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← Authors

Chiyuan Zhang

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

Achievements

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+14 more ↓ 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (11) 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