Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?

Author:

Lyu LijunORCID,Roy NirmalORCID,Oosterhuis HarrieORCID,Anand AvishekORCID

Publisher

Springer Nature Switzerland

Reference71 articles.

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2. Alvarez-Melis, D., Jaakkola, T.S.: Towards robust interpretability with self-explaining neural networks. In: Bengio, S., Wallach, H.M., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018(December), pp. 3–8, 2018. Montréal, Canada, pp. 7786–7795 (2018). https://proceedings.neurips.cc/paper/2018/hash/3e9f0fc9b2f89e043bc6233994dfcf76-Abstract.html

3. Arapakis, I., Bai, X., Cambazoglu, B.B.: Impact of response latency on user behavior in web search. In: Geva, S., Trotman, A., Bruza, P., Clarke, C.L.A., Järvelin, K. (eds.) The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014, Gold Coast, QLD, Australia - 06–11 July 2014, pp. 103–112. ACM (2014). https://doi.org/10.1145/2600428.2609627

4. Arik, S.Ö., Pfister, T.: TabNet: attentive interpretable tabular learning. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, 2–9 February 2021, pp. 6679–6687. AAAI Press (2021). https://doi.org/10.1609/AAAI.V35I8.16826

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