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<h1>Publication List</h1>
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<h2>Publications</h2>
<p>49. Sheng Liu, Zihan Wang, <b>Qi Lei</b>. <a href="https://arxiv.org/abs/2402.09478">Data Reconstruction Attacks and Defenses: A Systematic Evaluation</a>, <i>to appear at AISTATS 2025</i> </p>
<p>48. Tao Wen, Zihan Wang, Quan Zhang, <b>Qi Lei</b>. <a href="cecilialeiqi.github.io">Elastic Representation: Mitigating Spurious Correlations for Group Robustness</a>, <i>to appear at AISTATS 2025</i></p>
<p>47. Ziliang Samuel Zhong, Xiang Pan, <b>Qi Lei</b>. <a href="https://arxiv.org/abs/2403.06424">Bridging Domains with Approximately Shared Features</a>, <i>to appear at AISTATS 2025</i></p>
<p>46. Qi Zhang, Yifei Wang, Jingyi Cui, Xiang Pan, <b>Qi Lei</b>, Stefanie Jegelka, Yisen Wang. <a href="https://arxiv.org/abs/2410.21331">Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness</a>, <i>to appear at ICLR 2025</i> </p>
<p>45. Yijun Dong, Hoang Phan, Xiang Pan, <b>Qi Lei</b>. <a href="https://arxiv.org/abs/2407.06120">Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning</a>, <i>NeurIPS 2024</i></p>
<p>44. Qian Yu, Yining Wang, Baihe Huang, <b>Qi Lei</b>, Jason D Lee. <a href="https://arxiv.org/abs/2406.19617">Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity</a>, <i>NeurIPS 2024</i></p>
<p>43. Hoang Phan, Andrew G Wilson, <b>Qi Lei</b>. <a href="https://arxiv.org/abs/2403.02695">‘‘Controllable Prompt Tuning For Balancing Group Distributional Robustness"</a>，<i>ICML 2024</i></p>
<p>42. Hong Jun Jeon, Jason D Lee, <b>Qi Lei</b>, Benjamin Van Roy. <a href="https://arxiv.org/abs/2401.15530">‘‘An Information-Theoretic Analysis of In-Context Learning"</a>, <i>ICML 2024</i></p>
<p>41. Zhiyu Xue, Yinlong Dai, <b>Qi Lei</b>. <a href="https://openreview.net/pdf?id=MlgnGWdqWl">‘‘Exploring Minimally Sufficient Representation in Active Learning through Label-Irrelevant Patch Augmentation"</a>, <i>CPAL 2024</i></p>
<p>40. Jianwei Li, Sheng Liu, <b>Qi Lei</b>. <a href="https://arxiv.org/abs/2312.05720">‘‘Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning"</a>, <i>FL@FM-NeurIPS 2023</i></p>
<p>39. Yijun Dong, Kevin Miller, <b>Qi Lei</b>, Rachel Ward. <a href="https://arxiv.org/pdf/2307.11030">‘‘Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering"</a>, <i>NeurIPS 2023</i></p>
<p>38. Qian Yu, Yining Wang, Baihe Huang, <b>Qi Lei</b>, Jason D. Lee. <a href="https://arxiv.org/pdf/2306.12383">‘‘Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms"</a>, <i>NeurIPS 2023</i></p>
<p>37. Jianwei Li, <b>Qi Lei</b>, Wei Cheng, Dongkuan Xu. <a href="https://arxiv.org/abs/2310.13191">‘‘Towards Robust Pruning: An Adaptive Knowledge-Retention Pruning Strategy for Language Models"</a>, <i>EMNLP-Main 2023</i></p>
<p>36. Jianwei Li, Weizhi Gao, <b>Qi Lei</b>, Dongkuan Xu. <a href="https://arxiv.org/abs/2310.13183">‘‘Breaking through Deterministic Barriers: Randomized Pruning Mask Generation and Selection"</a>, <i>EMNLP-Findings 2023</i></p>
<p>35. Tianci Liu, Tong Yang, Quan Zhang, <b>Qi Lei</b>. <a href="https://arxiv.org/abs/2210.13983">‘‘Optimization for Amortized Inverse Problems"</a>, <i>ICML 2023</i> </p>
<p>34. Zihan Wang, Jason Lee, <b>Qi Lei</b>. <a href="https://arxiv.org/abs/2212.03714">‘‘Reconstructing Training Data from Model Gradient, Provably"</a>, <i>AISTATS 2023</i></p>
