Zhiqi Bu - RankMe

72 papers A* 16A 3C 2Journal 43Unranked 8

Year	Rank	Type	Title / Venue / Authors
2026	—	`conf`	BLUR: A Bi-Level Optimization Approach for LLM Unlearning. EACL (Volume 1: Long Papers) Hadi Reisizadeh, Jinghan Jia, Zhiqi Bu, Bhanukiran Vinzamuri, Anil Ramakrishna, Kai-Wei Chang, Volkan Cevher, Sijia Liu, Mingyi Hong
2026	J	`jnl`	Convex Dominance in Deep Learning I: A Scaling Law of Loss and Learning Rate. CoRR Zhiqi Bu, Shiyun Xu, Jialin Mao
2026	J	`jnl`	To 2:4 Sparsity and Beyond: Neuron-level Activation Function to Accelerate LLM Pre-Training. CoRR Meghana Madhyastha, Daniel Haziza, Jesse Cai, Newsha Ardalani, Zhiqi Bu, Carole-Jean Wu
2025	J	`jnl`	A Hessian-informed hyperparameter optimization for differential learning rate. CoRR Shiyun Xu, Zhiqi Bu, Yiliang Zhang, Ian J. Barnett
2025	J	`jnl`	Adaptive parameter-efficient fine-tuning via Hessian-informed subset selection. CoRR Shiyun Xu, Zhiqi Bu
2025	J	`jnl`	BLUR: A Bi-Level Optimization Approach for LLM Unlearning. CoRR Hadi Reisizadeh, Jinghan Jia, Zhiqi Bu, Bhanukiran Vinzamuri, Anil Ramakrishna, Kai-Wei Chang, Volkan Cevher, Sijia Liu, Mingyi Hong
2025	J	`jnl`	Deep Progressive Training: scaling up depth capacity of zero/one-layer models. CoRR Zhiqi Bu
2025	A*	`conf`	DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction. ICLR Xinwei Zhang, Zhiqi Bu, Borja Balle, Mingyi Hong, Meisam Razaviyayn, Vahab Mirrokni
2025	A*	`conf`	Gradient descent with generalized Newton's method. ICLR Zhiqi Bu, Shiyun Xu
2025	—	`conf`	LUME: LLM Unlearning with Multitask Evaluations. EMNLP (Findings) Anil Ramakrishna, Yixin Wan, Xiaomeng Jin, Kai-Wei Chang, Zhiqi Bu, Bhanukiran Vinzamuri, Volkan Cevher, Mingyi Hong, Rahul Gupta
2025	J	`jnl`	LUME: LLM Unlearning with Multitask Evaluations. CoRR Anil Ramakrishna, Yixin Wan, Xiaomeng Jin, Kai-Wei Chang, Zhiqi Bu, Bhanukiran Vinzamuri, Volkan Cevher, Mingyi Hong, Rahul Gupta
2025	A*	`conf`	MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation. ICLR Lu Li, Tianyu Zhang, Zhiqi Bu, Suyuchen Wang, Huan He, Jie Fu, Yonghui Wu, Jiang Bian, Yong Chen, Yoshua Bengio
2025	J	`jnl`	SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models. CoRR Anil Ramakrishna, Yixin Wan, Xiaomeng Jin, Kai-Wei Chang, Zhiqi Bu, Bhanukiran Vinzamuri, Volkan Cevher, Mingyi Hong, Rahul Gupta
2025	A*	`conf`	Towards hyperparameter-free optimization with differential privacy. ICLR Ruixuan Liu, Zhiqi Bu
2025	J	`jnl`	Towards hyperparameter-free optimization with differential privacy. CoRR Zhiqi Bu, Ruixuan Liu
2025	—	`conf`	Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate. NAACL (Long Papers) Xiaomeng Jin, Zhiqi Bu, Bhanukiran Vinzamuri, Anil Ramakrishna, Kai-Wei Chang, Volkan Cevher, Mingyi Hong
2024	J	`jnl`	Automatic gradient descent with generalized Newton's method. CoRR Zhiqi Bu, Shiyun Xu
2024	A*	`conf`	DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction. NeurIPS Xinwei Zhang, Zhiqi Bu, Mingyi Hong, Meisam Razaviyayn
2024	J	`jnl`	DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction. CoRR Xinwei Zhang, Zhiqi Bu, Mingyi Hong, Meisam Razaviyayn
2024	J	`jnl`	DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction. CoRR Xinwei Zhang, Zhiqi Bu, Borja Balle, Mingyi Hong, Meisam Razaviyayn, Vahab Mirrokni
2024	A*	`conf`	Differentially Private Bias-Term Fine-tuning of Foundation Models. ICML Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis
