41 citations · 133 across the 15 of their papers we have counts for
16 papers
Interpretable Hypergraph Learning via Neural Additive Models
Shihan Feng, Xin Zheng, Shiyi Yang +3
Hypergraphs offer a natural framework for modeling networked data, where dependencies among entities are governed by higher-order interactions. While hypergraph learning methods su…
The Asymmetric Harms of LLM Compression
Yuan Wu, Mairui Li, Lesia Semenova +1
Large language models (LLMs) compression reduces deployment costs, but standard aggregate metrics like perplexity and accuracy often mask underlying behavioral shifts. In this work…
Where You Inject Diversity Matters: A Unified Framework for Diverse Generation
Cheng Zhang, Rui Xin, Chudi Zhong
Open-ended generation tasks often require a set of meaningfully different outputs, yet large language models often produce similar generations. Existing test-time diversity methods…
The Double-Edged Nature of the Rashomon Set for Trustworthy Machine Learning
Ethan Hsu, Harry Chen, Chudi Zhong +1
Real-world machine learning (ML) pipelines rarely produce a single model; instead, they produce a Rashomon set of many near-optimal ones. We show that this multiplicity reshapes ke…
Parameter-Efficient Construction of the Rashomon Slice for Concept Bottleneck Models
Shihan Feng, Cheng Zhang, Michael Xi +3
In many machine learning problems, there may exist multiple models that achieve nearly identical predictive performance while relying on fundamentally different internal logic. How…
Models That Are Interpretable But Not Transparent
Chudi Zhong, Panyu Chen, Cynthia Rudin
Faithful explanations are essential for machine learning models in high-stakes applications. Inherently interpretable models are well-suited for these applications because they nat…
Amazing Things Come From Having Many Good Models
Cynthia Rudin, Chudi Zhong, Lesia Semenova +7
The Rashomon Effect, coined by Leo Breiman, describes the phenomenon that there exist many equally good predictive models for the same dataset. This phenomenon happens for many rea…
OKRidge: Scalable Optimal k-Sparse Ridge Regression
Jiachang Liu, Sam Rosen, Chudi Zhong +1
We consider an important problem in scientific discovery, namely identifying sparse governing equations for nonlinear dynamical systems. This involves solving sparse ridge regressi…
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,…
FasterRisk: Fast and Accurate Interpretable Risk Scores
Jiachang Liu, Chudi Zhong, Boxuan Li +2
Over the last century, risk scores have been the most popular form of predictive model used in healthcare and criminal justice. Risk scores are sparse linear models with integer co…
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…
Fast Sparse Classification for Generalized Linear and Additive Models
Jiachang Liu, Chudi Zhong, Margo Seltzer +1
We present fast classification techniques for sparse generalized linear and additive models. These techniques can handle thousands of features and thousands of observations in minu…
Fast Sparse Decision Tree Optimization via Reference Ensembles
Hayden McTavish, Chudi Zhong, Reto Achermann +4
Sparse decision tree optimization has been one of the most fundamental problems in AI since its inception and is a challenge at the core of interpretable machine learning. Sparse d…
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…
Generalized and Scalable Optimal Sparse Decision Trees
Jimmy Lin, Chudi Zhong, Diane Hu +2
Decision tree optimization is notoriously difficult from a computational perspective but essential for the field of interpretable machine learning. Despite efforts over the past 40…