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Found 16 papers, 5 papers with code
Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals
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.
Safe Policy Optimization with Local Generalized Linear Function Approximations
Safe exploration is a key to applying reinforcement learning (RL) in safety-critical systems.
Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality
Our results show that CAIL significantly outperforms other imitation learning methods from demonstrations with varying optimality.
Parkinsonian Chinese Speech Analysis towards Automatic Classification of Parkinson's Disease
Speech disorders often occur at the early stage of Parkinson's disease (PD).
No-Regret Reinforcement Learning with Heavy-Tailed Rewards
We consider such scenarios in the setting of undiscounted reinforcement learning.
ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes
ROIAL learns Bayesian posteriors that predict each exoskeleton user's utility landscape across four exoskeleton gait parameters.
Imitation with Neural Density Models
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.
Safe Reinforcement Learning in Constrained Markov Decision Processes
Safe reinforcement learning has been a promising approach for optimizing the policy of an agent that operates in safety-critical applications.
Deepfakes for Medical Video De-Identification: Privacy Protection and Diagnostic Information Preservation
Data sharing for medical research has been difficult as open-sourcing clinical data may violate patient privacy.
Dueling Posterior Sampling for Preference-Based Reinforcement Learning
In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback.
D3TW: Discriminative Differentiable Dynamic Time Warping for Weakly Supervised Action Alignment and Segmentation
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)
Stagewise Safe Bayesian Optimization with Gaussian Processes
We provide theoretical guarantees for both the satisfaction of safety constraints as well as convergence to the optimal utility value.
Quantifying Performance of Bipedal Standing with Multi-channel EMG
Spinal cord stimulation has enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function.
Bellman Gradient Iteration for Inverse Reinforcement Learning
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.
Correlational Dueling Bandits with Application to Clinical Treatment in Large Decision Spaces
This problem can be formulated as a $K$-armed Dueling Bandits problem where $K$ is the total number of decisions.
Multi-dueling Bandits with Dependent Arms
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.
