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Found 7 papers, 3 papers with code
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment
Traditional commercial DBS devices are only able to deliver fixed-frequency periodic pulses to the basal ganglia (BG) regions of the brain, i. e., continuous DBS (cDBS).
Robust Reinforcement Learning through Efficient Adversarial Herding
In this work, we extend the two-player game by introducing an adversarial herd, which involves a group of adversaries, in order to address ($\textit{i}$) the difficulty of the inner optimization problem, and ($\textit{ii}$) the potential over pessimism caused by the selection of a candidate adversary set that may include unlikely scenarios.
Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease Treatment
In this work, we introduce an offline reinforcement learning (RL) framework, allowing the use of past clinical data to train an RL policy to adjust the stimulation amplitude in real time, with the goal of reducing energy use while maintaining the same level of treatment (i. e., control) efficacy as cDBS.
Variational Latent Branching Model for Off-Policy Evaluation
In this work, we propose the variational latent branching model (VLBM) to learn the transition function of MDPs by formulating the environmental dynamics as a compact latent space, from which the next states and rewards are then sampled.
Gradient Importance Learning for Incomplete Observations
Though recent works have developed methods that can generate estimates (or imputations) of the missing entries in a dataset to facilitate downstream analysis, most depend on assumptions that may not align with real-world applications and could suffer from poor performance in subsequent tasks such as classification.
Learning-Based Vulnerability Analysis of Cyber-Physical Systems
This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS).
Learning Monotone Dynamics by Neural Networks
For monotonicity constraints, we propose to use nonnegative neural networks and batch normalization.
