no code implementations • 13 Aug 2023 • Yongxin Shao, Aihong Tan, Zhetao Sun, Enhui Zheng, Tianhong Yan, Peng Liao
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.
1 code implementation • 11 Apr 2023 • Susobhan Ghosh, Raphael Kim, Prasidh Chhabria, Raaz Dwivedi, Predrag Klasnja, Peng Liao, Kelly Zhang, Susan Murphy
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.
1 code implementation • CVPR 2023 • Peng Liao, Yaochu Jin, Wenli Du
In deep learning, this is usually achieved by sharing a common neural network architecture and jointly training the weights.
no code implementations • 9 Nov 2020 • Zhengling Qi, Peng Liao
We study the offline data-driven sequential decision making problem in the framework of Markov decision process (MDP).
no code implementations • 31 Jul 2020 • Sabina Tomkins, Peng Liao, Predrag Klasnja, Susan Murphy
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.
no code implementations • 23 Jul 2020 • Peng Liao, Zhengling Qi, Runzhe Wan, Predrag Klasnja, Susan Murphy
The performance of the method is illustrated by simulation studies and an analysis of a mobile health study promoting physical activity.
no code implementations • 23 Feb 2020 • Sabina Tomkins, Peng Liao, Predrag Klasnja, Serena Yeung, Susan Murphy
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.
no code implementations • 30 Dec 2019 • Peng Liao, Predrag Klasnja, Susan Murphy
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.
no code implementations • 8 Sep 2019 • Peng Liao, Kristjan Greenewald, Predrag Klasnja, Susan Murphy
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.
1 code implementation • 14 Aug 2017 • Feiyun Zhu, Jun Guo, Zheng Xu, Peng Liao, Junzhou Huang
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.
no code implementations • 17 Apr 2017 • Feiyun Zhu, Peng Liao
As a result, we can greatly enrich the data size at the beginning of online learning in our method.
no code implementations • 25 Mar 2017 • Feiyun Zhu, Peng Liao, Xinliang Zhu, Yaowen Yao, Junzhou Huang
In this paper, we propose a network cohesion constrained (actor-critic) Reinforcement Learning (RL) method for mHealth.