<p>33. Shuo Yang, Yijun Dong, Rachel Ward, Inderjit Dhillon, Sujay Sanghavi, <b>Qi Lei</b>. <a href="https://arxiv.org/abs/2202.12230">‘‘Sample Efficiency of Data Augmentation Consistency Regularization"</a>, <i>AISTATS 2023</i></p>
<p>32. Kurtland Chua, <b>Qi Lei</b>, Jason Lee. <a href="https://arxiv.org/abs/2110.09507">‘‘Provable Hierarchy-Based Meta-Reinforcement Learning"</a>, <i>AISTATS 2023</i></p>
<p>31. Qian Yu, Yining Wang, Baihe Huang, <b>Qi Lei</b>, Jason Lee. ‘‘Optimal Sample Complexity Bounds for Non-convex Optimization under Kurdyka-Lojasiewicz Condition", <i>AISTATS 2023</i></p>
<p>30. Chun-Yin Huang, <b>Qi Lei</b>, Xiaoxiao Li, <a href="https://arxiv.org/abs/2209.14434">Efficient Medical Image Assessment via Self-supervised Learning</a>, <i>MICCAI Workshop, DALI 2022</i>, with <b>Best Paper Honorable Mention</b></p>
<p>29. Minhao Cheng, <b>Qi Lei</b>, Pin-Yu Chen, Inderjit Dhillon, Cho-Jui Hsieh <a href="https://arxiv.org/abs/2002.06789">‘‘Cat: Customized adversarial training for improved robustness"</a>, <i>IJCAI 2022</i></p>
<p>28. Baihe Huang*, Kaixuan Huang*, Sham M. Kakade*, Jason D. Lee*, <b>Qi Lei</b>*, Runzhe Wang*, Jiaqi Yang* <a href="https://arxiv.org/abs/2107.04518">‘‘Optimal Gradient-based Algorithms for Non-concave Bandit Optimization"</a>, <i>NeurIPS 2021</i></p>
<p>27. Baihe Huang*, Kaixuan Huang*, Sham M. Kakade*, Jason D. Lee*, <b>Qi Lei</b>*, Runzhe Wang*, Jiaqi Yang* <a href="https://arxiv.org/abs/2107.06466">‘‘Going Beyond Linear RL: Sample Efficient Neural Function Approximation</a>, <i>NeurIPS 2021</i></p>
<p>26. Kurtland Chua, <b>Qi Lei</b>, Jason D. Lee, <a href="https://arxiv.org/abs/2105.02221">‘‘How fine-tuning allows for effective meta-learning"</a>, <i>NeurIPS 2021</i></p>
<p>25. Jason D Lee*, <b>Qi Lei</b>*, Nikunj Saunshi*, Jiacheng Zhuo*, <a href="https://arxiv.org/abs/2008.01064">‘‘Predicting What You Already Know Helps: Provable Self-Supervised Learning"</a>, <i>NeurIPS 2021</i></p>
<p>24. Tianle Cai*, Ruiqi Gao*, Jason D Lee*, <b>Qi Lei</b>*. <a href="https://arxiv.org/abs/2102.11203">‘‘A Theory of Label Propagation for Subpopulation Shift"</a>, <i>ICML 2021</i></p>
<p>23. <b>Qi Lei</b>, Wei Hu, Jason D. Lee. <a href="https://arxiv.org/abs/2106.12108">‘‘Near-Optimal Linear Regression under Distribution Shift"</a>, <i>ICML 2021</i></p>
<p>22. Jay Whang, <b>Qi Lei</b>, Alexandros G. Dimakis. <a href="https://arxiv.org/abs/2003.08089">“Solving Inverse Problems with a Flow-based Noise Model”</a>, <i>ICML 2021</i></p>
<p>21. <b>Qi Lei</b>*, Sai Ganesh Nagarajan*, Ioannis Panageas*, Xiao Wang*. <a href="https://arxiv.org/abs/2002.06768">“Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes”</a>, <i>AISTATS 2021</i></p>
<p>20. Simon S. Du*, Wei Hu*, Sham M. Kakade*, Jason D. Lee*, <b>Qi Lei</b>*. <a href="https://arxiv.org/abs/2002.09434">“Few-Shot Learning via Learning the Representation, Provably”</a>, <i>ICLR 2021</i></p>
<p>19. Xiao Wang, <b>Qi Lei</b>, Ioannis Panageas. “Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev”, <i>Proc. of Neural Information Processing Systems (NeurIPS), 2020</i></p>
<p>18. <b>Qi Lei</b>, Jason D. Lee, Alexandros G. Dimakis, Constantinos Daskalakis. <a href="https://arxiv.org/abs/1910.07030">“SGD Learns One-Layer Networks in WGANs”</a>, <i>Proc. of International Conference of Machine Learning (ICML) 2020</i></p>
