2 citations · 5 across the 5 of their papers we have counts for
5 papers
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…
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…
An adaptive model checking test for functional linear model
Enze Shi, Yi Liu, Ke Sun +2
Numerous studies have been devoted to the estimation and inference problems for functional linear models (FLM). However, few works focus on model checking problem that ensures the…
Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning
Ke Sun, Yingnan Zhao, Enze Shi +4
The remarkable empirical performance of distributional reinforcement learning (RL) has garnered increasing attention to understanding its theoretical advantages over classical RL.…
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observations
Ke Sun, Yingnan Zhao, Shangling Jui +1
In real scenarios, state observations that an agent observes may contain measurement errors or adversarial noises, misleading the agent to take suboptimal actions or even collapse…