Deep Learning with Gaussian Differential Privacy. - Zhiqi Bu; Jinshuo Dong; Qi Long; Weijie J. Su (CoRR, 2019)

The Complete Lasso Tradeoff Diagram. - Hua Wang; Yachong Yang; Zhiqi Bu; Weijie J. Su (NeurIPS, 2020)

A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks. - Zhiqi Bu; Shiyun Xu; Kan Chen (AISTATS, 2021)

DebiNet: Debiasing Linear Models with Nonlinear Overparameterized Neural Networks. - Shiyun Xu; Zhiqi Bu (AISTATS, 2021)

Efficient Designs Of SLOPE Penalty Sequences In Finite Dimension. - Yiliang Zhang; Zhiqi Bu (AISTATS, 2021)

Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. - Harsha Nori; Rich Caruana; Zhiqi Bu; Judy Hanwen Shen; Janardhan Kulkarni (ICML, 2021)

Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems. - Zongyu Dai; Zhiqi Bu; Qi Long (ICMLA, 2021)

Fast and Memory Efficient Differentially Private-SGD via JL Projections. - Zhiqi Bu; Sivakanth Gopi; Janardhan Kulkarni; Yin Tat Lee; Judy Hanwen Shen; Uthaipon Tantipongpipat (NeurIPS, 2021)

Asymptotic Statistical Analysis of Sparse Group LASSO via Approximate Message Passing. - Kan Chen; Zhiqi Bu; Shiyun Xu (ECML/PKDD (3), 2021)

Privacy Amplification via Iteration for Shuffled and Online PNSGD. - Matteo Sordello; Zhiqi Bu; Jinshuo Dong (ECML/PKDD (2), 2021)

Characterizing the SLOPE Trade-off: A Variational Perspective and the Donoho-Tanner Limit. - Zhiqi Bu; Jason M. Klusowski; Cynthia Rush; Weijie J. Su (CoRR, 2021)

On the Convergence of Deep Learning with Differential Privacy. - Zhiqi Bu; Hua Wang; Qi Long; Weijie J. Su (CoRR, 2021)

Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing. - Zhiqi Bu; Jason M. Klusowski; Cynthia Rush; Weijie J. Su (IEEE Trans. Inf. Theory, 2021)

Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data. - Zongyu Dai; Zhiqi Bu; Qi Long (ACML, 2022)

Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy. - Zhiqi Bu; Jialin Mao; Shiyun Xu (NeurIPS, 2022)

Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability. - Qiyiwen Zhang; Zhiqi Bu; Kan Chen; Qi Long (ECML/PKDD (4), 2022)

Differentially Private Bias-Term only Fine-tuning of Foundation Models. - Zhiqi Bu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (CoRR, 2022)

Accelerating Adversarial Perturbation by 50% with Semi-backward Propagation. - Zhiqi Bu (CoRR, 2022)

Differentially Private Optimization on Large Model at Small Cost. - Zhiqi Bu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (ICML, 2023)

Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger. - Zhiqi Bu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (NeurIPS, 2023)

MISNN: Multiple Imputation via Semi-parametric Neural Networks. - Zhiqi Bu; Zongyu Dai; Yiliang Zhang; Qi Long (PAKDD (1), 2023)

Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity. - Shiyun Xu; Zhiqi Bu; Pratik Chaudhari; Ian J. Barnett (ECML/PKDD (3), 2023)

Coupling public and private gradient provably helps optimization. - Ruixuan Liu; Zhiqi Bu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (CoRR, 2023)

On the accuracy and efficiency of group-wise clipping in differentially private optimization. - Zhiqi Bu; Ruixuan Liu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (CoRR, 2023)

Zero redundancy distributed learning with differential privacy. - Zhiqi Bu; Justin Chiu; Ruixuan Liu; Sheng Zha; George Karypis (CoRR, 2023)

On the Convergence and Calibration of Deep Learning with Differential Privacy. - Zhiqi Bu; Hua Wang; Zongyu Dai; Qi Long (Trans. Mach. Learn. Res., 2023)

Differentially Private Optimizers Can Learn Adversarially Robust Models. - Zhiqi Bu; Yuan Zhang (Trans. Mach. Learn. Res., 2023)

Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. - Yingyu Lin; Yian Ma; Yu-Xiang Wang 003; Rachel Redberg; Zhiqi Bu (ICLR, 2024)

Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach. - Xinwei Zhang 001; Zhiqi Bu; Steven Wu 001; Mingyi Hong 001 (ICLR, 2024)

Differentially Private Bias-Term Fine-tuning of Foundation Models. - Zhiqi Bu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (ICML, 2024)

DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction. - Xinwei Zhang 001; Zhiqi Bu; Mingyi Hong 001; Meisam Razaviyayn (NeurIPS, 2024)

Pre-training Differentially Private Models with Limited Public Data. - Zhiqi Bu; Xinwei Zhang 001; Sheng Zha; Mingyi Hong 001; George Karypis (NeurIPS, 2024)

Automatic gradient descent with generalized Newton's method. - Zhiqi Bu; Shiyun Xu (CoRR, 2024)

DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction. - Xinwei Zhang 001; Zhiqi Bu; Borja Balle; Mingyi Hong 001; Meisam Razaviyayn; Vahab Mirrokni (ICLR, 2025)

Gradient descent with generalized Newton's method. - Zhiqi Bu; Shiyun Xu (ICLR, 2025)

MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation. - Lu Li; Tianyu Zhang; Zhiqi Bu; Suyuchen Wang; Huan He; Jie Fu 001; Yonghui Wu; Jiang Bian 002; Yong Chen 016; Yoshua Bengio (ICLR, 2025)

Towards hyperparameter-free optimization with differential privacy. - Ruixuan Liu; Zhiqi Bu (ICLR, 2025)

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 001 (NAACL (Long Papers), 2025)

A Hessian-informed hyperparameter optimization for differential learning rate. - Shiyun Xu; Zhiqi Bu; Yiliang Zhang; Ian J. Barnett (CoRR, 2025)

LUME: LLM Unlearning with Multitask Evaluations. - Anil Ramakrishna; Yixin Wan; Xiaomeng Jin; Kai-Wei Chang; Zhiqi Bu; Bhanukiran Vinzamuri; Volkan Cevher; Mingyi Hong 001; Rahul Gupta 001 (CoRR, 2025)

SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models. - Anil Ramakrishna; Yixin Wan; Xiaomeng Jin; Kai-Wei Chang; Zhiqi Bu; Bhanukiran Vinzamuri; Volkan Cevher; Mingyi Hong 001; Rahul Gupta 001 (CoRR, 2025)

Adaptive parameter-efficient fine-tuning via Hessian-informed subset selection. - Shiyun Xu; Zhiqi Bu (CoRR, 2025)

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 001; Mingyi Hong 001 (CoRR, 2025)