{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:25:45Z","timestamp":1778199945650,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,12,17]]},"DOI":"10.1145\/3799830.3799835","type":"proceedings-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T06:45:08Z","timestamp":1777013108000},"page":"35-51","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["GANDALF: Gated Adaptive Network for Deep Automated Learning of Features for Tabular Data"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6358-2381","authenticated-orcid":false,"given":"Manu","family":"Joseph","sequence":"first","affiliation":[{"name":"Walmart Global Tech, BANGALORE, Karnataka, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3169-7871","authenticated-orcid":false,"given":"Harsh","family":"Raj","sequence":"additional","affiliation":[{"name":"Khoury College of Computer Sciences, Boston, Massachusetts, USA"}]}],"member":"320","published-online":{"date-parts":[[2026,4,23]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"publisher","unstructured":"Takuya Akiba Shotaro Sano Toshihiko Yanase Takeru Ohta and Masanori Koyama. 2019. Optuna: A Next-generation Hyperparameter Optimization Framework. Knowledge Discovery And Data Mining (2019). 10.1145\/3292500.3330701","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"publisher","unstructured":"Shun-ichi Amari. 1967. A Theory of Adaptive Pattern Classifiers. IEEE Transactions on Electronic Computers (Jun 1967). 10.1109\/pgec.1967.264666","DOI":"10.1109\/pgec.1967.264666"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16826"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","unstructured":"Klaudia Ba\u0142azy \u0141ukasz Struski Marek \u015amieja and Jacek Tabor. 2023. r-softmax: Generalized Softmax with Controllable Sparsity Rate. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2304.05243 (2023).","DOI":"10.1007\/978-3-031-36021-3_11"},{"key":"e_1_3_3_2_6_2","volume-title":"Advances in Neural Information Processing Systems","author":"Bergstra James","year":"2011","unstructured":"James Bergstra, R\u00e9mi Bardenet, Yoshua Bengio, and Bal\u00e1zs K\u00e9gl. 2011. Algorithms for Hyper-Parameter Optimization. In Advances in Neural Information Processing Systems, J.\u00a0Shawe-Taylor, R.\u00a0Zemel, P.\u00a0Bartlett, F.\u00a0Pereira, and K.Q. Weinberger (Eds.), Vol.\u00a024. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2011\/file\/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf"},{"key":"e_1_3_3_2_7_2","unstructured":"B. Bischl Giuseppe Casalicchio Matthias Feurer F. Hutter Michel Lang R. Mantovani J.\u00a0N. Rijn and J. Vanschoren. 2017. OpenML Benchmarking Suites. Neurips Datasets And Benchmarks (2017)."},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30484-3_6"},{"key":"e_1_3_3_2_9_2","unstructured":"Vadim Borisov Tobias Leemann Kathrin Se\u00dfler Johannes Haug Martin Pawelczyk and Gjergji Kasneci. 2021. Deep Neural Networks and Tabular Data: A Survey. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/Arxiv-2110.01889 (2021)."},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"publisher","unstructured":"Vadim Borisov Tobias Leemann Kathrin Se\u00dfler Johannes Haug Martin Pawelczyk and Gjergji Kasneci. 2022. Deep Neural Networks and Tabular Data: A Survey. IEEE Transactions on Neural Networks and Learning Systems PP (2022) 1\u201321. 10.1109\/TNNLS.2022.3229161","DOI":"10.1109\/TNNLS.2022.3229161"},{"key":"e_1_3_3_2_11_2","unstructured":"L. Breiman J.\u00a0H. Freidman Richard\u00a0A. Olshen and C.\u00a0J. Stone. 1984. CART: Classification and Regression Trees. https:\/\/api.semanticscholar.org\/CorpusID:59814698"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","unstructured":"Jintai Chen Kuan-Yu Liao Yao Wan D. Chen and Jian Wu. 2021. DANets: Deep Abstract Networks for Tabular Data Classification and Regression. Aaai Conference On Artificial Intelligence (2021). 10.1609\/aaai.v36i4.20309","DOI":"10.1609\/aaai.v36i4.20309"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"e_1_3_3_2_15_2","series-title":"Proceedings of Machine Learning Research","first-page":"933","volume-title":"Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017","volume":"70","author":"Dauphin Yann\u00a0N.","year":"2017","unstructured":"Yann\u00a0N. Dauphin, Angela Fan, Michael Auli, and David Grangier. 2017. Language Modeling with Gated Convolutional Networks. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017(Proceedings of Machine Learning Research, Vol.