22 citations · 34 across the 25 of their papers we have counts for
4 papers · 1 filter
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy
Yi Liu, Ke Sun, Linglong Kong +1
Gaussian differential privacy (GDP) is a single-parameter family of privacy notions that provides coherent guarantees to avoid the exposure of sensitive individual information. Des…
Conformalized Fairness via Quantile Regression
Meichen Liu, Lei Ding, Dengdeng Yu +3
Algorithmic fairness has received increased attention in socially sensitive domains. While rich literature on mean fairness has been established, research on quantile fairness rema…
How Does Return Distribution in Distributional Reinforcement Learning Help Optimization?
Ke Sun, Bei Jiang, Linglong Kong
Distributional reinforcement learning, which focuses on learning the entire return distribution instead of only its expectation in standard RL, has demonstrated remarkable success…
Distributional Reinforcement Learning with Regularized Wasserstein Loss
Ke Sun, Yingnan Zhao, Wulong Liu +2
The empirical success of distributional reinforcement learning (RL) highly relies on the choice of distribution divergence equipped with an appropriate distribution representation.…