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While SHAP (SHapley Additive exPlanations) and other feature attribution methods are commonly employed to explain model predictions, their application within information retrieval (IR), particularly for complex outputs such as ranked lists, remains limited.
A New Measure of Rank Correlation
Maurice G. Kendall. 1938 · 1938
Earlier work this paper cites.
A Value for n-Person Games
Lloyd S. Shapley. 1953 · 1953
Earlier work this paper cites.
Feature Selection for Ranking. In SIGIR 2007: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Amsterdam, The Netherlands, July 23-27, 2007 , Wessel Kraaij, Arjen P. de Vries, Charles L. A. Clarke, Norbert Fuhr, and Noriko Kando (Eds.). ACM, 407–414
Xiubo Geng, Tie-Yan Liu, Tao Qin, and Hang Li. 2007 · 2007
Earlier work this paper cites.
An Efficient Explanation of Individual Classifications using Game Theory
Erik Strumbelj and Igor Kononenko. 2010 · 2010
Earlier work this paper cites.
Introducing LETOR 4.0 Datasets
Tao Qin and Tie-Yan Liu. 2013 · 2013
Earlier work this paper cites.
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. In 2nd International Conference on Learning Representations, ICLR 2014, Workshop Track Proceedings
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014 · 2014
Earlier work this paper cites.
Explaining Prediction Models and Individual Predictions with Feature Contributions
Erik Štrumbelj and Igor Kononenko. 2014 · 2014
Earlier work this paper cites.
Fast Feature Selection for Learning to Rank. In Proceedings of the 2016 ACM on International Conference on the Theory of Information Retrieval, ICTIR 2016, Newark, DE, USA, September 12- 6, 2016 , Ben Carterette, Hui Fang, Mounia Lalmas, and Jian-Yun Nie (Eds.). ACM, 167–170
Andrea Gigli, Claudio Lucchese, Franco Maria Nardini, and Raffaele Perego. 2016 · 2016
Earlier work this paper cites.
Rationalizing Neural Predictions. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing . 107–117
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
Earlier work this paper cites.
“Why Should I Trust You?” Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 1135–1144
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in neural information processing systems , Vol. 30
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017 · 2017
Earlier work this paper cites.
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg and Su-In Lee. 2017 · 2017
Earlier work this paper cites.
Learning Important Features Through Propagating Activation Differences. In International Conference on Machine Learning . PMLR, 3145–3153
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017 · 2017
Earlier work this paper cites.
Axiomatic Attribution for Deep Networks. In International Conference on Machine Learning . PMLR, 3319–3328
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
Earlier work this paper cites.
Posthoc Interpretability of Learning to Rank Models using Secondary Training Data
Jaspreet Singh and Avishek Anand. 2018 · 2018
Earlier work this paper cites.
A Study on the Interpretability of Neural Retrieval Models Using DeepSHAP. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris, France) (SIGIR’19) . ACM, New York, NY, USA, 1005–1008
Zeon Trevor Fernando, Jaspreet Singh, and Avishek Anand. 2019 · 2019
Earlier work this paper cites.
Explanation in Artificial Intelligence: Insights from the Social Sciences
Tim Miller. 2019 · 2019
Earlier work this paper cites.
An Axiomatic Approach to Diagnosing Neural IR Models. In European Conference on Information Retrieval . Springer, 489–503
Daniël Rennings, Felipe Moraes, and Claudia Hauff. 2019 · 2019
Earlier work this paper cites.
EXS: Explainable Search Using Local Model Agnostic Interpretability. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (Melbourne VIC, Australia) (WSDM ’19) . ACM, New York, NY, USA, 770–773
Jaspreet Singh and Avishek Anand. 2019 · 2019
Earlier work this paper cites.
LIRME: Locally Interpretable Ranking Model Explanation. In Proceedings of the 42nd International ACM SIGIR
Manisha Verma and Debasis Ganguly. 2019 · 2019
Cited alongside, same era.
Attention is not not Explanation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) , Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan (Eds.). Association for Computational Linguistics, Hong Kong, China, 11–20
Sarah Wiegreffe and Yuval Pinter. 2019 · 2019
Cited alongside, same era.
Diagnosing BERT with Retrieval Heuristics
Arthur Câmara and Claudia Hauff. 2020 · 2020
Cited alongside, same era.
Interpreting Neural Ranking Models Using Grad-CAM
Jaekeol Choi, Jungin Choi, and Wonjong Rhee. 2020 · 2020
Cited alongside, same era.
Explain and Predict, and then Predict again. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining . 418–426
Zijian Zhang, Koustav Rudra, and Avishek Anand. 2021 · 2021
Later among the works it cites.
Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics
Jianlong Zhou, Amir H. Gandomi, Fang Chen, and Andreas Holzinger. 2021 · 2021
Later among the works it cites.
