ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2209.08040
  4. Cited By
Exploring the Whole Rashomon Set of Sparse Decision Trees

Exploring the Whole Rashomon Set of Sparse Decision Trees

16 September 2022
Rui Xin
Chudi Zhong
Zhi Chen
Takuya Takagi
Margo Seltzer
Cynthia Rudin
ArXivPDFHTML

Papers citing "Exploring the Whole Rashomon Set of Sparse Decision Trees"

9 / 9 papers shown
Title
Unique Rashomon Sets for Robust Active Learning
Simon Nugyen
Kentaro Hoffman
Tyler H. McCormick
57
0
0
13 Mar 2025
Amazing Things Come From Having Many Good Models
Amazing Things Come From Having Many Good Models
Cynthia Rudin
Chudi Zhong
Lesia Semenova
Margo Seltzer
Ronald E. Parr
Jiachang Liu
Srikar Katta
Jon Donnelly
Harry Chen
Zachery Boner
26
23
0
05 Jul 2024
Efficient Exploration of the Rashomon Set of Rule Set Models
Efficient Exploration of the Rashomon Set of Rule Set Models
Martino Ciaperoni
Han Xiao
A. Gionis
18
3
0
05 Jun 2024
What is different between these datasets?
What is different between these datasets?
Varun Babbar
Zhicheng Guo
Cynthia Rudin
57
1
0
08 Mar 2024
Exploring and Interacting with the Set of Good Sparse Generalized
  Additive Models
Exploring and Interacting with the Set of Good Sparse Generalized Additive Models
Chudi Zhong
Zhi Chen
Jiachang Liu
Margo Seltzer
Cynthia Rudin
22
11
0
28 Mar 2023
FasterRisk: Fast and Accurate Interpretable Risk Scores
FasterRisk: Fast and Accurate Interpretable Risk Scores
Jiachang Liu
Chudi Zhong
Boxuan Li
Margo Seltzer
Cynthia Rudin
30
16
0
12 Oct 2022
Seeking Interpretability and Explainability in Binary Activated Neural
  Networks
Seeking Interpretability and Explainability in Binary Activated Neural Networks
Benjamin Leblanc
Pascal Germain
FAtt
17
1
0
07 Sep 2022
Causality-Inspired Taxonomy for Explainable Artificial Intelligence
Causality-Inspired Taxonomy for Explainable Artificial Intelligence
Pedro C. Neto
Tiago B. Gonccalves
João Ribeiro Pinto
W. Silva
Ana F. Sequeira
Arun Ross
Jaime S. Cardoso
XAI
26
12
0
19 Aug 2022
Understanding How Dimension Reduction Tools Work: An Empirical Approach
  to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization
Yingfan Wang
Haiyang Huang
Cynthia Rudin
Yaron Shaposhnik
157
300
0
08 Dec 2020
1