International Conference on Learning Representations (ICLR)
January 28, 2022
irregular sampling, uncertainty, imputation, interpolation, multivariate time series, missing data, variational autoencoder
In order to model and represent uncertainty in mHealth biomarkers to account for multifaceted uncertainty during momentary decision making in selecting, adapting, and delivering temporally-precise mHealth interventions. In this period, we extended our previous deep learning approach, Multi-Time Attention Networks, to enable improved representation of output uncertainty. Our new approach preserves the idea of learned temporal similarity functions and adds heteroskedastic output uncertainty. The new framework is referred to as the Heteroskedastic Variational Autoencoder and models real-valued multivariate data.
Irregularly sampled time series commonly occur in several domains where they present a significant challenge to standard deep learning models. In this paper, we propose a new deep learning framework for probabilistic interpolation of irregularly sampled time series that we call the Heteroscedastic Temporal Variational Autoencoder (HeTVAE). HeTVAE includes a novel input layer to encode information about input observation sparsity, a temporal VAE architecture to propagate uncertainty due to input sparsity, and a heteroscedastic output layer to enable variable uncertainty in output interpolations. Our results show that the proposed architecture is better able to reflect variable uncertainty through time due to sparse and irregular sampling than a range of baseline and traditional models, as well as recently proposed deep latent variable models that use homoscedastic output layers.
We present a new deep learning architecture for probabilistic interpolation of irregularly sampled time series.
IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)
September 12, 2022
Bayesian inference, probabilistic programming, time series, missing data, Bayesian imputation, mobile health
We have developed a toolbox for the specification and estimation of mechanistic models in the dynamic bayesian network family. This toolbox focuses on making it easier to specify probabilistic dynamical models for time series data and to perform Bayesian inference and imputation in the specified model given incomplete data as input. The toolbox is referred to as BayesLDM. We have been working with members of CP3, CP4, and TR&D2 to develop offline data analysis and simulation models using this toolbox. We are also currently in discussions with members of CP4 to deploy the toolbox’s Bayesian imputation methods within a live controller optimization trial in the context of an adaptive walking intervention.
In this paper we present BayesLDM, a system for Bayesian longitudinal data modeling consisting of a high-level modeling language with specific features for modeling complex multivariate time series data coupled with a compiler that can produce optimized probabilistic program code for performing inference in the specified model. BayesLDM supports modeling of Bayesian network models with a specific focus on the efficient, declarative specification of dynamic Bayesian Networks (DBNs). The BayesLDM compiler combines a model specification with inspection of available data and outputs code for performing Bayesian inference for unknown model parameters while simultaneously handling missing data. These capabilities have the potential to significantly accelerate iterative modeling workflows in domains that involve the analysis of complex longitudinal data by abstracting away the process of producing computationally efficient probabilistic inference code. We describe the BayesLDM system components, evaluate the efficiency of representation and inference optimizations and provide an illustrative example of the application of the system to analyzing heterogeneous and partially observed mobile health data.
We present a a toolbox for the specification and estimation of mechanistic models in the dynamic bayesian network family.
We consider the batch (off-line) policy learning problem in the infinite horizon Markov decision process. Motivated by mobile health applications, we focus on learning a policy that maximizes the long-term average reward. We propose a doubly robust estimator for the average reward and show that it achieves semiparametric efficiency. Further, we develop an optimization algorithm to compute the optimal policy in a parameterized stochastic policy class. The performance of the estimated policy is measured by the difference between the optimal average reward in the policy class and the average reward of the estimated policy and we establish a finite-sample regret guarantee. The performance of the method is illustrated by simulation studies and an analysis of a mobile health study promoting physical activity.
We consider batch policy learning in an infinite horizon Markov Decision Process, focusing on optimizing a policy for long-term average reward in the context of mobile health applications.
To promote healthy behaviors, many mobile health applications provide message-based interventions, such as tips, motivational messages, or suggestions for healthy activities. Ideally, the intervention policies should be carefully designed so that users obtain the benefits without being overwhelmed by overly frequent messages. As part of the HeartSteps physical-activity intervention, users receive messages intended to disrupt sedentary behavior. HeartSteps uses an algorithm to uniformly spread out the daily message budget over time, but does not attempt to maximize treatment effects. This limitation motivates constructing a policy to optimize the message delivery decisions for more effective treatments. Moreover, the learned policy needs to be interpretable to enable behavioral scientists to examine it and to inform future theorizing. We address this problem by learning an effective and interpretable policy that reduces sedentary behavior. We propose Optimal Policy Trees + (OPT+), an innovative batch off-policy learning method, that combines a personalized threshold learning and an extension of Optimal Policy Trees under a budget-constrained setting. We implement and test the method using data collected in HeartSteps V2N3. Computational results demonstrate a significant reduction in sedentary behavior with a lower delivery budget. OPT + produces a highly interpretable and stable output decision tree thus enabling theoretical insights to guide future research.
Online RL faces challenges like real-time stability and handling complex, unpredictable environments; to address these issues, the PCS framework originally used in supervised learning is extended to guide the design of RL algorithms for such settings, including guidelines for creating simulation environments, as exemplified in the development of an RL algorithm for the mobile health study Oralytics aimed at enhancing tooth-brushing behaviors through personalized intervention messages.
January 13, 2022
CP, Heart Disease, Physical Activity, TR&D1, TR&D2