Publications (7)
Detecting Transportation Mode Using Dense Smartphone GPS Trajectories and Transformer Models
Yuandong Zhang, Othmane Echchabi, Tianshu Feng +3
Transportation mode detection is an important topic within GeoAI and transportation research. In this study, we introduce SpeedTransformer, a novel Transformer-based model that rel…
Comparing Baseline Shapley and Integrated Gradients for Local Explanation: Some Additional Insights
Tianshu Feng, Zhipu Zhou, Joshi Tarun +1
There are many different methods in the literature for local explanation of machine learning results. However, the methods differ in their approaches and often do not provide same…
Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses
Tianshu Feng, Rohan Gnanaolivu, Abolfazl Safikhani +7
Human cancers present a significant public health challenge and require the discovery of novel drugs through translational research. Transcriptomics profiling data that describes m…
Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters
Qiang Meng, Feng Zhou, Hainan Ren +3
The growing public concerns on data privacy in face recognition can be greatly addressed by the federated learning (FL) paradigm. However, conventional FL methods perform poorly du…
Explaining Adverse Actions in Credit Decisions Using Shapley Decomposition
Vijayan N. Nair, Tianshu Feng, Linwei Hu +3
When a financial institution declines an application for credit, an adverse action (AA) is said to occur. The applicant is then entitled to an explanation for the negative decision…
Beyond Expected Goals: A Probabilistic Framework for Shot Occurrences in Soccer
Jonathan Pipping-Gamón, Tianshu Feng, R. Paul Sabin
Expected goals (xG) models estimate the probability that a shot results in a goal from its context (e.g., location, pressure), but they operate only on observed shots. We propose x…
Nonparametric Automatic Differentiation Variational Inference with Spline Approximation
Yuda Shao, Shan Yu, Tianshu Feng
Automatic Differentiation Variational Inference (ADVI) is efficient in learning probabilistic models. Classic ADVI relies on the parametric approach to approximate the posterior. I…