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Interpretable Companions for Black-Box Models
v1v2 (latest)

Interpretable Companions for Black-Box Models

10 February 2020
Dan-qing Pan
Tong Wang
Satoshi Hara
    FaML
ArXiv (abs)PDFHTML

Papers citing "Interpretable Companions for Black-Box Models"

3 / 3 papers shown
Title
Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods
Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods
Julien Ferry
Gabriel Laberge
Ulrich Aïvodji
107
5
0
08 Mar 2023
Partially Interpretable Estimators (PIE): Black-Box-Refined
  Interpretable Machine Learning
Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning
Tong Wang
Jingyi Yang
Yunyi Li
Boxiang Wang
FAtt
60
5
0
06 May 2021
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment
  Effect
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect
Tong Wang
Cynthia Rudin
CMLBDL
97
27
0
16 Oct 2017
1