of Hallucination Benchmarks for Vision-Language Models”, The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) Findings, 2024. PDF, CODE
  5. Yue Huang, Chenrui Fan, Yuan Li, Siyuan Wu, Tianyi Zhou, Xiangliang Zhang, Lichao Sun, “1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators?”, The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024. PDF
  6. Xirui Li, Ruochen Wang, Minhao Cheng, Tianyi Zhou, Cho-Jui Hsieh, “DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers”, The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) Findings, 2024. PDF, CODE
  7. Lilly Kumari, Shengjie Wang, Tianyi Zhou, Nikhil Sarda, Anthony Rowe, Jeff Bilmes, “BumbleBee: Dynamic KV Cache Summarization in Transformers using Submodular Optimization”, First Conference on Language Modeling (COLM), 2024. PDF
  8. Yuanhao Ban, Ruochen Wang, Tianyi Zhou, Minhao Cheng, Boqing Gong, Cho-Jui Hsieh, “When and How do negative prompts take effect?”, European Conference on Computer Vision (ECCV), 2024. PDF
  9. Soumik Mukhopadhyay, Matthew Gwilliam, Yosuke Yamaguchi, Vatsal Agarwal, Namitha Padmanabhan, Archana Swaminathan, Tianyi Zhou, Jun Ohya, Abhinav Shrivastava, “Do text-free diffusion models learn discriminative visual representations?”, European Conference on Computer Vision (ECCV), 2024. PDF
  10. Chunxu Zhang, Guodong Long, Tianyi Zhou, Zijian Zhang, Peng Yan, and Bo Yang, “GPFedRec: Graph-Guided Personalization for Federated Recommendation”, 30th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024. PDF
  11. Ming Li, Yong Zhang, Shwai He, Zhitao Li, Hongyu Zhao, Jianzong Wang, Ning Cheng, and Tianyi Zhou, “Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning”, Annual Meeting of the Association for Computational Linguistics (ACL), 2024. PDF, CODE
  12. Yibin Lei, Di Wu, Tianyi Zhou, Tao Shen, Yu Cao, Chongyang Tao, and Andrew Yates, “Meta-Task Prompting Elicits Embedding from Large Language Models”, Annual Meeting of the Association for Computational Linguistics (ACL), 2024. PDF, CODE
  13. Dang Nguyen, Jiuhai Chen, and Tianyi Zhou, “Multi-Objective Linguistic Control of Large Language Models”, Annual Meeting of the Association for Computational Linguistics (ACL) Findings, 2024. PDF, CODE
  14. Ming Li, Jiuhai Chen, Lichang Chen, and Tianyi Zhou, “Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements”, Annual Meeting of the Association for Computational Linguistics (ACL) Findings, 2024. PDF, CODE
  15. Ming Li, Lichang Chen, Jiuhai Chen, Shwai He, Jiuxiang Gu, and Tianyi Zhou, “Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning”, Annual Meeting of the Association for Computational Linguistics (ACL) Findings, 2024. PDF, CODE
  16. Tao Shen, Guodong Long, Xiubo Geng, Chongyang Tao, Yibin Lei, Tianyi Zhou, Michael Blumenstein, and Daxin Jiang, “Large Language Models are Strong Zero-Shot Retriever”, Annual Meeting of the Association for Computational Linguistics (ACL) Findings, 2024. PDF
  17. Lichang Chen*, Jiuhai Chen*, Heng Huang, Tom Goldstein, and Tianyi Zhou. “InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models”, International Conference on Machine Learning (ICML), 2024. PDF, CODE
  18. Ruochen Wang, Sohyun An, Minhao Cheng, Tianyi Zhou, Sung Ju Hwang, and Cho-Jui Hsieh. “One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts”, International Conference on Machine Learning (ICML), 2024. PDF, CODE
  19. Lichang Chen, Chen Zhu, Davit Soselia, Jiuhai Chen, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, and Bryan Catanzaro. “ODIN: Disentangled Reward Mitigates Hacking in RLHF”, International Conference on Machine Learning (ICML), 2024. PDF, MODEL
  20. Lichao Sun, Yue Huang, et al. “TrustLLM: Trustworthiness in Large Language Models”, International Conference on Machine Learning (ICML), 2024. PDF
  21. Ming Li, Yong Zhang, Zhitao Li, Jiuhai Chen, Lichang Chen, Ning Cheng, Jianzong Wang, Tianyi Zhou, and Jing Xiao. “From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning”, Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024. PDF, CODE
  22. Lilly Kumari, Shengjie Wang, Arnav Mohanty Das, Tianyi Zhou, and Jeff Bilmes. “An End-to-End Submodular Framework for Data-Efficient In-Context Learning”, Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) Findings, 2024. PDF
  23. Tianrui Guan*, Fuxiao Liu*, Xiyang Wu, Ruiqi Xian, Zongxia Li, Xiaoyu Liu, Xijun Wang, Lichang Chen, Furong Huang, Yaser Yacoob, Dinesh Manocha, and Tianyi Zhou. “HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. PDF, CODE+DATA
  24. Yijun Yang, Tianyi Zhou, Kanxue Li, Dapeng Tao, Lusong Li, Li Shen, Xiaodong He, Jing Jiang, and Yuhui Shi. “Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. PDF, CODE
  25. Chunxu Zhang, Guodong Long, Tianyi Zhou, Zijian Zhang, Peng Yan, Bo Yang, “When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions”, The Web Conference (WWW), 2024. PDF
  26. Yu Shen, Laura Zheng, Tianyi Zhou, Ming C. Lin, “Task-Driven Domain-Agnostic Learning with Information Bottleneck for Autonomous Steering”, IEEE International Conference on Robotics and Automation (ICRA), 2024. PDF
  27. Yibin Lei, Yu Cao, Tianyi Zhou, Tao Shen, Andrew Yates, “Corpus-Steered Query Expansion with Large Language Models”, The 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2024. PDF
  28. Yucheng Yang, Tianyi Zhou, Qiang He, Lei Han, Mykola Pechenizkiy, Meng Fang, “Task Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning”, International Conference on Learning Representations (ICLR), 2024. ( Spotlight )PDF
  29. Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, Hongxia Jin, “Alpagasus: Training a Better Alpaca Model with Fewer Data”, International Conference on Learning Representations (ICLR), 2024. PDF, CODE
  30. Qiang He, Tianyi Zhou, Meng Fang, Setareh Maghsudi, “Adaptive Regularization of Representation Rank as an Implicit Constraint of Bellman Equation”, International Conference on Learning Representations (ICLR), 2024. PDF
  31. Zhiwei Li, Guodong Long, Tianyi Zhou, “Federated Recommendation with Additive Personalization”, International Conference on Learning Representations (ICLR), 2024. PDF
  32. Yucheng Yang, Tianyi Zhou, Lei Han, Meng Fang, Mykola Pechenizkiy, “Automatic Curriculum for Unsupervised Reinforcement Learning”, International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2024. PDF

