Da Yu
12 papers · 2019–2025 · 7 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (22) π Academic Marathon Β· Sprint π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
π§¬
Topic Evolution
π§
Keyword Pioneer
π
Trend Setter
π
Century Club Β· Bronze
π₯
Unstoppable Β· Bronze
π
Renaissance Researcher
(5)
β
The Questioner
ποΈ
Keyword Collector
(62)
Top co-authors
Research topics
Machine Learning
(10)
Artificial Intelligence
(5)
Application Areas
(5)
Optimization & Theory
(5)
Core AI
(4)
Learning Types Β· Machine Learning
(4)
Deep Learning
(3)
Natural Language Processing
(3)
Security & Privacy
(3)
Differential Privacy
(3)
Resources & Methods
(3)
Mathematics & Optimization
(2)
Learning Types Β· Deep Learning
(2)
Optimization
(2)
Computer Science
(1)
Keywords
differential privacy
(8)
large language model
(4)
neural network
(2)
deep learning
(2)
privacy budget
(2)
gradient perturbation
(2)
data augmentation
(1)
logical reasoning
(1)
privacy attack
(1)
network monitoring
(1)
gradient descent
(1)
network diagnosis
(1)
set classification
(1)
adversarial attack
(1)
reinforcement learning from human feedback
(1)
convex optimization
(1)
parameter-efficient fine-tuning
(1)
gradient clipping
(1)
scaling law
(1)
synthetic data
(1)
Papers
On Memorization of Large Language Models in Logical Reasoning
IJCNLP 2025
Scaling Laws for Differentially Private Language Models
ICML 2025
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
ICML 2024
Privacy-Preserving Instructions for Aligning Large Language Models
ICML 2024
Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks
AISTATS 2023
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
ICLR 2023
Differentially Private Fine-tuning of Language Models
ICLR 2022
Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
ICLR 2021
How Does Data Augmentation Affect Privacy in Machine Learning?
AAAI 2021
Large Scale Private Learning via Low-rank Reparametrization
ICML 2021
Gradient Perturbation is Underrated for Differentially Private Convex Optimization
IJCAI 2020
dShark: A General, Easy to Program and Scalable Framework for Analyzing In-network Packet Traces
NSDI 2019