<p>17. Jiacheng Zhuo, <b>Qi Lei</b>, Alexandros G. Dimakis, Constantine Caramanis. <a href="https://arxiv.org/abs/1910.07703">“Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls”</a>, <i>AISTATS 2020</i></p>
<p>16. <b>Qi Lei</b>, Jiacheng Zhuo, Constantine Caramanis, Inderjit S Dhillon, Alexandros G Dimakis. <a href="https://arxiv.org/abs/1906.02436">“Primal-Dual Block Frank-Wolfe”</a>, <i>Proc. of Neural Information Processing Systems (NeurIPS) 2019</i> (<a href="PDBFW.pdf">slides</a>, <a href="PDBFW_poster.pdf">poster</a>, <a href="https://github.com/CarlsonZhuo/primal_dual_frank_wolfe">code</a>)</p>
<p>15. <b>Qi Lei</b>, Ajil Jalal, Inderjit S. Dhillon, Alexandros G. Dimakis. <a href="https://arxiv.org/abs/1906.07437">“Inverting Deep Generative models, One layer at a time”</a>, <i>Proc. of Neural Information Processing Systems (NeurIPS) 2019</i> (<a href="invert_GAN_poster.pdf">poster</a>, <a href="https://github.com/cecilialeiqi/InvertGAN_LP">code</a>)</p>
<p>14. <b>Qi Lei</b>, Jinfeng Yi, Roman Vaculin, Lingfei Wu, Inderjit Dhillon. <a href="https://arxiv.org/abs/1702.03584">“Similarity Preserving
Representation Learning for Time Series Analysis”</a>, <i>The 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019</i>. <a href="https://github.com/cecilialeiqi/SPIRAL">(code)</a></p>
<p>13. <b>Qi Lei</b>, Lingfei Wu, Pin-Yu Chen, Alexandros G. Dimakis, Inderjit S. Dhillon, Michael Witbrock. <a href="https://arxiv.org/abs/1812.00151">“Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification”</a>, <i>Systems and Machine Learning (sysML). 2019</i> <a href="https://github.com/cecilialeiqi/adversarial_text">(code</a>, <a href="discrete_attack.pdf">slides)</a> </p>
<ul>
<li><p>Press coverage: <a href="https://www.nature.com/articles/d41586-019-01510-1?utm_source=twt_nnc&utm_medium=social&utm_campaign=naturenews&sf212595612=1"><Nature Story></a> <a href="https://venturebeat.com/2019/04/01/text-based-ai-models-are-vulnerable-to-paraphrasing-attacks-researchers-find/"><Vecturebeat></a> <a href="https://bdtechtalks.com/2019/04/02/ai-nlp-paraphrasing-adversarial-attacks/"><Tech Talks></a> <a href="https://www.jiqizhixin.com/articles/2019-03-27-10?from=synced&keyword=SysML%202019"><机器之心></a></p>
</li>
</ul>
<p>12. Jinfeng Yi, <b>Qi Lei</b>, Wesley Gifford, Ji Liu. <a href="https://arxiv.org/pdf/1702.06362.pdf">“Negative-Unlabeled Tensor Factorization for Location Category Inference from Inaccurate Mobility Data”</a>, <i>SIAM International Conference on Data Mining (SDM), 2019</i> <a href="https://github.com/cecilialeiqi/NUTF">(code)</a></p>
<p>11. Zhewei Yao, Amir Gholami, <b>Qi Lei</b>, Kurt Keutzer, Michael W. Mahoney. <a href="https://arxiv.org/abs/1802.08241">“Hessian-based Analysis of Large Batch Training and Robustness to Adversaries”</a>, <i>Neural Information Processing Systems (NIPS), 2018</i></p>
<p>10. Jiong Zhang, <b>Qi Lei</b>, Inderjit Dhillon, <a href="http://proceedings.mlr.press/v80/zhang18g.html">“Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization”</a>, <i>International Conference of Machine Learning (ICML), July. 2018</i></p>
