| 2026 | EACL | BLUR: A Bi-Level Optimization Approach for LLM Unlearning. | Hadi Reisizadeh, Jinghan Jia, Zhiqi Bu, Bhanukiran Vinzamuri, Anil Ramakrishna, Kai-Wei Chang, Volkan Cevher, Sijia Liu, Mingyi Hong |
| 2025 | EMNLP | LUME: LLM Unlearning with Multitask Evaluations. | Anil Ramakrishna, Yixin Wan, Xiaomeng Jin, Kai-Wei Chang, Zhiqi Bu, Bhanukiran Vinzamuri, Volkan Cevher, Mingyi Hong, Rahul Gupta |
| 2025 | ICLR | DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction. | Xinwei Zhang, Zhiqi Bu, Borja Balle, Mingyi Hong, Meisam Razaviyayn, Vahab Mirrokni |
| 2025 | ICLR | Gradient descent with generalized Newton's method. | Zhiqi Bu, Shiyun Xu |
| 2025 | ICLR | Towards hyperparameter-free optimization with differential privacy. | Ruixuan Liu, Zhiqi Bu |
| 2025 | ICLR | MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation. | Lu Li, Tianyu Zhang, Zhiqi Bu, Suyuchen Wang, Huan He, Jie Fu, Yonghui Wu, Jiang Bian, Yong Chen, Yoshua Bengio |
| 2025 | NAACL | Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate. | Xiaomeng Jin, Zhiqi Bu, Bhanukiran Vinzamuri, Anil Ramakrishna, Kai-Wei Chang, Volkan Cevher, Mingyi Hong |
| 2024 | ICLR | Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. | Yingyu Lin, Yian Ma, Yu-Xiang Wang, Rachel Redberg, Zhiqi Bu |
| 2024 | ICLR | Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach. | Xinwei Zhang, Zhiqi Bu, Steven Wu, Mingyi Hong |
| 2024 | ICML | Differentially Private Bias-Term Fine-tuning of Foundation Models. | Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis |
| 2023 | ICML | Differentially Private Optimization on Large Model at Small Cost. | Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis |
| 2023 | PAKDD | MISNN: Multiple Imputation via Semi-parametric Neural Networks. | Zhiqi Bu, Zongyu Dai, Yiliang Zhang, Qi Long |
| 2022 | ACML | Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data. | Zongyu Dai, Zhiqi Bu, Qi Long |
| 2021 | AISTATS | A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks. | Zhiqi Bu, Shiyun Xu, Kan Chen |
| 2021 | AISTATS | DebiNet: Debiasing Linear Models with Nonlinear Overparameterized Neural Networks. | Shiyun Xu, Zhiqi Bu |
| 2021 | AISTATS | Efficient Designs Of SLOPE Penalty Sequences In Finite Dimension. | Yiliang Zhang, Zhiqi Bu |
| 2021 | ICML | Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. | Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni |
| 2021 | ICMLA | Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems. | Zongyu Dai, Zhiqi Bu, Qi Long |