no code implementations • 7 Feb 2023 • Chen Gong, Yue Chen, Yanan Sui, Luming Li
This sleep stage classification model could be adapted to chronic and continuous monitor sleep for Parkinson's patients in daily life, and potentially utilized for more precise treatment in deep brain-machine interfaces, such as closed-loop deep brain stimulation.
1 code implementation • NeurIPS 2021 • Akifumi Wachi, Yunyue Wei, Yanan Sui
Safe exploration is a key to applying reinforcement learning (RL) in safety-critical systems.
2 code implementations • NeurIPS 2021 • Songyuan Zhang, Zhangjie Cao, Dorsa Sadigh, Yanan Sui
Our results show that CAIL significantly outperforms other imitation learning methods from demonstrations with varying optimality.
no code implementations • 31 May 2021 • Hao Fang, Chen Gong, Chen Zhang, Yanan Sui, Luming Li
Speech disorders often occur at the early stage of Parkinson's disease (PD).
no code implementations • 25 Feb 2021 • Vincent Zhuang, Yanan Sui
We consider such scenarios in the setting of undiscounted reinforcement learning.
1 code implementation • 9 Nov 2020 • Kejun Li, Maegan Tucker, Erdem Biyik, Ellen Novoseller, Joel W. Burdick, Yanan Sui, Dorsa Sadigh, Yisong Yue, Aaron D. Ames
ROIAL learns Bayesian posteriors that predict each exoskeleton user's utility landscape across four exoskeleton gait parameters.
no code implementations • NeurIPS 2021 • Kuno Kim, Akshat Jindal, Yang song, Jiaming Song, Yanan Sui, Stefano Ermon
We propose a new framework for Imitation Learning (IL) via density estimation of the expert's occupancy measure followed by Maximum Occupancy Entropy Reinforcement Learning (RL) using the density as a reward.
1 code implementation • ICML 2020 • Akifumi Wachi, Yanan Sui
Safe reinforcement learning has been a promising approach for optimizing the policy of an agent that operates in safety-critical applications.
no code implementations • 7 Feb 2020 • Bingquan Zhu, Hao Fang, Yanan Sui, Luming Li
Data sharing for medical research has been difficult as open-sourcing clinical data may violate patient privacy.
1 code implementation • 4 Aug 2019 • Ellen R. Novoseller, Yibing Wei, Yanan Sui, Yisong Yue, Joel W. Burdick
In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback.
no code implementations • CVPR 2019 • Chien-Yi Chang, De-An Huang, Yanan Sui, Li Fei-Fei, Juan Carlos Niebles
The key technical challenge for discriminative modeling with weak supervision is that the loss function of the ordering supervision is usually formulated using dynamic programming and is thus not differentiable.
Dynamic Time Warping Weakly Supervised Action Segmentation (Transcript)
no code implementations • ICML 2018 • Yanan Sui, Vincent Zhuang, Joel W. Burdick, Yisong Yue
We provide theoretical guarantees for both the satisfaction of safety constraints as well as convergence to the optimal utility value.
no code implementations • 21 Nov 2017 • Yanan Sui, Kun Ho Kim, Joel W. Burdick
Spinal cord stimulation has enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function.
no code implementations • 24 Jul 2017 • Kun Li, Yanan Sui, Joel W. Burdick
We introduce a strategy to flexibly handle different types of actions with two approximations of the Bellman Optimality Equation, and a Bellman Gradient Iteration method to compute the gradient of the Q-value with respect to the reward function.
no code implementations • 8 Jul 2017 • Yanan Sui, Yisong Yue, Joel W. Burdick
This problem can be formulated as a $K$-armed Dueling Bandits problem where $K$ is the total number of decisions.
no code implementations • 29 Apr 2017 • Yanan Sui, Vincent Zhuang, Joel W. Burdick, Yisong Yue
The dueling bandits problem is an online learning framework for learning from pairwise preference feedback, and is particularly well-suited for modeling settings that elicit subjective or implicit human feedback.