2024	A*	`conf`	Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach. ICLR Xinwei Zhang, Zhiqi Bu, Steven Wu, Mingyi Hong
2024	J	`jnl`	MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation. CoRR Lu Li, Tianyu Zhang, Zhiqi Bu, Suyuchen Wang, Huan He, Jie Fu, Yonghui Wu, Jiang Bian, Yong Chen, Yoshua Bengio
2024	A*	`conf`	Pre-training Differentially Private Models with Limited Public Data. NeurIPS Zhiqi Bu, Xinwei Zhang, Sheng Zha, Mingyi Hong, George Karypis
2024	J	`jnl`	Pre-training Differentially Private Models with Limited Public Data. CoRR Zhiqi Bu, Xinwei Zhang, Mingyi Hong, Sheng Zha, George Karypis
2024	A*	`conf`	Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. ICLR Yingyu Lin, Yian Ma, Yu-Xiang Wang, Rachel Redberg, Zhiqi Bu
2024	J	`jnl`	Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate. CoRR Zhiqi Bu, Xiaomeng Jin, Bhanukiran Vinzamuri, Anil Ramakrishna, Kai-Wei Chang, Volkan Cevher, Mingyi Hong
2023	A*	`conf`	Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger. NeurIPS Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis
2023	J	`jnl`	Coupling public and private gradient provably helps optimization. CoRR Ruixuan Liu, Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis
2023	A*	`conf`	Differentially Private Optimization on Large Model at Small Cost. ICML Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis
2023	J	`jnl`	Differentially Private Optimizers Can Learn Adversarially Robust Models. Trans. Mach. Learn. Res. Zhiqi Bu, Yuan Zhang
2023	J	`jnl`	Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach. CoRR Xinwei Zhang, Zhiqi Bu, Zhiwei Steven Wu, Mingyi Hong
2023	—	`conf`	MISNN: Multiple Imputation via Semi-parametric Neural Networks. PAKDD (1) Zhiqi Bu, Zongyu Dai, Yiliang Zhang, Qi Long
2023	J	`jnl`	MISNN: Multiple Imputation via Semi-parametric Neural Networks. CoRR Zhiqi Bu, Zongyu Dai, Yiliang Zhang, Qi Long
2023	J	`jnl`	On the Convergence and Calibration of Deep Learning with Differential Privacy. Trans. Mach. Learn. Res. Zhiqi Bu, Hua Wang, Zongyu Dai, Qi Long
2023	J	`jnl`	On the accuracy and efficiency of group-wise clipping in differentially private optimization. CoRR Zhiqi Bu, Ruixuan Liu, Yu-Xiang Wang, Sheng Zha, George Karypis
2023	—	`conf`	Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity. ECML/PKDD (3) Shiyun Xu, Zhiqi Bu, Pratik Chaudhari, Ian J. Barnett
2023	J	`jnl`	Zero redundancy distributed learning with differential privacy. CoRR Zhiqi Bu, Justin Chiu, Ruixuan Liu, Sheng Zha, George Karypis
2022	J	`jnl`	Accelerating Adversarial Perturbation by 50% with Semi-backward Propagation. CoRR Zhiqi Bu
2022	J	`jnl`	Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger. CoRR Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis
2022	—	`conf`	Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability. ECML/PKDD (4) Qiyiwen Zhang, Zhiqi Bu, Kan Chen, Qi Long
2022	J	`jnl`	Differentially Private Bias-Term only Fine-tuning of Foundation Models. CoRR Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis
2022	J	`jnl`	Differentially Private Optimization on Large Model at Small Cost. CoRR Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis
2022	J	`jnl`	Differentially Private Optimizers Can Learn Adversarially Robust Models. CoRR Yuan Zhang, Zhiqi Bu
2022	C	`conf`	Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data. ACML Zongyu Dai, Zhiqi Bu, Qi Long
