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Retrieval models in information retrieval are used to rank documents for typically under-specified queries.
Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad. 2015 · 2015
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Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
Benjamin Letham, Cynthia Rudin, Tyler H McCormick, David Madigan, and others. 2015 · 2015
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Query expansion with locally-trained word embeddings. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics
Fernando Diaz, Bhaskar Mitra, and Nick Craswell. 2016 · 2016
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A deep relevance matching model for ad-hoc retrieval. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
Jiafeng Guo, Yixing Fan, Qingyao Ai, and W Bruce Croft. 2016 · 2016
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A dual embedding space model for document ranking
Bhaskar Mitra, Eric Nalisnick, Nick Craswell, and Rich Caruana. 2016 · 2016
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Improving document ranking with dual word embeddings. In Proceedings of the 25th International Conference Companion on World Wide Web
Eric Nalisnick, Bhaskar Mitra, Nick Craswell, and Rich Caruana. 2016 · 2016
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Model-agnostic interpretability of machine learning
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016a · 2016
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Embedding-based Query Language Models. In Proceedings of the 2016 ACM on International Conference on the Theory of Information Retrieval
Hamed Zamani and W Bruce Croft. 2016a · 2016
Cited alongside, same era.
Hamed Zamani and W Bruce Croft. 2016b · 2016
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A causal framework for explaining the predictions of black-box sequence-to-sequence models
David Alvarez-Melis and Tommi S Jaakkola. 2017 · 2017
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Variational Deep Semantic Hashing for Text Documents
Suthee Chaidaroon and Yi Fang. 2017 · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim. 2017 · 2017
Cited alongside, same era.
Neural Models for Information Retrieval
Bhaskar Mitra and Nick Craswell. 2017 · 2017
Later among the works it cites.
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. 2017 · 2017
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Adrian Weller. 2017 · 2017
Later among the works it cites.
Anchors: High-precision model-agnostic explanations. In AAAI Conference on Artificial Intelligence
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
Closest in time.
Posthoc Interpretability of Learning to Rank Models using Secondary Training Data
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang. 2017 · 2017
Cited alongside, same era.
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
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016b
Cited in the paper.
Jaspreet Singh and Avishek Anand. 2018 · 2018
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Show, attend and tell: Neural image caption generation with visual attention. In International Conference on Machine Learning
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015 · 2057
Closest in time.