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Zhiqi Bu
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2020 – today
- 2024
- [c20]Yingyu Lin, Yian Ma, Yu-Xiang Wang, Rachel Redberg, Zhiqi Bu:
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. ICLR 2024 - [c19]Xinwei Zhang, Zhiqi Bu, Steven Wu, Mingyi Hong:
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach. ICLR 2024 - [c18]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Bias-Term Fine-tuning of Foundation Models. ICML 2024 - [i30]Zhiqi Bu, Xinwei Zhang, Mingyi Hong, Sheng Zha, George Karypis:
Pre-training Differentially Private Models with Limited Public Data. CoRR abs/2402.18752 (2024) - [i29]Lu Li, Tianyu Zhang, Zhiqi Bu, Suyuchen Wang, Huan He, Jie Fu, Yonghui Wu, Jiang Bian, Yong Chen, Yoshua Bengio:
MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation. CoRR abs/2406.07529 (2024) - [i28]Zhiqi Bu, Shiyun Xu:
Automatic gradient descent with generalized Newton's method. CoRR abs/2407.02772 (2024) - [i27]Xinwei Zhang, Zhiqi Bu, Mingyi Hong, Meisam Razaviyayn:
DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction. CoRR abs/2408.13460 (2024) - [i26]Xinwei Zhang, Zhiqi Bu, Borja Balle, Mingyi Hong, Meisam Razaviyayn, Vahab Mirrokni:
DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction. CoRR abs/2410.03883 (2024) - 2023
- [j3]Zhiqi Bu, Hua Wang, Zongyu Dai, Qi Long:
On the Convergence and Calibration of Deep Learning with Differential Privacy. Trans. Mach. Learn. Res. 2023 (2023) - [j2]Zhiqi Bu, Yuan Zhang:
Differentially Private Optimizers Can Learn Adversarially Robust Models. Trans. Mach. Learn. Res. 2023 (2023) - [c17]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Optimization on Large Model at Small Cost. ICML 2023: 3192-3218 - [c16]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger. NeurIPS 2023 - [c15]Zhiqi Bu, Zongyu Dai, Yiliang Zhang, Qi Long:
MISNN: Multiple Imputation via Semi-parametric Neural Networks. PAKDD (1) 2023: 430-442 - [c14]Shiyun Xu, Zhiqi Bu, Pratik Chaudhari, Ian J. Barnett:
Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity. ECML/PKDD (3) 2023: 343-359 - [i25]Zhiqi Bu, Zongyu Dai, Yiliang Zhang, Qi Long:
MISNN: Multiple Imputation via Semi-parametric Neural Networks. CoRR abs/2305.01794 (2023) - [i24]Ruixuan Liu, Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Coupling public and private gradient provably helps optimization. CoRR abs/2310.01304 (2023) - [i23]Zhiqi Bu, Ruixuan Liu, Yu-Xiang Wang, Sheng Zha, George Karypis:
On the accuracy and efficiency of group-wise clipping in differentially private optimization. CoRR abs/2310.19215 (2023) - [i22]Zhiqi Bu, Justin Chiu, Ruixuan Liu, Sheng Zha, George Karypis:
Zero redundancy distributed learning with differential privacy. CoRR abs/2311.11822 (2023) - [i21]Xinwei Zhang, Zhiqi Bu, Zhiwei Steven Wu, Mingyi Hong:
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach. CoRR abs/2311.14632 (2023) - 2022
- [c13]Zongyu Dai, Zhiqi Bu, Qi Long:
Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data. ACML 2022: 265-279 - [c12]Zhiqi Bu, Jialin Mao, Shiyun Xu:
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy. NeurIPS 2022 - [c11]Qiyiwen Zhang, Zhiqi Bu, Kan Chen, Qi Long:
Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability. ECML/PKDD (4) 2022: 604-619 - [i20]Shiyun Xu, Zhiqi Bu, Pratik Chaudhari, Ian J. Barnett:
Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity. CoRR abs/2202.12482 (2022) - [i19]Zhiqi Bu, Jialin Mao, Shiyun Xu:
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy. CoRR abs/2205.10683 (2022) - [i18]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger. CoRR abs/2206.07136 (2022) - [i17]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Bias-Term only Fine-tuning of Foundation Models. CoRR abs/2210.00036 (2022) - [i16]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Optimization on Large Model at Small Cost. CoRR abs/2210.00038 (2022) - [i15]Zhiqi Bu:
Accelerating Adversarial Perturbation by 50% with Semi-backward Propagation. CoRR abs/2211.04973 (2022) - [i14]Yuan Zhang, Zhiqi Bu:
Differentially Private Optimizers Can Learn Adversarially Robust Models. CoRR abs/2211.08942 (2022) - [i13]Zongyu Dai, Zhiqi Bu, Qi Long:
Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data. CoRR abs/2211.13297 (2022) - 2021
- [j1]Zhiqi Bu, Jason M. Klusowski
, Cynthia Rush
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing. IEEE Trans. Inf. Theory 67(1): 506-537 (2021) - [c10]Shiyun Xu, Zhiqi Bu:
DebiNet: Debiasing Linear Models with Nonlinear Overparameterized Neural Networks. AISTATS 2021: 3097-3105 - [c9]Zhiqi Bu, Shiyun Xu, Kan Chen:
A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks. AISTATS 2021: 3187-3195 - [c8]Yiliang Zhang, Zhiqi Bu:
Efficient Designs Of SLOPE Penalty Sequences In Finite Dimension. AISTATS 2021: 3277-3285 - [c7]Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni:
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. ICML 2021: 8227-8237 - [c6]Zongyu Dai, Zhiqi Bu, Qi Long:
Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems. ICMLA 2021: 791-798 - [c5]Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat:
Fast and Memory Efficient Differentially Private-SGD via JL Projections. NeurIPS 2021: 19680-19691 - [c4]Kan Chen, Zhiqi Bu, Shiyun Xu:
Asymptotic Statistical Analysis of Sparse Group LASSO via Approximate Message Passing. ECML/PKDD (3) 2021: 510-526 - [c3]Matteo Sordello, Zhiqi Bu, Jinshuo Dong:
Privacy Amplification via Iteration for Shuffled and Online PNSGD. ECML/PKDD (2) 2021: 796-813 - [i12]Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat:
Fast and Memory Efficient Differentially Private-SGD via JL Projections. CoRR abs/2102.03013 (2021) - [i11]Yiliang Zhang, Zhiqi Bu:
Efficient Designs of SLOPE Penalty Sequences in Finite Dimension. CoRR abs/2102.07211 (2021) - [i10]Zhiqi Bu, Jason M. Klusowski, Cynthia Rush, Weijie J. Su:
Characterizing the SLOPE Trade-off: A Variational Perspective and the Donoho-Tanner Limit. CoRR abs/2105.13302 (2021) - [i9]Zhiqi Bu, Hua Wang, Qi Long, Weijie J. Su:
On the Convergence of Deep Learning with Differential Privacy. CoRR abs/2106.07830 (2021) - [i8]Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni:
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. CoRR abs/2106.09680 (2021) - [i7]Matteo Sordello, Zhiqi Bu, Jinshuo Dong:
Privacy Amplification via Iteration for Shuffled and Online PNSGD. CoRR abs/2106.11767 (2021) - [i6]Qiyiwen Zhang, Zhiqi Bu, Kan Chen, Qi Long:
Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability. CoRR abs/2107.08461 (2021) - [i5]Zongyu Dai, Zhiqi Bu, Qi Long:
Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems. CoRR abs/2112.11507 (2021) - 2020
- [c2]Hua Wang, Yachong Yang, Zhiqi Bu, Weijie J. Su:
The Complete Lasso Tradeoff Diagram. NeurIPS 2020 - [i4]Hua Wang, Yachong Yang, Zhiqi Bu, Weijie J. Su:
The Complete Lasso Tradeoff Diagram. CoRR abs/2007.11078 (2020) - [i3]Zhiqi Bu, Shiyun Xu, Kan Chen:
A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks. CoRR abs/2010.13165 (2020)
2010 – 2019
- 2019
- [c1]Zhiqi Bu, Jason M. Klusowski, Cynthia Rush, Weijie J. Su:
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing. NeurIPS 2019: 9361-9371 - [i2]Zhiqi Bu, Jason M. Klusowski, Cynthia Rush, Weijie J. Su:
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing. CoRR abs/1907.07502 (2019) - [i1]Zhiqi Bu, Jinshuo Dong, Qi Long, Weijie J. Su:
Deep Learning with Gaussian Differential Privacy. CoRR abs/1911.11607 (2019)
Coauthor Index
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last updated on 2024-11-13 23:51 CET by the dblp team
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