Zhiqi Bu
25 papers · 2019–2026 · 10 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (6) π Interdisciplinary Bridge π§ Keyword Pioneer π Conference Polyglot (9) π£ Hot Topic Early Bird
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Interdisciplinary Bridge
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Conference Polyglot
(9)
π
Academic Marathon
(6)
π
Keyword Champion
(3)
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Triple Crown
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Deep Specialist
(10)
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(87)
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(5)
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Century Club
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Unstoppable
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Conferences
NIPS (7)
ICLR (6)
AISTATS (3)
ICML (3)
ACL (1)
ACML (1)
EACL (1)
EMNLP (1)
NAACL (1)
SEMEVAL (1)
Top co-authors
Research topics
Keywords
differential privacy
(7)
machine unlearning
(5)
gradient clipping
(3)
large language model
(3)
private training
(3)
deep learning
(3)
model editing
(3)
stochastic gradient descent
(3)
sorted l1 penalty
(2)
privacy preservation
(2)
overparameterized neural network
(2)
high-dimensional regression
(2)
privacy-preserving learning
(2)
data privacy
(2)
personally identifiable information
(2)
model architecture
(1)
model pretraining
(1)
variational inference
(1)
feature selection
(1)
text classification
(1)
Papers
BLUR: A Bi-Level Optimization Approach for LLM Unlearning
EACL 2026
LUME: LLM Unlearning with Multitask Evaluations
EMNLP 2025
SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models
ACL 2025
Towards hyperparameter-free optimization with differential privacy
ICLR 2025
DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction
ICLR 2025
MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation
ICLR 2025
Gradient descent with generalized Newtonβs method
ICLR 2025
Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate
NAACL 2025
SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models
SEMEVAL 2025
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach
ICLR 2024
Differentially Private Bias-Term Fine-tuning of Foundation Models
ICML 2024
DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction
NIPS 2024
Pre-training Differentially Private Models with Limited Public Data
NIPS 2024
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy
ICLR 2024
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger
NIPS 2023
Differentially Private Optimization on Large Model at Small Cost
ICML 2023
Multiple Imputation with Neural Network Gaussian
Process for High-dimensional Incomplete Data
ACML 2022
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy
NIPS 2022
Efficient Designs Of SLOPE Penalty Sequences In Finite Dimension
AISTATS 2021
DebiNet: Debiasing Linear Models with Nonlinear Overparameterized Neural Networks
AISTATS 2021
A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks
AISTATS 2021
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
ICML 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections
NIPS 2021
The Complete Lasso Tradeoff Diagram
NIPS 2020
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing
NIPS 2019