2023

  1. Jiuhai Chen, Lichang Chen, Chen Zhu, Tianyi Zhou, “How Many Demonstrations Do You Need for In-context Learning?”, The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP) Findings, 2023. PDF
  2. Shwai He, Run-Ze Fan, Liang Ding, Li Shen, Tianyi Zhou, Dacheng Tao, “Merging Experts into One: Improving Computational Efficiency of Mixture of Experts”, The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023. PDF
  3. Divya Kothandaraman, Tianyi Zhou, Ming C. Lin, Dinesh Manocha, “Aerial Diffusion: Text Guided Ground-to-Aerial View Translation from a Single Image using Diffusion Models”, The 16th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia Pacific (SIGGRAPH Asia), 2023. PDF, CODE
  4. Chen Liang, Jiahui Yu, Ming-Hsuan Yang, Matthew Brown, Yin Cui, Tuo Zhao, Boqing Gong, Tianyi Zhou, “Module-wise Adaptive Distillation for Multimodality Foundation Models”, Advances in Neural Information Processing Systems 37 (NeurIPS), 2023. PDF
  5. Jie Ma, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang, “Structured Federated Learning through Clustered Additive Modeling”, Advances in Neural Information Processing Systems 37 (NeurIPS), 2023. PDF
  6. Chengkai Hou, Jieyu Zhang, Tianyi Zhou, “When to Learn What: Model-Adaptive Data Augmentation Curriculum”, International Conference on Computer Vision (ICCV), 2023. PDF
  7. Chengkai Hou, Jieyu Zhang, Haonan Wang, Tianyi Zhou, “Subclass-balancing Contrastive Learning for Long-tailed Recognition”, International Conference on Computer Vision (ICCV), 2023. PDF
  8. Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang, “Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks”, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2023. PDF
  9. Qiang He, Tianyi Zhou, Meng Fang, Setareh Maghsudi, “Eigensubspace of Temporal-Difference Dynamics and How It Improves Value Approximation in Reinforcement Learning”, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2023. PDF
  10. Yijun Yang, Tianyi Zhou, Jing Jiang, Guodong Long, and Yuhui Shi. “Continual Task Allocation in Meta-Policy Network via Sparse Prompting”, International Conference on Machine Learning (ICML), 2023. PDF, Code
  11. Shuangtong Li, Tianyi Zhou, Xinmei Tian, and Dacheng Tao. “Structured Cooperative Learning with Graphical Model Priors”, International Conference on Machine Learning (ICML), 2023. PDF, Code, Poster
  12. Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang. “Does Continual Learning Equally Forget All Parameters?”, International Conference on Machine Learning (ICML), 2023. PDF
  13. Chunxu Zhang, Guodong Long, Tianyi Zhou, Peng Yan, Zijian Zhang, Chengqi Zhang, and Bo Yang, “Dual Personalization on Federated Recommendation”, International Joint Conference on Artificial Intelligence (IJCAI), 2023. PDF

