3 citations · 9 across the 8 of their papers we have counts for
8 papers
Locally Private Online Quantile Regression: Estimation and Inference
Yi Liu, Qirui Hu
We study estimation and inference for online quantile regression under a one-report user-level $\eps$-locally differentially private ($\eps$-LDP) protocol. The main difficulty is t…
Gaussian Differential Privacy on Riemannian Manifolds
Yangdi Jiang, Xiaotian Chang, Yi Liu +3
We develop an advanced approach for extending Gaussian Differential Privacy (GDP) to general Riemannian manifolds. The concept of GDP stands out as a prominent privacy definition t…
Online Local Differential Private Quantile Inference via Self-normalization
Yi Liu, Qirui Hu, Lei Ding +2
Based on binary inquiries, we developed an algorithm to estimate population quantiles under Local Differential Privacy (LDP). By self-normalizing, our algorithm provides asymptotic…
Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models
Yi Liu, Gaurav Datta, Ellen Novoseller +1
Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted reward function. Ho…
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…
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…
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization
Ke Sun, Yafei Wang, Yi Liu +5
Anderson mixing has been heuristically applied to reinforcement learning (RL) algorithms for accelerating convergence and improving the sampling efficiency of deep RL. Despite its…
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…