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Found 12 papers, 3 papers with code
PV-SSD: A Multi-Modal Point Cloud Feature Fusion Method for Projection Features and Variable Receptive Field Voxel Features
This paper proposes a multi-modal point cloud feature fusion method for projection features and variable receptive field voxel features (PV-SSD) based on projection and variable voxelization to solve the information loss problem.
Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling
We use a working definition of personalization and introduce a resampling-based methodology for investigating whether the personalization exhibited by the RL algorithm is an artifact of the RL algorithm stochasticity.
EMT-NAS:Transferring Architectural Knowledge Between Tasks From Different Datasets
In deep learning, this is usually achieved by sharing a common neural network architecture and jointly training the weights.
Robust Batch Policy Learning in Markov Decision Processes
We study the offline data-driven sequential decision making problem in the framework of Markov decision process (MDP).
IntelligentPooling: Practical Thompson Sampling for mHealth
In this work we are concerned with the following challenges: 1) individuals who are in the same context can exhibit differential response to treatments 2) only a limited amount of data is available for learning on any one individual, and 3) non-stationary responses to treatment.
Batch Policy Learning in Average Reward Markov Decision Processes
The performance of the method is illustrated by simulation studies and an analysis of a mobile health study promoting physical activity.
Rapidly Personalizing Mobile Health Treatment Policies with Limited Data
In mobile health (mHealth), reinforcement learning algorithms that adapt to one's context without learning personalized policies might fail to distinguish between the needs of individuals.
Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health
The mHealth intervention policies, often called just-in-time adaptive interventions, are decision rules that map an individual's current state (e. g., individual's past behaviors as well as current observations of time, location, social activity, stress and urges to smoke) to a particular treatment at each of many time points.
Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity
In this paper, we develop a Reinforcement Learning (RL) algorithm that continuously learns and improves the treatment policy embedded in the JITAI as the data is being collected from the user.
Group-driven Reinforcement Learning for Personalized mHealth Intervention
Due to the popularity of smartphones and wearable devices nowadays, mobile health (mHealth) technologies are promising to bring positive and wide impacts on people's health.
Effective Warm Start for the Online Actor-Critic Reinforcement Learning based mHealth Intervention
As a result, we can greatly enrich the data size at the beginning of online learning in our method.
Cohesion-based Online Actor-Critic Reinforcement Learning for mHealth Intervention
In this paper, we propose a network cohesion constrained (actor-critic) Reinforcement Learning (RL) method for mHealth.