\u00a070), Doina Precup and Yee\u00a0Whye Teh (Eds.). PMLR, 933\u2013941. http:\/\/proceedings.mlr.press\/v70\/dauphin17a.html"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1423"},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Jerome\u00a0H. Friedman. 2001. Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics 29 5 (2001) 1189\u20131232. http:\/\/www.jstor.org\/stable\/2699986","DOI":"10.1214\/aos\/1013203451"},{"key":"e_1_3_3_2_18_2","series-title":"Proceedings of Machine Learning Research","first-page":"1243","volume-title":"Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017","volume":"70","author":"Gehring Jonas","year":"2017","unstructured":"Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann\u00a0N. Dauphin. 2017. Convolutional Sequence to Sequence Learning. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017(Proceedings of Machine Learning Research, Vol.\u00a070), Doina Precup and Yee\u00a0Whye Teh (Eds.). PMLR, 1243\u20131252. http:\/\/proceedings.mlr.press\/v70\/gehring17a.html"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.5555\/3086952"},{"key":"e_1_3_3_2_20_2","first-page":"18932","volume-title":"Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual","author":"Gorishniy Yury","year":"2021","unstructured":"Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko. 2021. Revisiting Deep Learning Models for Tabular Data. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, Marc\u2019Aurelio Ranzato, Alina Beygelzimer, Yann\u00a0N. Dauphin, Percy Liang, and Jennifer\u00a0Wortman Vaughan (Eds.). 18932\u201318943. https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/9d86d83f925f2149e9edb0ac3b49229c-Abstract.html"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"crossref","unstructured":"L\u00e9o Grinsztajn Edouard Oyallon and Ga\u00ebl Varoquaux. 2022. Why do tree-based models still outperform deep learning on tabular data? ArXiv abs\/2207.08815 (2022).","DOI":"10.52202\/068431-0037"},{"key":"e_1_3_3_2_22_2","unstructured":"Cheng Guo and Felix Berkhahn. 2016. Entity Embeddings of Categorical Variables. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/Arxiv-1604.06737 (2016)."},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"publisher","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long Short-term Memory. Neural computation 9 (12 1997) 1735\u201380. 10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_3_2_24_2","series-title":"Proceedings of Machine Learning Research","first-page":"754","volume-title":"Proceedings of the 31st International Conference on Machine Learning","volume":"32","author":"Hutter Frank","year":"2014","unstructured":"Frank Hutter, Holger Hoos, and Kevin Leyton-Brown. 2014. An Efficient Approach for Assessing Hyperparameter Importance. In Proceedings of the 31st International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a032), Eric\u00a0P. Xing and Tony Jebara (Eds.). PMLR, Bejing, China, 754\u2013762. https:\/\/proceedings.mlr.press\/v32\/hutter14.html"},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"publisher","unstructured":"Kalervo J\u00e4rvelin and Jaana Kek\u00e4l\u00e4inen. 2002. Cumulated Gain-Based Evaluation of IR Techniques. ACM Trans. Inf. Syst. 20 4 (oct 2002) 422\u2013446. 10.1145\/582415.582418","DOI":"10.1145\/582415.582418"},{"key":"e_1_3_3_2_26_2","unstructured":"Manu Joseph. 2021. PyTorch Tabular: A Framework for Deep Learning with Tabular Data. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/Arxiv-2104.13638 (2021)."},{"key":"e_1_3_3_2_27_2","volume-title":"International Conference on Learning Representations","author":"Katzir Liran","year":"2021","unstructured":"Liran Katzir, Gal Elidan, and Ran El-Yaniv. 2021. Net-{DNF}: Effective Deep Modeling of Tabular Data. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=73WTGs96kho"},{"key":"e_1_3_3_2_28_2","volume-title":"Advances in Neural Information Processing Systems","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0Von Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.), Vol.\u00a030. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf"},{"key":"e_1_3_3_2_29_2","doi-asserted-by":"publisher","unstructured":"Alex Krizhevsky Ilya Sutskever and Geoffrey\u00a0E. Hinton. 2017. ImageNet Classification with Deep Convolutional Neural Networks. Commun. ACM 60 6 (may 2017) 84\u201390. 10.1145\/3065386","DOI":"10.1145\/3065386"},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"crossref","unstructured":"Yiwen Liao Raphael Latty and Binh Yang. 2020. Feature Selection Using Batch-Wise Attenuation and Feature Mask Normalization. 