Ruoming Jin
12 papers · 2019–2025 · 6 venues · across top CS/AI venues
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Achievements
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π Interdisciplinary Bridge Β· Silver πΊοΈ Taxonomy Completionist (28) π Conference Polyglot Β· Silver π£ Hot Topic Early Bird π Academic Marathon Β· Sprint
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Renaissance Researcher
(6)
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Century Club Β· Bronze
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Research topics
Machine Learning
(9)
Artificial Intelligence
(6)
Core AI
(6)
Optimization & Theory
(6)
Learning Types
(5)
Application Areas
(4)
Deep Learning
(3)
Mathematics & Optimization
(3)
Mathematics
(3)
Data Science & Analytics
(2)
Natural Language Processing
(2)
Security & Privacy
(2)
Applications
(2)
Core Methods
(2)
Resources & Methods
(2)
Keywords
large language model
(3)
adversarial robustness
(2)
adversarial attack
(2)
evaluation metric
(2)
differential privacy
(2)
graph matching
(2)
recommender system
(2)
top-k metrics
(2)
robust optimization
(1)
graph learning
(1)
adversarial learning
(1)
curriculum learning
(1)
expectation maximization
(1)
privacy preservation
(1)
statistical analysis
(1)
stochastic gradient descent
(1)
fisher information
(1)
adaptive sampling
(1)
kernel density estimation
(1)
neural network optimization
(1)
Papers
$Ξ΄$-STEAL: LLM Stealing Attack with Local Differential Privacy
ACML 2025
Can Large Language Models Understand Intermediate Representations in Compilers?
ICML 2025
Flexible, Efficient, and Stable Adversarial Attacks on Machine Unlearning
ICML 2025
Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models
EMNLP 2024
Towards Reliable Item Sampling for Recommendation Evaluation
AAAI 2023
Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks
ICML 2021
Integrated Defense for Resilient Graph Matching
ICML 2021
On Estimating Recommendation Evaluation Metrics under Sampling
AAAI 2021
Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory
NeurIPS 2021
Adversarial Attacks on Deep Graph Matching
NeurIPS 2020
Scalable Differential Privacy with Certified Robustness in Adversarial Learning
ICML 2020
Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness
IJCAI 2019