283 citations · 490 across the 13 of their papers we have counts for
13 papers
Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning
Mary V. Bastawrous, Zhi Chen, Alexander C. Ogren +3
Manipulating the dispersive characteristics of vibrational waves is beneficial for many applications, e.g., high-precision instruments. architected hierarchical phononic materials…
Sparse and Faithful Explanations Without Sparse Models
Yiyang Sun, Zhi Chen, Vittorio Orlandi +2
Even if a model is not globally sparse, it is possible for decisions made from that model to be accurately and faithfully described by a small number of features. For instance, an…
CLEVA: Chinese Language Models EVAluation Platform
Yanyang Li, Jianqiao Zhao, Duo Zheng +8
With the continuous emergence of Chinese Large Language Models (LLMs), how to evaluate a model's capabilities has become an increasingly significant issue. The absence of a compreh…
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling
Dixian Zhu, Bokun Wang, Zhi Chen +4
This paper considers a novel application of deep AUC maximization (DAM) for multi-instance learning (MIL), in which a single class label is assigned to a bag of instances (e.g., mu…
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?
Zhi Chen, Sarah Tan, Urszula Chajewska +2
Missing values are a fundamental problem in data science. Many datasets have missing values that must be properly handled because the way missing values are treated can have large…
Exploring and Interacting with the Set of Good Sparse Generalized Additive Models
Chudi Zhong, Zhi Chen, Jiachang Liu +2
In real applications, interaction between machine learning models and domain experts is critical; however, the classical machine learning paradigm that usually produces only a sing…
Exploring the Whole Rashomon Set of Sparse Decision Trees
Rui Xin, Chudi Zhong, Zhi Chen +3
In any given machine learning problem, there may be many models that could explain the data almost equally well. However, most learning algorithms return only one of these models,…
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization
Zijie J. Wang, Chudi Zhong, Rui Xin +5
Given thousands of equally accurate machine learning (ML) models, how can users choose among them? A recent ML technique enables domain experts and data scientists to generate a co…
SegDiscover: Visual Concept Discovery via Unsupervised Semantic Segmentation
Haiyang Huang, Zhi Chen, Cynthia Rudin
Visual concept discovery has long been deemed important to improve interpretability of neural networks, because a bank of semantically meaningful concepts would provide us with a s…
How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning
Zhi Chen, Alexander Ogren, Chiara Daraio +2
Machine learning models can assist with metamaterials design by approximating computationally expensive simulators or solving inverse design problems. However, past work has usuall…
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Cynthia Rudin, Chaofan Chen, Zhi Chen +3
Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel…
Concept Whitening for Interpretable Image Recognition
Zhi Chen, Yijie Bei, Cynthia Rudin
What does a neural network encode about a concept as we traverse through the layers? Interpretability in machine learning is undoubtedly important, but the calculations of neural n…
Adversarial Feature Matching for Text Generation
Yizhe Zhang, Zhe Gan, Kai Fan +4
The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with…