← Authors

Chiyuan Zhang

44 papers · 2011–2025 · 10 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (9) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (10) πŸƒ Academic Marathon (14) 🌈 Renaissance Researcher (9) πŸ—ΊοΈ Taxonomy Completionist (11) 🌱 Topic Pioneer πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (10) πŸ† Keyword Champion 🀝 Dynamic Duo (16) πŸ—ƒοΈ Keyword Collector (113) ❓ The Questioner (5) ⚑ Prolific Year (10) πŸ“ˆ Trend Setter πŸ’Ž Century Club (44) πŸ”₯ Unstoppable (8)

Conferences

NIPS (17) ICML (10) ICLR (8) COLT (2) JMLR (2) AACL (1) ACL (1) AISTATS (1) EMNLP (1) IJCNLP (1)

Top co-authors

Badih Ghazi (16) Ravi Kumar (16) Pasin Manurangsi (13) Pritish Kamath (13) Yangsibo Huang (11) Nicholas Carlini (6) Amer Sinha (6) Daphne Ippolito (5) Katherine Lee (5) Matthew Jagielski (5)

Research topics

Differential Privacy (3)

Keywords

neural network (6) differential privacy (6) large language model (4) training datum (3) representation learning (3) manifold learning (3) deep learning (3) transfer learning (3) feature reuse (2) vision transformer (2) logical reasoning (2) semi-supervised learning (2) vector field (2) machine unlearning (2) language model (2) gradient field (2) privacy-preserving learning (2) generalization error (2) learning theory (1) adversarial robustness (1)

Papers

Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards ICML 2025
MUSE: Machine Unlearning Six-Way Evaluation for Language Models ICLR 2025
Fantastic Copyrighted Beasts and How (Not) to Generate Them ICLR 2025
Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy ICLR 2025
Balls-and-Bins Sampling for DP-SGD AISTATS 2025
On Memorization of Large Language Models in Logical Reasoning AACL 2025
MATH-Perturb: Benchmarking LLMs’ Math Reasoning Abilities against Hard Perturbations ICML 2025
Empirical Privacy Variance ICML 2025
On Memorization of Large Language Models in Logical Reasoning IJCNLP 2025
Scaling Laws for Differentially Private Language Models ICML 2025
How Private are DP-SGD Implementations? ICML 2024
Evaluating Copyright Takedown Methods for Language Models NIPS 2024
LabelDP-Pro: Learning with Label Differential Privacy via Projections ICLR 2024
On Convex Optimization with Semi-Sensitive Features COLT 2024
Scalable DP-SGD: Shuffling vs. Poisson Subsampling NIPS 2024
Regression with Label Differential Privacy ICLR 2023
Quantifying Memorization Across Neural Language Models ICLR 2023
Can Neural Network Memorization Be Localized? ICML 2023
Sparsity-Preserving Differentially Private Training of Large Embedding Models NIPS 2023
User-Level Differential Privacy With Few Examples Per User NIPS 2023
Counterfactual Memorization in Neural Language Models NIPS 2023
Optimal Unbiased Randomizers for Regression with Label Differential Privacy NIPS 2023
Ticketed Learning–Unlearning Schemes COLT 2023
On User-Level Private Convex Optimization ICML 2023
Measuring Forgetting of Memorized Training Examples ICLR 2023
Are All Layers Created Equal? JMLR 2022
Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures NIPS 2022
The Privacy Onion Effect: Memorization is Relative NIPS 2022
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation NIPS 2022
Deduplicating Training Data Makes Language Models Better ACL 2022
Just Fine-tune Twice: Selective Differential Privacy for Large Language Models EMNLP 2022
Deep Learning with Label Differential Privacy NIPS 2021
Do Vision Transformers See Like Convolutional Neural Networks? NIPS 2021
Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers ICML 2021
Characterizing Structural Regularities of Labeled Data in Overparameterized Models ICML 2021
What is being transferred in transfer learning? NIPS 2020
Identity Crisis: Memorization and Generalization Under Extreme Overparameterization ICLR 2020
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation NIPS 2020
Transfusion: Understanding Transfer Learning for Medical Imaging NIPS 2019
Machine Theory of Mind ICML 2018
Learning with a Wasserstein Loss NIPS 2015
Parallel Vector Field Embedding JMLR 2013
Multi-task Vector Field Learning NIPS 2012
Semi-supervised Regression via Parallel Field Regularization NIPS 2011