<p>9. Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, <b>Qi Lei</b> and Michael Witbrock, <a href="http://proceedings.mlr.press/v84/wu18b.html">“Random Warping Series: A Random Features Method for Time-Series Embedding”</a>, <i>Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS), 2018</i></p>
<p>8. Hsiang-fu Yu, Cho-Jui Hsieh, <b>Qi Lei</b>, Inderjit Dhillon, <a href="https://arxiv.org/abs/1610.03317">“A Greedy Approach for Budgeted Maximum Inner Product Search”</a>, <i>Proc. of Neural Information Processing Systems (NIPS), 2017</i> </p>
<p>7. <b>Qi Lei</b>, Enxu Yan, Chao-yuan Wu, Pradeep Ravikumar, Inderjit Dhillon, <a href="DGPDC.pdf">“Doubly Greedy Primal-Dual Coordinate Methods for Sparse Empirical Risk Minimization”</a>, <i>Proc. of International Conference of Machine Learning (ICML), 2017</i> <a href="https://github.com/a061105/Primal-Dual-ActiveCD">(code)</a></p>
<p>6. Rashish Tandon, <b>Qi Lei</b>, Alexandros G. Dimakis, Nikos Karampatziakis, <a href="https://arxiv.org/abs/1612.03301">“Gradient Coding: Avoiding Stragglers in Distributed Learning”</a>, <i>Proc. of International Conference of Machine Learning (ICML), 2017</i> <a href="https://github.com/rashisht1/gradient_coding">(code)</a></p>
<p>5. <b>Qi Lei</b>, Kai Zhong, Inderjit. Dhillon, <a href="http://users.ices.utexas.edu/~leiqi/CPM.pdf">“Coordinate-wise Power Method”</a>, <i>Proc. of Neural Information Processing
Systems (NIPS), Dec. 2016</i> (<a href="https://github.com/cecilialeiqi/CPM">code</a>,<a href="cpm_poster.pdf">poster</a>)</p>
<p>4. Arnaud Vandaele, Nicolas Gillis, <b>Qi Lei</b>, Kai Zhong, Inderjit Dhillon, <a href="http://users.ices.utexas.edu/~leiqi/symNMF.pdf">“Efficient and Non-Convex Coordinate Descent Methods for Symmetric Nonnegative Matrix Factorization”</a>, <i>IEEE Transactions on Signal Processing 64.21 (2016): 5571-5584</i> <a href="https://www.dropbox.com/s/rh24r7cf1qxiv6k/symNMF%20-%20code%20Arnaud.zip?dl=0">(code)</a></p>
<p>3. Maria R. D'Orsogna, <b>Qi Lei</b>, Tom Chou, <a href="https://www.csun.edu/~dorsogna/mwebsite/papers/coagfrag_TC15.pdf">“First assembly times and equilibration in stochastic coagulation-fragmentation”</a>, <i>The Journal of Chemical Physics, 2015: 143.1, 014112</i></p>
<p>2. Jiazhou Chen, <b>Qi Lei</b>, Yongwei Miao, Qunsheng Peng, <a href="vectorization.pdf">“Vectorization of Line Drawing Image based on Junction Analysis”</a>, <i>Science China Information Sciences, 2014:1-14</i> <a href="vectorization/vector.zip">(code)</a></p>
<p>1. Jiazhou Chen, <b>Qi Lei</b>, Fan Zhong, Qunsheng Peng, <a href="imagePro.pdf">“Interactive Tensor Field Design Based on Line Singularities”</a>, <i>Proceedings of the 13th International CAD/Graphics, 2013</i> <a href="imagePro/imagePro.zip">(code)</a></p>
<h2>Dissertation</h2>
<p><a href="https://cecilialeiqi.github.io/LEI-DISSERTATION-2020.pdf">“Provably Effective Algorithms for Min-Max Optimization”</a> May, 2020
with <a href="https://www.oden.utexas.edu/about/news/qi-lei-wins-Outstanding-Dissertation-Award/">Oden Institute Outstanding Dissertation Award</a></p>
<h2>Patents</h2>
<p>“Method and System for General and Efficient Time Series Representation Learning via Dynamic Time Warping” <br />
with J. Yi, R. Vaculin, W. Sun</p>
<p>“Real-Time Cold Start Recommendation and Rationale within a Dialog System” <br />
with J. Yi, R. Vaculin, M. Pietro</p>
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