Statistics - Machine Learning

Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection
There is currently a large gap in performance between the statistically rigorous methods like linear regression or additive splines and …
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When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning
Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level …
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Towards Accurate Spatiotemporal COVID-19 Risk Scores using High Resolution Real-World Mobility Data
As countries look towards re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been …
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How does this interaction affect me? Interpretable attribution for feature interactions
Machine learning transparency calls for interpretable explanations of how inputs relate to predictions. Feature attribution is a way to …
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NoiseRank: Unsupervised Label Noise Reduction with Dependence Models
Label noise is increasingly prevalent in datasets acquired from noisy channels. Existing approaches that detect and remove label noise …
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D$textasciicircum2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios
Driving datasets accelerate the development of intelligent driving and related computer vision technologies, while substantial and …
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Differentiable Physics-informed Graph Networks
While physics conveys knowledge of nature built from an interplay between observations and theory, it has been considered less …
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Combating Fake News: A Survey on Identification and Mitigation Techniques
The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of …
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Recurrent Neural Networks for Multivariate Time Series with Missing Values
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety …
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Detecting Statistical Interactions from Neural Network Weights
Interpreting neural networks is a crucial and challenging task in machine learning. In this paper, we develop a novel framework for …
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