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Cited By

WebSHAP: Towards Explaining Any Machine Learning Models Anywhere

WebSHAP: Towards Explaining Any Machine Learning Models Anywhere

The Web Conference (WWW), 2023

16 March 2023

Duen Horng Chau

ArXiv (abs)PDF HTML Github (60★)

Papers citing "WebSHAP: Towards Explaining Any Machine Learning Models Anywhere"

5 / 5 papers shown

MeMemo: On-device Retrieval Augmentation for Private and Personalized
Text Generation

MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generation

Duen Horng Chau

254

12

0

02 Jul 2024

InterroLang: Exploring NLP Models and Datasets through Dialogue-based
Explanations

InterroLang: Exploring NLP Models and Datasets through Dialogue-based ExplanationsConference on Empirical Methods in Natural Language Processing (EMNLP), 2023

Nils Feldhus

Qianli Wang

Tatiana Anikina

Sebastian Möller

334

20

0

09 Oct 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

Himabindu Lakkaraju

777

243

0

03 Feb 2022

The Language Interpretability Tool: Extensible, Interactive
Visualizations and Analysis for NLP Models

The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP ModelsConference on Empirical Methods in Natural Language Processing (EMNLP), 2020

Jasmijn Bastings

Tolga Bolukbasi

...

Mahima Pushkarna

Carey Radebaugh

363

210

0

12 Aug 2020

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Marco Tulio Ribeiro

Carlos Guestrin

2.5K

19,924

0

16 Feb 2016

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