2022	J	`jnl`	Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data. CoRR Zongyu Dai, Zhiqi Bu, Qi Long
2022	A*	`conf`	Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy. NeurIPS Zhiqi Bu, Jialin Mao, Shiyun Xu
2022	J	`jnl`	Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy. CoRR Zhiqi Bu, Jialin Mao, Shiyun Xu
2022	J	`jnl`	Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity. CoRR Shiyun Xu, Zhiqi Bu, Pratik Chaudhari, Ian J. Barnett
2021	A	`conf`	A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks. AISTATS Zhiqi Bu, Shiyun Xu, Kan Chen
2021	A*	`conf`	Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. ICML Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni
2021	J	`jnl`	Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. CoRR Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni
2021	J	`jnl`	Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing. IEEE Trans. Inf. Theory Zhiqi Bu, Jason M. Klusowski, Cynthia Rush, Weijie J. Su
2021	—	`conf`	Asymptotic Statistical Analysis of Sparse Group LASSO via Approximate Message Passing. ECML/PKDD (3) Kan Chen, Zhiqi Bu, Shiyun Xu
2021	J	`jnl`	Characterizing the SLOPE Trade-off: A Variational Perspective and the Donoho-Tanner Limit. CoRR Zhiqi Bu, Jason M. Klusowski, Cynthia Rush, Weijie J. Su
2021	A	`conf`	DebiNet: Debiasing Linear Models with Nonlinear Overparameterized Neural Networks. AISTATS Shiyun Xu, Zhiqi Bu
2021	J	`jnl`	Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability. CoRR Qiyiwen Zhang, Zhiqi Bu, Kan Chen, Qi Long
2021	A	`conf`	Efficient Designs Of SLOPE Penalty Sequences In Finite Dimension. AISTATS Yiliang Zhang, Zhiqi Bu
2021	J	`jnl`	Efficient Designs of SLOPE Penalty Sequences in Finite Dimension. CoRR Yiliang Zhang, Zhiqi Bu
2021	A*	`conf`	Fast and Memory Efficient Differentially Private-SGD via JL Projections. NeurIPS Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
2021	J	`jnl`	Fast and Memory Efficient Differentially Private-SGD via JL Projections. CoRR Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
2021	C	`conf`	Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems. ICMLA Zongyu Dai, Zhiqi Bu, Qi Long
2021	J	`jnl`	Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems. CoRR Zongyu Dai, Zhiqi Bu, Qi Long
2021	J	`jnl`	On the Convergence of Deep Learning with Differential Privacy. CoRR Zhiqi Bu, Hua Wang, Qi Long, Weijie J. Su
2021	—	`conf`	Privacy Amplification via Iteration for Shuffled and Online PNSGD. ECML/PKDD (2) Matteo Sordello, Zhiqi Bu, Jinshuo Dong
2021	J	`jnl`	Privacy Amplification via Iteration for Shuffled and Online PNSGD. CoRR Matteo Sordello, Zhiqi Bu, Jinshuo Dong
2020	J	`jnl`	A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks. CoRR Zhiqi Bu, Shiyun Xu, Kan Chen
2020	A*	`conf`	The Complete Lasso Tradeoff Diagram. NeurIPS Hua Wang, Yachong Yang, Zhiqi Bu, Weijie J. Su
2020	J	`jnl`	The Complete Lasso Tradeoff Diagram. CoRR Hua Wang, Yachong Yang, Zhiqi Bu, Weijie J. Su
2019	A*	`conf`	Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing. NeurIPS Zhiqi Bu, Jason M. Klusowski, Cynthia Rush, Weijie J. Su
2019	J	`jnl`	Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing. CoRR Zhiqi Bu, Jason M. Klusowski, Cynthia Rush, Weijie J. Su
2019	J	`jnl`	Deep Learning with Gaussian Differential Privacy. CoRR Zhiqi Bu, Jinshuo Dong, Qi Long, Weijie J. Su