65 citations · 251 across the 19 of their papers we have counts for
20 papers
Stochastic Attention: Connectome-Inspired Randomized Routing for Expressive Linear-Time Attention
Zehao Jin, Yanan Sui
The whole-brain connectome of a fruit fly comprises over 130K neurons connected with a probability of merely 0.02%, yet achieves an average shortest path of only 4.4 hops. Despite…
A Survey of Constraint Formulations in Safe Reinforcement Learning
Akifumi Wachi, Xun Shen, Yanan Sui
Safety is critical when applying reinforcement learning (RL) to real-world problems. As a result, safe RL has emerged as a fundamental and powerful paradigm for optimizing an agent…
Adaptive Learning based Upper-Limb Rehabilitation Training System with Collaborative Robot
Jun Hong Lim, Kaibo He, Zeji Yi +4
Rehabilitation training for patients with motor disabilities usually requires specialized devices in rehabilitation centers. Home-based multi-purpose training would significantly i…
Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals
Chen Gong, Yue Chen, Yanan Sui +1
The detection of human sleep stages is widely used in the diagnosis and intervention of neurological and psychiatric diseases. Some patients with deep brain stimulator implanted co…
Safe Policy Optimization with Local Generalized Linear Function Approximations
Akifumi Wachi, Yunyue Wei, Yanan Sui
Safe exploration is a key to applying reinforcement learning (RL) in safety-critical systems. Existing safe exploration methods guaranteed safety under the assumption of regularity…
Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality
Songyuan Zhang, Zhangjie Cao, Dorsa Sadigh +1
Most existing imitation learning approaches assume the demonstrations are drawn from experts who are optimal, but relaxing this assumption enables us to use a wider range of data.…
Parkinsonian Chinese Speech Analysis towards Automatic Classification of Parkinson's Disease
Hao Fang, Chen Gong, Chen Zhang +2
Speech disorders often occur at the early stage of Parkinson's disease (PD). The speech impairments could be indicators of the disorder for early diagnosis, while motor symptoms ar…
No-Regret Reinforcement Learning with Heavy-Tailed Rewards
Vincent Zhuang, Yanan Sui
Reinforcement learning algorithms typically assume rewards to be sampled from light-tailed distributions, such as Gaussian or bounded. However, a wide variety of real-world systems…
ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes
Kejun Li, Maegan Tucker, Erdem Bıyık +6
Characterizing what types of exoskeleton gaits are comfortable for users, and understanding the science of walking more generally, require recovering a user's utility landscape. Le…
Imitation with Neural Density Models
Kuno Kim, Akshat Jindal, Yang Song +3
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) us…
Safe Reinforcement Learning in Constrained Markov Decision Processes
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. In this paper, we propose an algorith…
Deepfakes for Medical Video De-Identification: Privacy Protection and Diagnostic Information Preservation
Bingquan Zhu, Hao Fang, Yanan Sui +1
Data sharing for medical research has been difficult as open-sourcing clinical data may violate patient privacy. Traditional methods for face de-identification wipe out facial info…
Preference-Based Learning for Exoskeleton Gait Optimization
Maegan Tucker, Ellen Novoseller, Claudia Kann +4
This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, ou…
Dueling Posterior Sampling for Preference-Based Reinforcement Learning
Ellen R. Novoseller, Yibing Wei, Yanan Sui +2
In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback. While there is increasing researc…
D3TW: Discriminative Differentiable Dynamic Time Warping for Weakly Supervised Action Alignment and Segmentation
Chien-Yi Chang, De-An Huang, Yanan Sui +2
We address weakly supervised action alignment and segmentation in videos, where only the order of occurring actions is available during training. We propose Discriminative Differen…
Stagewise Safe Bayesian Optimization with Gaussian Processes
Yanan Sui, Vincent Zhuang, Joel W. Burdick +1
Enforcing safety is a key aspect of many problems pertaining to sequential decision making under uncertainty, which require the decisions made at every step to be both informative…
Quantifying Performance of Bipedal Standing with Multi-channel EMG
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. Quantifying the quality of…
Bellman Gradient Iteration for Inverse Reinforcement Learning
Kun Li, Yanan Sui, Joel W. Burdick
This paper develops an inverse reinforcement learning algorithm aimed at recovering a reward function from the observed actions of an agent. We introduce a strategy to flexibly han…
Correlational Dueling Bandits with Application to Clinical Treatment in Large Decision Spaces
Yanan Sui, Yisong Yue, Joel W. Burdick
We consider sequential decision making under uncertainty, where the goal is to optimize over a large decision space using noisy comparative feedback. This problem can be formulated…
Multi-dueling Bandits with Dependent Arms
Yanan Sui, Vincent Zhuang, Joel W. Burdick +1
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 subjec…