activity
20182024
most citedDesigning Inherently Interpretable Machine Learning Models

21 citations · 71 across the 14 of their papers we have counts for

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Showing 2020 · cs.LGShow all

5 papers · 2 filters

cs.LG2020★ 2 cited

Explainable Recommendation Systems by Generalized Additive Models with Manifest and Latent Interactions

Yifeng Guo, Yu Su, Zebin Yang +1

In recent years, the field of recommendation systems has attracted increasing attention to developing predictive models that provide explanations of why an item is recommended to a…

cs.LG2020★ 20 cited

Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification

Agus Sudjianto, William Knauth, Rahul Singh +2

The deep neural networks (DNNs) have achieved great success in learning complex patterns with strong predictive power, but they are often thought of as "black box" models without a…

cs.LG2020

Hyperparameter Optimization via Sequential Uniform Designs

Zebin Yang, Aijun Zhang

Hyperparameter optimization (HPO) plays a central role in the automated machine learning (AutoML). It is a challenging task as the response surfaces of hyperparameters are generall…

cs.LG2020

An Effective and Efficient Initialization Scheme for Training Multi-layer Feedforward Neural Networks

Zebin Yang, Hengtao Zhang, Agus Sudjianto +1

Network initialization is the first and critical step for training neural networks. In this paper, we propose a novel network initialization scheme based on the celebrated Stein's…

cs.LG2020

Balance-Subsampled Stable Prediction

Kun Kuang, Hengtao Zhang, Fei Wu +2

In machine learning, it is commonly assumed that training and test data share the same population distribution. However, this assumption is often violated in practice because the s…