2022

  1. Yu Cao, Dianqi Li, Meng Fang, Tianyi Zhou, Jun Gao, Yibing Zhan and Dacheng Tao, “TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack”, The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022. PDF
  2. Lilly Kumari, Shengjie Wang, Tianyi Zhou, Jeff A. Bilmes, “Retrospective Adversarial Replay for Continual Learning”, Advances in Neural Information Processing Systems 36 (NeurIPS), 2022. PDF, CODE
  3. Yue Tan, Guodong Long, Jie Ma, Lu Liu, Tianyi Zhou, Jing Jiang, “Federated Learning from Pre-Trained Models: A Contrastive Learning Approach”, Advances in Neural Information Processing Systems 36 (NeurIPS), 2022. PDF
  4. Kaiwen Yang, Yanchao Sun, Jiahao Su, Fengxiang He, Xinmei Tian, Furong Huang, Tianyi Zhou, Dacheng Tao, “Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach”, Advances in Neural Information Processing Systems 36 (NeurIPS), 2022. PDF, Code
  5. Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang, “EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning”, International Conference on Machine Learning (ICML), 2022. PDF
  6. Kaiwen Yang, Tianyi Zhou, Xinmei Tian, Dacheng Tao, “Identity-Disentangled Adversarial Augmentation for Self-supervised Learning”, International Conference on Machine Learning (ICML), 2022. PDF
  7. Fengwen Chen, Guodong Long, Zonghan Wu, Tianyi Zhou, Jing Jiang, “Personalized Federated Learning With Structural Information”, International Joint Conference on Artificial Intelligence (IJCAI), 2022. (long presentation) PDF
  8. Yibin Lei, Yu Cao, Dianqi Li, Tianyi Zhou, Meng Fang, Mykola Pechenizkiy, “Phrase-level Textual Adversarial Attack with Label Preservation”, Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL Findings), 2022. PDF
  9. Shuangtong Li, Tianyi Zhou, Xinmei Tian, Dacheng Tao, “Learning to Collaborate in Decentralized Learning of Personalized Models”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. PDF
  10. Le Hou*, Richard Yuanzhe Pang*, Tianyi Zhou, Yuexin Wu, Xinying Song, Xiaodan Song, Denny Zhou, “Token Dropping for Efficient BERT Pretraining”, Annual Meeting of the Association for Computational Linguistics (ACL), 2022. PDF
  11. Yijun Yang, Jing Jiang, Tianyi Zhou, Jie Ma, Yuhui Shi, “Pareto Policy Pool for Model-based Offline Reinforcement Learning”, International Conference on Learning Representations (ICLR), 2022. PDF
  12. Ravikumar Balakrishnan*, Tian Li*, Tianyi Zhou*, Nageen Himayat, Virginia Smith, Jeff A. Bilmes, “Diverse Client Selection for Federated Learning via Submodular Maximization”, International Conference on Learning Representations (ICLR), 2022. PDF, Code
  13. Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Michael Blumenstein, Jing Jiang, “Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification”, International Conference on Learning Representations (ICLR), 2022. PDF
  14. Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, Chengqi Zhang, “FedProto: Federated Prototype Learning across Heterogeneous Clients”, The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2022. PDF, Code

