Publication ListPublications45. Yijun Dong, Hoang Phan, Xiang Pan, Qi Lei. Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning, to appear at NeurIPS 2024 44. Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D Lee. Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity, to appear at NeurIPS 2024 43. Hoang Phan, Andrew G Wilson, Qi Lei. ‘‘Controllable Prompt Tuning For Balancing Group Distributional Robustness"，ICML 2024 42. Hong Jun Jeon, Jason D Lee, Qi Lei, Benjamin Van Roy. ‘‘An Information-Theoretic Analysis of In-Context Learning", ICML 2024 41. Zhiyu Xue, Yinlong Dai, Qi Lei. ‘‘Exploring Minimally Sufficient Representation in Active Learning through Label-Irrelevant Patch Augmentation", CPAL 2024 40. Jianwei Li, Sheng Liu, Qi Lei. ‘‘Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning", FL@FM-NeurIPS 2023 39. Yijun Dong, Kevin Miller, Qi Lei, Rachel Ward. ‘‘Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering", NeurIPS 2023 38. Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee. ‘‘Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms", NeurIPS 2023 37. Jianwei Li, Qi Lei, Wei Cheng, Dongkuan Xu. ‘‘Towards Robust Pruning: An Adaptive Knowledge-Retention Pruning Strategy for Language Models", EMNLP-Main 2023 36. Jianwei Li, Weizhi Gao, Qi Lei, Dongkuan Xu. ‘‘Breaking through Deterministic Barriers: Randomized Pruning Mask Generation and Selection", EMNLP-Findings 2023 35. Tianci Liu, Tong Yang, Quan Zhang, Qi Lei. ‘‘Optimization for Amortized Inverse Problems", ICML 2023 34. Zihan Wang, Jason Lee, Qi Lei. ‘‘Reconstructing Training Data from Model Gradient, Provably", AISTATS 2023 33. Shuo Yang, Yijun Dong, Rachel Ward, Inderjit Dhillon, Sujay Sanghavi, Qi Lei. ‘‘Sample Efficiency of Data Augmentation Consistency Regularization", AISTATS 2023 32. Kurtland Chua, Qi Lei, Jason Lee. ‘‘Provable Hierarchy-Based Meta-Reinforcement Learning", AISTATS 2023 31. Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason Lee. ‘‘Optimal Sample Complexity Bounds for Non-convex Optimization under Kurdyka-Lojasiewicz Condition", AISTATS 2023 30. Chun-Yin Huang, Qi Lei, Xiaoxiao Li, Efficient Medical Image Assessment via Self-supervised Learning, MICCAI Workshop, DALI 2022, with Best Paper Honorable Mention 29. Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit Dhillon, Cho-Jui Hsieh ‘‘Cat: Customized adversarial training for improved robustness", IJCAI 2022 28. Baihe Huang*, Kaixuan Huang*, Sham M. Kakade*, Jason D. Lee*, Qi Lei*, Runzhe Wang*, Jiaqi Yang* ‘‘Optimal Gradient-based Algorithms for Non-concave Bandit Optimization", NeurIPS 2021 27. Baihe Huang*, Kaixuan Huang*, Sham M. Kakade*, Jason D. Lee*, Qi Lei*, Runzhe Wang*, Jiaqi Yang* ‘‘Going Beyond Linear RL: Sample Efficient Neural Function Approximation, NeurIPS 2021 26. Kurtland Chua, Qi Lei, Jason D. Lee, ‘‘How fine-tuning allows for effective meta-learning", NeurIPS 2021 25. Jason D Lee*, Qi Lei*, Nikunj Saunshi*, Jiacheng Zhuo*, ‘‘Predicting What You Already Know Helps: Provable Self-Supervised Learning", NeurIPS 2021 24. Tianle Cai*, Ruiqi Gao*, Jason D Lee*, Qi Lei*. ‘‘A Theory of Label Propagation for Subpopulation Shift", ICML 2021 23. Qi Lei, Wei Hu, Jason D. Lee. ‘‘Near-Optimal Linear Regression under Distribution Shift", ICML 2021 22. Jay Whang, Qi Lei, Alexandros G. Dimakis. “Solving Inverse Problems with a Flow-based Noise Model”, ICML 2021 21. Qi Lei*, Sai Ganesh Nagarajan*, Ioannis Panageas*, Xiao Wang*. “Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes”, AISTATS 2021 20. Simon S. Du*, Wei Hu*, Sham M. Kakade*, Jason D. Lee*, Qi Lei*. “Few-Shot Learning via Learning the Representation, Provably”, ICLR 2021 19. Xiao Wang, Qi Lei, Ioannis Panageas. “Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev”, Proc. of Neural Information Processing Systems (NeurIPS), 2020 18. Qi Lei, Jason