Linglong Kong

Exponential-Wrapped Mechanisms: Differential Privacy on Hadamard Manifolds Made Practical

Principled Fast and Meta Knowledge Learners for Continual Reinforcement Learning

Adaptive Conformal Prediction via Mixture-of-Experts Gating Similarity

One-Shot Weighted Ensemble Estimation for Federated Quantile Regression: Optimal Statistical Guarantees under Heterogeneous Structured Data

Achieving Fairness-Utility Trade-offs through Decoupling Direct and Indirect Bias

A Bayesian Nonparametric Framework for Private, Fair, and Balanced Tabular Data Synthesis

Measuring Invariance in Representation Learning: A Robust Evaluation Framework

Online Continual Learning under Real Concept Drift: A Statistical Perspective

Adaptive robust integration of internal data with external summaries under distributional shift

CNB: A Bayesian Nonparametric Approach to Optimal Conformal Prediction

Maximum Probability-driven Bandit Learning for Matching Markets

Private Top-$k$ Selection under Gumbel Differential Privacy Guarantees

Understanding Fairness and Prediction Error through Subspace Decomposition and Influence Analysis

Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning

Principled Fast and Meta Knowledge Learners for Continual Reinforcement Learning

Exponential-Wrapped Mechanisms for Differential Privacy on Hadamard Manifolds

CBMA: Improving Conformal Prediction through Bayesian Model Averaging

The Benefits of Being Categorical Distributional: Uncertainty-aware Regularized Exploration in Reinforcement Learning

Differentially Private Analysis for Binary Response Models: Optimality, Estimation, and Inference

An Efficient LLM Alignment Framework for Automated Radiology Impression Generation

Conformal Prediction with Model-Aware Debiasing

FDA: Generating Fair Synthetic Data with Provable Trade-off between Fairness and Faithfulness

Bidirectional Communication-Efficient Non-Convex Adaptive Federated Learning