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Valid Explanations for Learning to Rank Models
v1v2v3 (latest)

Valid Explanations for Learning to Rank Models

29 April 2020
Jaspreet Singh
Zhenye Wang
Megha Khosla
Avishek Anand
    LRMFAtt
ArXiv (abs)PDFHTML

Papers citing "Valid Explanations for Learning to Rank Models"

4 / 4 papers shown
RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank
RankSHAP: Shapley Value Based Feature Attributions for Learning to RankInternational Conference on Learning Representations (ICLR), 2024
Tanya Chowdhury
Yair Zick
James Allan
242
0
0
03 May 2024
ExaRanker: Explanation-Augmented Neural Ranker
ExaRanker: Explanation-Augmented Neural Ranker
Fernando Ferraretto
Thiago Laitz
R. Lotufo
Rodrigo Nogueira
ELMLRM
375
8
0
25 Jan 2023
Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank
Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to RankInternational Conference on the Theory of Information Retrieval (ICTIR), 2022
Tanya Chowdhury
Razieh Rahimi
James Allan
FAtt
212
23
0
24 Dec 2022
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural NetworksIEEE Transactions on Knowledge and Data Engineering (TKDE), 2021
Thorben Funke
Megha Khosla
Mandeep Rathee
Avishek Anand
FAtt
398
59
0
18 May 2021
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