2021

  1. Shengjie Wang*, Tianyi Zhou*, Chandrashekhar Lavania, Jeff A. Bilmes, “Constrained Robust Submodular Partitioning”, Advances in Neural Information Processing Systems 35 (NeurIPS), 2021. ( Spotlight ) PDF, Appendix
  2. Shuang Ao, Tianyi Zhou, Guodong Long, Qinghua Lu, Liming Zhu, Jing Jiang, “CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum”, Advances in Neural Information Processing Systems 35 (NeurIPS), 2021. PDF, Appendix, Code, Slides
  3. Kaiwen Yang, Tianyi Zhou, Yonggang Zhang, Xinmei Tian, Dacheng Tao, “Class-Disentanglement and Applications in Adversarial Detection and Defense”, Advances in Neural Information Processing Systems 35 (NeurIPS), 2021. PDF, Appendix, Code, Slides
  4. Bo Wang, Tao Shen, Guodong Long, Tianyi Zhou, Yi Chang, “Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction”, The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP) Findings, 2021. PDF
  5. Tianyi Zhou*, Shengjie Wang*, and Jeff A. Bilmes, “Curriculum Learning by Optimizing Learning Dynamics”, International Conference on Artificial Intelligence and Statistics (AISTATS), 2021. PDF, Appendix, Slides, Talk
  6. Tianyi Zhou*, Shengjie Wang*, and Jeff A. Bilmes, “Robust Curriculum Learning: from clean label detection to noisy label self-correction”, International Conference on Learning Representations (ICLR), 2021. PDF, Slides
  7. Yuchen Jin, Tianyi Zhou, Liangyu Zhao, Yibo Zhu, Chuanxiong Guo, Marco Canini and Arvind Krishnamurthy, “AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly”, International Conference on Learning Representations (ICLR), 2021. PDF, Slides, Code
  8. Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Xuanyi Dong and Chengqi Zhang, “Isometric Propagation Network for Generalized Zero-shot Learning”, International Conference on Learning Representations (ICLR), 2021. PDF
  9. Bo Wang, Tao Shen, Guodong Long, Tianyi Zhou, Yi Chang, “Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion”, The WEB Conference (WWW), 2021. arXiv

2020

  1. Tianyi Zhou*, Shengjie Wang*, and Jeff A. Bilmes, “Curriculum Learning with Dynamic Instance Hardness”, Advances in Neural Information Processing Systems 34 (NeurIPS), 2020. PDF, Appendix, Slides, Code, Talk
  2. Tianyi Zhou*, Shengjie Wang*, and Jeff A. Bilmes, “Time-Consistent Self-Supervision for Semi-Supervised Learning”, International Conference on Machine Learning (ICML), 2020. PDF, Appendix, Slides, Talk
  3. Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang, “Attribute Propagation Network for Graph Zero-shot Learning”, The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), New York, USA, 2020. PDF, Code, Poster
  4. Yang Li, Tao Shen, Guodong Long, Jing Jiang, Tianyi Zhou, Chengqi Zhang, “Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention”, The 28th International Conference on Computational Linguistics (COLING), 2020. PDF, Code
  5. Yang Li, Guodong Long, Tao Shen, Tianyi Zhou, Lina Yao, Huan Huo and Jing Jiang, “Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction”, The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), New York, USA, 2020. PDF

2019

  1. Meng Fang, Tianyi Zhou, Yali Du, Lei Han, and Zhengyou Zhang, “Curriculum-guided Hindsight Experience Replay”, Advances in Neural Information Processing Systems 33 (NeurIPS), Vancouver, BC, Canada, 2019. PDF, Appendix, Code, Poster
  2. Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang, “Learning to Propagate for Graph Meta-Learning”, Advances in Neural Information Processing Systems 33 (NeurIPS), Vancouver, BC, Canada, 2019. PDF, Appendix, Code, Poster, Slides
  3. Tianyi Zhou*, Shengjie Wang*, and Jeff A. Bilmes, “Jumpout: Improved Dropout for Deep Neural Networks with Rectified Linear Units”, International Conference on Machine Learning (ICML), Long Beach, CA, 2019. PDF, Slides, Talk
  4. Tianyi Zhou*, Shengjie Wang*, and Jeff A. Bilmes, “Bias Also Matters: Bias Attribution for Deep Neural Network Explanation”, International Conference on Machine Learning (ICML), Long Beach, CA, 2019. PDF, Appendix, Slides, Talk(slide 223-231)
  5. Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Lina Yao and Chengqi Zhang, “Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph”, International Joint Conferences on Artificial Intelligence (IJCAI), Macau, China, 2019. PDF, Code
  6. Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang and Chengqi Zhang, “Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together”, Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2019. PDF, Code