D. Lee, Alexandros G. Dimakis, Constantinos Daskalakis. “SGD Learns One-Layer Networks in WGANs”, Proc. of International Conference of Machine Learning (ICML) 2020 17. Jiacheng Zhuo, Qi Lei, Alexandros G. Dimakis, Constantine Caramanis. “Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls”, AISTATS 2020 16. Qi Lei, Jiacheng Zhuo, Constantine Caramanis, Inderjit S Dhillon, Alexandros G Dimakis. “Primal-Dual Block Frank-Wolfe”, Proc. of Neural Information Processing Systems (NeurIPS) 2019 (slides, poster, code) 15. Qi Lei, Ajil Jalal, Inderjit S. Dhillon, Alexandros G. Dimakis. “Inverting Deep Generative models, One layer at a time”, Proc. of Neural Information Processing Systems (NeurIPS) 2019 (poster, code) 14. Qi Lei, Jinfeng Yi, Roman Vaculin, Lingfei Wu, Inderjit Dhillon. “Similarity Preserving Representation Learning for Time Series Analysis”, The 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019. (code) 13. Qi Lei, Lingfei Wu, Pin-Yu Chen, Alexandros G. Dimakis, Inderjit S. Dhillon, Michael Witbrock. “Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification”, Systems and Machine Learning (sysML). 2019 (code, slides)
12. Jinfeng Yi, Qi Lei, Wesley Gifford, Ji Liu. “Negative-Unlabeled Tensor Factorization for Location Category Inference from Inaccurate Mobility Data”, SIAM International Conference on Data Mining (SDM), 2019 (code) 11. Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, Michael W. Mahoney. “Hessian-based Analysis of Large Batch Training and Robustness to Adversaries”, Neural Information Processing Systems (NIPS), 2018 10. Jiong Zhang, Qi Lei, Inderjit Dhillon, “Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization”, International Conference of Machine Learning (ICML), July. 2018 9. Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei and Michael Witbrock, “Random Warping Series: A Random Features Method for Time-Series Embedding”, Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS), 2018 8. Hsiang-fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit Dhillon, “A Greedy Approach for Budgeted Maximum Inner Product Search”, Proc. of Neural Information Processing Systems (NIPS), 2017 7. Qi Lei, Enxu Yan, Chao-yuan Wu, Pradeep Ravikumar, Inderjit Dhillon, “Doubly Greedy Primal-Dual Coordinate Methods for Sparse Empirical Risk Minimization”, Proc. of International Conference of Machine Learning (ICML), 2017 (code) 6. Rashish Tandon, Qi Lei, Alexandros G. Dimakis, Nikos Karampatziakis, “Gradient Coding: Avoiding Stragglers in Distributed Learning”, Proc. of International Conference of Machine Learning (ICML), 2017 (code) 5. Qi Lei, Kai Zhong, Inderjit. Dhillon, “Coordinate-wise Power Method”, Proc. of Neural Information Processing Systems (NIPS), Dec. 2016 (code,poster) 4. Arnaud Vandaele, Nicolas Gillis, Qi Lei, Kai Zhong, Inderjit Dhillon, “Efficient and Non-Convex Coordinate Descent Methods for Symmetric Nonnegative Matrix Factorization”, IEEE Transactions on Signal Processing 64.21 (2016): 5571-5584 (code) 3. Maria R. D'Orsogna, Qi Lei, Tom Chou, “First assembly times and equilibration in stochastic coagulation-fragmentation”, The Journal of Chemical Physics, 2015: 143.1, 014112 2. Jiazhou Chen, Qi Lei, Yongwei Miao, Qunsheng Peng, “Vectorization of Line Drawing Image based on Junction Analysis”, Science China Information Sciences, 2014:1-14 (code) 1. Jiazhou Chen, Qi Lei, Fan Zhong, Qunsheng Peng, “Interactive Tensor Field Design Based on Line Singularities”, Proceedings of the 13th International CAD/Graphics, 2013 (code) Dissertation“Provably Effective Algorithms for Min-Max Optimization” May, 2020 with Oden Institute Outstanding Dissertation Award Patents“Method and System for General and Efficient Time Series Representation Learning via Dynamic Time Warping” “Real-Time Cold Start Recommendation and Rationale within a Dialog System” |