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The Bayesian Case Model: A Generative Approach for Case-Based Reasoning
  and Prototype Classification

The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification

3 March 2015
Been Kim
Cynthia Rudin
J. Shah
ArXivPDFHTML

Papers citing "The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification"

9 / 9 papers shown
Title
A Quantitative Evaluation of the Expressivity of BMI, Pose and Gender in Body Embeddings for Recognition and Identification
A Quantitative Evaluation of the Expressivity of BMI, Pose and Gender in Body Embeddings for Recognition and Identification
Basudha Pal
Siyuan
Huang
112
0
0
09 Mar 2025
Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation
N. Dennler
Stefanos Nikolaidis
Maja J. Matarić
371
0
0
03 Jan 2025
Learning Personalized Decision Support Policies
Learning Personalized Decision Support Policies
Umang Bhatt
Valerie Chen
Katherine M. Collins
Parameswaran Kamalaruban
Emma Kallina
Adrian Weller
Ameet Talwalkar
OffRL
118
10
0
13 Apr 2023
ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents
ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents
Yotam Amitai
Guy Avni
Ofra Amir
59
3
0
24 Jan 2023
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Satyapriya Krishna
Tessa Han
Alex Gu
Steven Wu
S. Jabbari
Himabindu Lakkaraju
228
190
0
03 Feb 2022
Interpretable classifiers using rules and Bayesian analysis: Building a
  better stroke prediction model
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
Benjamin Letham
Cynthia Rudin
Tyler H. McCormick
D. Madigan
FAtt
48
743
0
05 Nov 2015
Methods and Models for Interpretable Linear Classification
Methods and Models for Interpretable Linear Classification
Berk Ustun
Cynthia Rudin
80
44
0
16 May 2014
Box Drawings for Learning with Imbalanced Data
Box Drawings for Learning with Imbalanced Data
Siong Thye Goh
Cynthia Rudin
35
43
0
13 Mar 2014
How to Explain Individual Classification Decisions
How to Explain Individual Classification Decisions
D. Baehrens
T. Schroeter
Stefan Harmeling
M. Kawanabe
K. Hansen
K. Müller
FAtt
104
1,098
0
06 Dec 2009
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