2018

  1. Tianyi Zhou, Shengjie Wang, and Jeff A. Bilmes, “Diverse Ensemble Evolution: Curriculum Data-Model Marriage”, Advances in Neural Information Processing Systems 32 (NeurIPS), Montreal, QC, Canada, 2018. PDF, Appendix, Poster
  2. Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Sen Wang and Chengqi Zhang, “Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling”, International Joint Conferences on Artificial Intelligence (IJCAI), Stockholm, Sweden, 2018. PDF, Code
  3. Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Shirui Pan and Chengqi Zhang, “DiSAN: Directional self-attention network for rnn/cnn-free language understanding”, The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), New Orleans, Louisiana, USA, 2018. ( Most cited student’s paper, 809 citations ) PDF, Code
  4. Tianyi Zhou and Jeff A. Bilmes, “Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity”, Sixth International Conference on Learning Representations (ICLR), Vancouver, BC, Canada, 2018. PDF
  5. Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang and Chengqi Zhang, “Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling”, Sixth International Conference on Learning Representations (ICLR), Vancouver, BC, Canada, 2018. PDF, Code

2009-2017

  1. Tianyi Zhou, Hua Ouyang, Jeff A. Bilmes, Yi Chang and Carlos Guestrin, “Scaling Submodular Maximization via Pruned Submodularity Graphs”, Twentyth International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, FL, 2017. PDF, Appendix
  2. Tianyi Zhou, Jeff A. Bilmes and Carlos Guestrin, “Divide-and-Conquer Learning by Anchoring a Conical Hull”, Twenty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), Montreal, Canada, 2014. PDF
  3. Tianyi Zhou and Dacheng Tao, “Multi-task Copula by Sparse Graph Regression”, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), NYC, USA, 2014. PDF
  4. Tianyi Zhou, Wei Bian, and Dacheng Tao, “Divide-and-Conquer Anchoring for Near Separable Nonnegative Matrix Factorization and Completion in High Dimensions”, IEEE International Conference on Data Mining (ICDM), pp., Dallas, TX, USA, 2013. ( Best Student Paper Award ) PDF, Slides
  5. Tianyi Zhou and Dacheng Tao, “Shifted Subspaces Tracking on Sparse Outlier for Motion Segmentation”, International Joint Conferences on Artificial Intelligence (IJCAI), Beijing, China, 2013. PDF
  6. Yang Mu, Wei Ding, Tianyi Zhou, Dacheng Tao, “Constrained Stochastic Gradient Descent for Large-scale Least Squares Problem”, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Chicago, Illinois, USA, 2013. PDF
  7. Tianyi Zhou and Dacheng Tao, “Greedy Bilateral Sketch, Completion & Smoothing”, International Conference on Artificial Intelligence and Statistics (AISTATS), Journal of Machine Learning Research - Proceedings Track, Scottsdale, Arizona, USA, 2013. PDF
  8. Tianyi Zhou and Dacheng Tao, “k-bit Hamming Compressed Sensing”, IEEE International Symposium on Information Theory (ISIT), Istanbul, Turkey, 2013. PDF
  9. Tianyi Zhou and Dacheng Tao, “Multi-label Subspace Ensemble”, International Conference on Artificial Intelligence and Statistics (AISTATS), Journal of Machine Learning Research - Proceedings Track 22: 1444-1452, La Palma, Canary Islands, Spain, 2012. PDF
  10. Tianyi Zhou and Dacheng Tao, “Bilateral Random Projections”, IEEE International Symposium on Information Theory (ISIT), MIT, Boston, USA, 2012. PDF
  11. Tianyi Zhou and Dacheng Tao, “1-bit Hamming Compressed Sensing”, IEEE International Symposium on Information Theory (ISIT), MIT, Boston, USA, 2012. PDF
  12. Tianyi Zhou and Dacheng Tao, “Labelset Anchored Subspace Ensemble (LASE) for Multi-label Annotation”, ACM International Conference on Multimedia Retrieval (ICMR), Hong Kong, China, 2012. PDF
  13. Tianyi Zhou and Dacheng Tao, “GoDec: Randomized Low-rank & Sparse Matrix Decomposition in Noisy Case”, Twenty-Eighth International Conference on Machine Learning (ICML), Bellevue, WA, USA, 2011. ( IEEE TCSC Most Influential Paper Award, Most cited first-author paper, 808 citations ) PDF, Code, Demo Videos, Talk
  14. Tianyi Zhou, Dacheng Tao and Xindong Wu, “NESVM: a Fast Gradient Method for Support Vector Machines”, IEEE International Conference on Data Mining (ICDM), pp. 679-688, Sydney, Australia, 2010. PDF, Code
  15. Tianyi Zhou and Dacheng Tao, “Backward-Forward Least Angle Shrinkage for Sparse Quadratic Optimization”, International Conference on Neural Information Processing (ICONIP), pp. 388-396, Sydney, Australia, 2010. PDF
  16. Tianyi Zhou and Dacheng Tao, “Manifold elastic net for sparse learning”, IEEE International conference on Systems, Man and Cybernetics (SMC), pp. 3699-3704, San Antonio, Texas, USA, 2009. PDF

