Linglong Kong
22 papers · 2019–2026 · 8 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
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Grand Slam
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(17)
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Century Club Β· Bronze
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Renaissance Researcher
(7)
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Top co-authors
Research topics
Machine Learning
(21)
Optimization & Theory
(12)
Mathematics & Optimization
(10)
Application Areas
(9)
Artificial Intelligence
(7)
Learning Types
(6)
Mathematics
(6)
Core AI
(5)
Security & Privacy
(4)
Core Methods
(4)
Optimization
(4)
Reinforcement Learning
(3)
Differential Privacy
(3)
Methods
(3)
Deep Learning
(2)
Keywords
gaussian differential privacy
(3)
confidence interval
(3)
differential privacy
(3)
conformal prediction
(3)
prediction interval
(3)
distributional reinforcement learning
(2)
dependent data
(2)
gender bia
(2)
stochastic gradient descent
(2)
social bia
(2)
deep reinforcement learning
(2)
streaming data
(2)
local differential privacy
(2)
sample average approximation
(2)
feature selection
(1)
model misspecification
(1)
statistical inference
(1)
natural language processing
(1)
ensemble learning
(1)
algorithmic fairness
(1)
Papers
Transfer Conformal Predictive Inference for Regression
JMLR 2026
Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data
AISTATS 2025
CBMA: Improving Conformal Prediction through Bayesian Model Averaging
ICLR 2025
Differentially Private Analysis for Binary Response Models: Optimality, Estimation, and Inference
ICML 2025
Analysis of Differentially Private Synthetic Data: A Measurement Error Approach
AAAI 2024
Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations
NAACL 2024
Distributional Reinforcement Learning with Regularized Wasserstein Loss
NeurIPS 2024
Inference on High-dimensional Single-index Models with Streaming Data
JMLR 2024
Probing Social Bias in Labor Market Text Generation by ChatGPT: A Masked Language Model Approach
NeurIPS 2024
Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility
AAAI 2024
Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data
ICML 2024
Tuning-free Estimation and Inference of Cumulative Distribution Function under Local Differential Privacy
ICML 2024
Gaussian Differential Privacy on Riemannian Manifolds
NeurIPS 2023
Online Local Differential Private Quantile Inference via Self-normalization
ICML 2023
Opposite Online Learning via Sequentially Integrated Stochastic Gradient Descent Estimators
AAAI 2023
Conformalized Fairness via Quantile Regression
NeurIPS 2022
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy
NeurIPS 2022
Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability
AAAI 2022
Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving
AAAI 2022
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization
NeurIPS 2021
Distributional Reinforcement Learning for Efficient Exploration
ICML 2019
Ensemble-based Ultrahigh-dimensional Variable Screening
IJCAI 2019