The Disagreement Problem in Explainable Machine Learning: A Practitioner’s Perspective
Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, and Himabindu Lakkaraju. 2022 · 2022
Later among the works it cites.
WeightedSHAP: Analyzing and Improving Shapley based Feature Attributions
Yongchan Kwon and James Y Zou. 2022 · 2022
Later among the works it cites.
Pairwise Review-based Explanations for Voice Product Search. In Proceedings of the 2022 Conference on Human Information Interaction and Retrieval . 300–304
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Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . 4198–4205
Alon Jacovi and Yoav Goldberg. 2020 · 2020
Cited alongside, same era.
Problems with Shapley-value-based Explanations as Feature Importance Measures. In International Conference on Machine Learning . PMLR, 5491–5500
I. Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, and Sorelle Friedler. 2020 · 2020
Cited alongside, same era.
Model Agnostic Interpretability of Rankers via Intent Modelling. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency . 618–628
Jaspreet Singh and Avishek Anand. 2020 · 2020
Cited alongside, same era.
Fooling Lime and SHAP: Adversarial Attacks on Post Hoc Explanation Methods. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society . 180–186
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju. 2020 · 2020
Cited alongside, same era.
Explaining Individual Predictions When Features are Dependent: More Accurate Approximations to Shapley Values
Kjersti Aas, Martin Jullum, and Anders Løland. 2021 · 2021
Cited alongside, same era.
Towards Rigorous Interpretations: A Formalisation of Feature Attribution. In International Conference on Machine Learning . PMLR, 76–86
Darius Afchar, Vincent Guigue, and Romain Hennequin. 2021 · 2021
Cited alongside, same era.
On Locality of Local Explanation Models
Sahra Ghalebikesabi, Lucile Ter-Minassian, Karla DiazOrdaz, and Chris C Holmes. 2021 · 2021
Cited alongside, same era.
Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, Canada) (SIGIR ’21) . Association for Computing Machinery, New York, NY, USA, 1349–1358
Sebastian Hofstätter, Bhaskar Mitra, Hamed Zamani, Nick Craswell, and Allan Hanbury. 2021 · 2021
Cited alongside, same era.
Gustavo Penha, Eyal Krikon, and Vanessa Murdock. 2022 · 2022
Later among the works it cites.
A Consistent and Efficient Evaluation Strategy for Attribution Methods. In Proceedings of the 39th International Conference on Machine Learning . 18770–18795
Yao Rong, Tobias Leemann, Vadim Borisov, Gjergji Kasneci, and Enkelejda Kasneci. 2022 · 2022
Later among the works it cites.
Towards Explainable Search Results: A Listwise Explanation Generator. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 669–680
Puxuan Yu, Razieh Rahimi, and James Allan. 2022 · 2022
Later among the works it cites.
Explainable Information Retrieval. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval . 3448–3451
Avishek Anand, Procheta Sen, Sourav Saha, Manisha Verma, and Mandar Mitra. 2023 · 2023
Later among the works it cites.
Rank-LIME: Local Model-agnostic Feature Attribution for Learning to Rank. In Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval . 33–37
Tanya Chowdhury, Razieh Rahimi, and James Allan. 2023 · 2023
Later among the works it cites.
Listwise Explanations for Ranking Models Using Multiple Explainers. In European Conference on Information Retrieval . Springer Nature Switzerland Cham, 653–668
Lijun Lyu and Avishek Anand. 2023 · 2023
Later among the works it cites.
Interpreting Machine Learning Models with SHAP
Christophe Molnar. 2023 · 2023
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From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
Meike Nauta, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, Jörg Schlötterer, Maurice van Keulen, and Christin Seifert. 2023 · 2023
Later among the works it cites.
Probing BERT for Ranking Abilities. In European Conference on Information Retrieval . Springer Nature Switzerland Cham, 255–273
Jonas Wallat, Fabian Beringer, Abhijit Anand, and Avishek Anand. 2023 · 2023
Later among the works it cites.
Efficient sampling approaches to shapley value approximation
Jiayao Zhang, Qiheng Sun, Jinfei Liu, Li Xiong, Jian Pei, and Kui Ren. 2023 · 2023
Later among the works it cites.
RankSHAP: a Gold Standard Feature Attribution Method for the Ranking Task
Tanya Chowdhury, Yair Zick, and James Allan. 2024 · 2024
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SHAP@ k: Efficient and Probably Approximately Correct (PAC) Identification of Top-k Features. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 13068–13075
Sanjay Kariyappa, Leonidas Tsepenekas, Freddy Lécué, and Daniele Magazzeni. 2024 · 2024
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Towards faithful model explanation in nlp: A survey
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ir_explain: a Python Library of Explainable IR Methods
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