activity
20192022
most citedHierarchical Hidden Markov Jump Processes for Cancer Screening Modeling

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2022

Interpretable Research Replication Prediction via Variational Contextual Consistency Sentence Masking

Tianyi Luo, Rui Meng, Xin Eric Wang +1

Research Replication Prediction (RRP) is the task of predicting whether a published research result can be replicated or not. Building an interpretable neural text classifier for R…

cs.LG2022

Compressed Predictive Information Coding

Rui Meng, Tianyi Luo, Kristofer Bouchard

Unsupervised learning plays an important role in many fields, such as artificial intelligence, machine learning, and neuroscience. Compared to static data, methods for extracting l…

cs.LG2020

Spatiotemporal Attention for Multivariate Time Series Prediction and Interpretation

Tryambak Gangopadhyay, Sin Yong Tan, Zhanhong Jiang +2

Multivariate time series modeling and prediction problems are abundant in many machine learning application domains. Accurate interpretation of such prediction outcomes from a mach…

stat.ME20191 cited

Nonstationary Multivariate Gaussian Processes for Electronic Health Records

Rui Meng, Braden Soper, Herbert Lee +3

We propose multivariate nonstationary Gaussian processes for jointly modeling multiple clinical variables, where the key parameters, length-scales, standard deviations and the corr…

stat.ME20191 cited

Hierarchical Hidden Markov Jump Processes for Cancer Screening Modeling

Rui Meng, Soper Braden, Jan Nygard +2

Hidden Markov jump processes are an attractive approach for modeling clinical disease progression data because they are explainable and capable of handling both irregularly sampled…