Zheng Xu
29 papers · 2016–2025 · 9 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (9) π Academic Marathon (9) π§ Keyword Pioneer π Interdisciplinary Bridge π Cross-Pollinator (8)
π
Cross-Pollinator
(8)
π
Renaissance Researcher
(8)
πΊοΈ
Taxonomy Completionist
(43)
π€
Dynamic Duo
(12)
π
Grand Slam
β
The Questioner
(2)
β‘
Prolific Year
(6)
ποΈ
Keyword Collector
(102)
π₯
Unstoppable
(10)
π
Century Club
(29)
π
Conference Pioneer
π
Trend Setter
Conferences
ICML (10)
NIPS (6)
EMNLP (3)
ICLR (3)
ACL (2)
CVPR (2)
AAAI (1)
AISTATS (1)
NAACL (1)
Top co-authors
Research topics
Keywords
differential privacy
(7)
federated learning
(7)
stochastic gradient descent
(4)
large language model
(3)
neural network optimization
(2)
adversarial training
(2)
skip connection
(2)
adversarial attack
(2)
language model
(2)
model compression
(2)
convex optimization
(2)
convergence analysis
(2)
alternating direction method of multiplier
(2)
domain adaptation
(1)
non-convex optimization
(1)
model quantization
(1)
nonconvex optimization
(1)
neural network training
(1)
convergence acceleration
(1)
batch normalization
(1)
Papers
Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications
ACL 2025
Synthesizing Privacy-Preserving Text Data via Finetuning *without* Finetuning Billion-Scale LLMs
ICML 2025
Debiasing Federated Learning with Correlated Client Participation
ICLR 2025
Can Public Large Language Models Help Private Cross-device Federated Learning?
NAACL 2024
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models
EMNLP 2024
User Inference Attacks on Large Language Models
EMNLP 2024
A Hassle-free Algorithm for Strong Differential Privacy in Federated Learning Systems
EMNLP 2024
Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy
ICML 2024
Safe and Robust Subgame Exploitation in Imperfect Information Games
ICML 2024
Privacy-Preserving Instructions for Aligning Large Language Models
ICML 2024
(Amplified) Banded Matrix Factorization: A unified approach to private training
NIPS 2023
Federated Learning of Gboard Language Models with Differential Privacy
ACL 2023
Learning To Generate Image Embeddings With User-Level Differential Privacy
CVPR 2023
Beyond Uniform Lipschitz Condition in Differentially Private Optimization
ICML 2023
On the Convergence of Federated Averaging with Cyclic Client Participation
ICML 2023
Efficient Subgame Refinement for Extensive-form Games
NIPS 2023
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions
ICLR 2022
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
NIPS 2021
Practical and Private (Deep) Learning Without Sampling or Shuffling
ICML 2021
Universal Adversarial Training
AAAI 2020
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent
ICML 2020
Adversarial training for free!
NIPS 2019
Visualizing the Loss Landscape of Neural Nets
NIPS 2018
Stabilizing Adversarial Nets with Prediction Methods
ICLR 2018
Adaptive ADMM with Spectral Penalty Parameter Selection
AISTATS 2017
Training Quantized Nets: A Deeper Understanding
NIPS 2017
Adaptive Consensus ADMM for Distributed Optimization
ICML 2017
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation
CVPR 2017
Training Neural Networks Without Gradients: A Scalable ADMM Approach
ICML 2016