Journal

  1. Songhua Wu, Tianyi Zhou, Yuxuan Du, Jun Yu, Bo Han, Tongliang Liu, “A Time-Consistency Curriculum for Learning from Instance-Dependent Noisy Labels”, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2024.
  2. Zhihong Deng, Zuyue Fu, Lingxiao Wang, Zhuoran Yang, Chenjia Bai, Tianyi Zhou, Zhaoran Wang, Jing Jiang, “False Correlation Reduction for Offline Reinforcement Learning”, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2023.
  3. Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang, “Extracting Local Reasoning Chains of Deep Neural Networks”, Transactions on Machine Learning Research (TMLR), 2022. PDF
  4. Guodong Long, Ming Xie, Tao Shen, Tianyi Zhou, Xianzhi Wang, Jing Jiang, “Multi-Center Federated Learning: clients clustering for better personalization”, World Wide Web Journal (Springer), 2022. PDF
  5. Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang, “Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy”, IEEE Transactions on Knowledge and Data Engineering (T-KDE), 2020. PDF, Code
  6. Meng Fang, Tianyi Zhou, Jie Yin, Wang Yang, and Dacheng Tao, “Data Subset Selection with Imperfect Multiple Labels”, IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), 2018.
  7. Dongjin Song, Wei Liu, Tianyi Zhou, Dacheng Tao, and David A Meyer,”Efficient Robust Conditional Random Fields”, IEEE Transactions on Image Processing (T-IP), 24(10): 3124-3136, 2015.
  8. Wei Bian, Tianyi Zhou, Aleix M. Martinez, George Baciu and Dacheng Tao, “Minimizing Nearest Neighbor Classification Error for Nonparametric Dimension Reduction”, IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), 25(8): 1588-1594, 2014.
  9. Tianyi Zhou and Dacheng Tao, “Double Shrinking for Sparse Learning”, IEEE Transactions on Image Processing (T-IP), 22(1): 244-257, 2013. PDF
  10. Tianyi Zhou, Dacheng Tao and Xindong Wu,”Compressed Labeling on Distilled Labelsets for Multi-label Learning”, Machine Learning (Springer) (MLJ) 88(1-2): 69-126, 2012. PDF
  11. Tianyi Zhou, Dacheng Tao and Xindong Wu, “Manifold elastic net: a unified framework for sparse dimension reduction”, Data Mining and Knowledge Discovery (Springer) (DMKD) 22(3): 340-371, 2011. PDF

Preprint

  1. Zhuowei Wang, Tianyi Zhou, Guodong Long, Bo Han, and Jing Jiang, “FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels”, arXiv: 2205.10110, 2022. PDF
  2. Yujia Xie, Tianyi Zhou, Yi Mao and Weizhu Chen, “Conditional Self-Attention for Query-based Summarization”, arXiv: 2002.07338, 2020. PDF
  3. Tianyi Zhou and Jeff A. Bilmes, “Stream Clipper: Scalable Submodular Maximization on Stream”, arXiv: 1606.00389, 2016. PDF
  4. Tianyi Zhou and Dacheng Tao, “Hamming Compressed Sensing”, arxiv: 1110.0073, 2011. PDF

Misc

  1. “Contextual Bandits: From EXP3 to EXP4”, Lecture notes for Online Learning (CSE599s, University of Washington), 2014. PDF