2021 International Joint Conference on Neural Networks (IJCNN) (2020) 1\u20139. https:\/\/api.semanticscholar.org\/CorpusID:225067574","DOI":"10.1109\/IJCNN52387.2021.9533531"},{"key":"e_1_3_3_2_31_2","volume-title":"Advances in Neural Information Processing Systems","author":"Lundberg Scott\u00a0M","year":"2017","unstructured":"Scott\u00a0M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0Von Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.), Vol.\u00a030. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/8a20a8621978632d76c43dfd28b67767-Paper.pdf"},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045561"},{"key":"e_1_3_3_2_33_2","doi-asserted-by":"publisher","unstructured":"Daniele Micci-Barreca. 2001. A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems. SIGKDD Explor. Newsl. 3 1 (jul 2001) 27\u201332. 10.1145\/507533.507538","DOI":"10.1145\/507533.507538"},{"key":"e_1_3_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1146"},{"key":"e_1_3_3_2_35_2","volume-title":"8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020","author":"Popov Sergei","year":"2020","unstructured":"Sergei Popov, Stanislav Morozov, and Artem Babenko. 2020. Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net. https:\/\/openreview.net\/forum?id=r1eiu2VtwH"},{"key":"e_1_3_3_2_36_2","volume-title":"Advances in Neural Information Processing Systems","author":"Prokhorenkova Liudmila","year":"2018","unstructured":"Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna\u00a0Veronika Dorogush, and Andrey Gulin. 2018. CatBoost: unbiased boosting with categorical features. In Advances in Neural Information Processing Systems, S.\u00a0Bengio, H.\u00a0Wallach, H.\u00a0Larochelle, K.\u00a0Grauman, N.\u00a0Cesa-Bianchi, and R.\u00a0Garnett (Eds.), Vol.\u00a031. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper\/2018\/file\/14491b756b3a51daac41c24863285549-Paper.pdf"},{"key":"e_1_3_3_2_37_2","unstructured":"Avanti Shrikumar P. Greenside and A. Kundaje. 2017. Learning Important Features Through Propagating Activation Differences. International Conference On Machine Learning (2017)."},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"publisher","unstructured":"Ravid Shwartz-Ziv and Amitai Armon. 2022. Tabular data: Deep learning is not all you need. Inf. Fusion 81 (2022) 84\u201390. 10.1016\/j.inffus.2021.11.011","DOI":"10.1016\/j.inffus.2021.11.011"},{"key":"e_1_3_3_2_39_2","doi-asserted-by":"publisher","unstructured":"David Silver Aja Huang Chris\u00a0J. Maddison Arthur Guez Laurent Sifre George van\u00a0den Driessche Julian Schrittwieser Ioannis Antonoglou Veda Panneershelvam Marc Lanctot Sander Dieleman Dominik Grewe John Nham Nal Kalchbrenner Ilya Sutskever Timothy Lillicrap Madeleine Leach Koray Kavukcuoglu Thore Graepel and Demis Hassabis. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529 7587 (01 Jan 2016) 484\u2013489. 10.1038\/nature16961","DOI":"10.1038\/nature16961"},{"key":"e_1_3_3_2_40_2","unstructured":"Rupesh\u00a0Kumar Srivastava Klaus Greff and J\u00fcrgen Schmidhuber. 2015. Highway Networks. ArXiv abs\/1505.00387 (2015)."},{"key":"e_1_3_3_2_41_2","doi-asserted-by":"publisher","unstructured":"Richard Tomsett Dan Harborne Supriyo Chakraborty Prudhvi Gurram and Alun Preece. 2020. Sanity Checks for Saliency Metrics. Proceedings of the AAAI Conference on Artificial Intelligence 34 04 (Apr. 2020) 6021\u20136029. 10.1609\/aaai.v34i04.6064","DOI":"10.1609\/aaai.v34i04.6064"},{"key":"e_1_3_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2015.7344817"},{"key":"e_1_3_3_2_43_2","unstructured":"Yutaro Yamada Ofir Lindenbaum Sahand\u00a0N. Negahban and Yuval Kluger. 2018. Deep supervised feature selection using Stochastic Gates. ArXiv abs\/1810.04247 (2018). https:\/\/api.semanticscholar.org\/CorpusID:59413892"},{"key":"e_1_3_3_2_44_2","unstructured":"Ligeng Zhu. 2013. THOP: PyTorch-OpCounter. https:\/\/github.com\/Lyken17\/pytorch-OpCounter."}],"event":{"name":"CODS 2025: 13th ACM IKDD International Conference on Data Science","location":"Pune India","acronym":"CODS 2025"},"container-title":["Proceedings of the 13th ACM IKDD International Conference on Data Science"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3799830.3799835","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T07:12:34Z","timestamp":1777014754000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3799830.3799835"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,17]]},"references-count":43,"alternative-id":["10.1145\/3799830.3799835","10.1145\/3799830"],"URL":"https:\/\/doi.org\/10.1145\/3799830.3799835","relation":{},"subject":[],"published":{"date-parts":[[2025,12,17]]},"assertion":[{"value":"2026-04-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}