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  • 2024

    Gandalf: Learning Label-label Correlations in Extreme Multi-label Classification via Label Features

    Kharbanda, S., Gupta, D., Schultheis, E., Banerjee, A., Hsieh, C. J. & Babbar, R., 25 Aug 2024, KDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. U.S. A.: Association for Computing Machinery, p. 1360-1371 12 p. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    Open Access
  • 2023

    Generalized test utilities for long-tail performance in extreme multi-label classification

    Schultheis, E., Wydmuch, M., Kotłowski, W., Babbar, R. & Dembczyński, K., 9 Nov 2023, 37th Conference on Neural Information Processing Systems, NeurIPS 2023. Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M. & Levine, S. (eds.). Neural Information Processing Systems Foundation, Inc., Vol. 36. 35 p.

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    Open Access
    File
    28 Downloads (Pure)
  • InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification

    Kharbanda, S., Banerjee, A., Gupta, D., Palrecha, A. & Babbar, R., 18 Jul 2023, SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, p. 760-769 10 p. (SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    3 Citations (SciVal)
  • Towards Memory-Efficient Training for Extremely Large Output Spaces – Learning with 670k Labels on a Single Commodity GPU

    Schultheis, E. & Babbar, R., 17 Sept 2023, Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, ECML PKDD 2023, Proceedings. Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E. & Bonchi, F. (eds.). Springer Science and Business Media Deutschland GmbH, p. 689-704 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 14171 LNAI).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

  • 2022

    CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification

    Kharbanda, S., Banerjee, A., Schultheis, E. & Babbar, R., 9 Dec 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural Information Processing Systems Foundation, Inc., (Advances in Neural Information Processing Systems; vol. 35).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    Open Access
    16 Citations (SciVal)
  • Explainable Publication Year Prediction of Eighteenth Century Texts with the BERT Model

    Rastas, I., Ryan, Y., Tiihonen, I., Qaraei, M., Repo, L., Babbar, R., Mäkelä, E., Tolonen, M. & Ginter, F., 25 May 2022, LChange 2022 - 3rd International Workshop on Computational Approaches to Historical Language Change 2022, Proceedings of the Workshop. Tahmasebi, N., Montariol, S., Kutuzov, A., Hengchen, S., Dubossarsky, H. & Borin, L. (eds.). Association for Computational Linguistics (ACL), p. 68-77 10 p. (LChange 2022 - 3rd International Workshop on Computational Approaches to Historical Language Change 2022, Proceedings of the Workshop).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    14 Citations (SciVal)
  • On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification

    Schultheis, E., Wydmuch, M., Babbar, R. & Dembczynski, K., 14 Aug 2022, KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, p. 1547-1557 11 p. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    Open Access
    18 Citations (SciVal)
  • 2021

    Convex surrogates for unbiased loss functions in extreme classification with missing labels

    Qaraei, M., Schultheis, E., Gupta, P. & Babbar, R., 3 Jun 2021, The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021. Leskovec, J. & Grobelnik, M. (eds.). New York, U. S. A.: Association for Computing Machinery, p. 3711-3720 10 p. (The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    18 Citations (SciVal)
  • Propensity-scored Probabilistic Label Trees

    Wydmuch, M., Jasinska-Kobus, K., Babbar, R. & Dembczynski, K., 11 Jul 2021, SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, p. 2252-2256 5 p. 3463084. (SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    6 Citations (SciVal)
  • 2020

    Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading

    Mohammed, T., Joe-Wong, C., Babbar, R. & Francesco, M. D., Jul 2020, INFOCOM 2020 - IEEE Conference on Computer Communications. IEEE, p. 854-863 10 p. 9155237. (Proceedings - IEEE INFOCOM; vol. 2020-July).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    157 Citations (SciVal)
  • Neural Architecture Search for Extreme Multi-label Text Classification

    Pauletto, L., Amini, M. R., Babbar, R. & Winckler, N., 19 Nov 2020, Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings. Yang, H., Pasupa, K., Leung, A.C.-S., Kwok, J. T., Chan, J. H. & King, I. (eds.). Springer Science and Business Media Deutschland GmbH, p. 282-293 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12534 LNCS).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    2 Citations (SciVal)
  • Why state-of-the-art deep learning barely works as good as a linear classifier in extreme multi-label text classification

    Qaraei, M., Khandagale, S. & Babbar, R., 2 Oct 2020, ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN (i6doc.com), p. 223-228 6 p. (ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

  • 2019

    A simple and effective scheme for data pre-processing in extreme classification

    Khandagale, S. & Babbar, R., 26 Apr 2019, ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN (i6doc.com), p. 67-72 6 p. (ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    1 Citation (SciVal)
  • 2018

    Extreme multi-label classification for information retrieval

    Dembczyński, K. & Babbar, R., 23 Mar 2018, Advances in Information Retrieval - 40th European Conference on IR Research, ECIR 2018, Proceedings. Azzopardi, L., Pasi, G., Hanbury, A. & Piwowarski, B. (eds.). Springer Verlag, p. 839-840 2 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10772 LNCS).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

  • 2017

    DiSMEC - Distributed sparse machines for extreme multi-label classification

    Babbar, R. & Schölkopf, B., 2 Feb 2017, WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, p. 721-729 9 p. (WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    Open Access
    196 Citations (SciVal)
  • 2016

    TerseSVM: A scalable approach for learning compact models in large-scale classification

    Babbar, R., Maundet, K. & Schölkopf, B., 11 Aug 2016, (E-pub ahead of print) 16th SIAM International Conference on Data Mining 2016, SDM 2016. Venkatasubramanian, S. C. & Meira, W. (eds.). Society for Industrial and Applied Mathematics Publications, p. 234-242 9 p. (16th SIAM International Conference on Data Mining 2016, SDM 2016).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    Open Access
    4 Citations (SciVal)
  • 2015

    Efficient model selection for regularized classification by exploiting unlabeled data

    Balikas, G., Partalas, I., Gaussier, E., Babbar, R. & Amini, M. R., 22 Nov 2015, Advances in Intelligent Data Analysis XIV - 14th International Symposium, IDA 2015, Proceedings. De Bie, T., van Leeuwen, M. & Fromont, E. (eds.). Springer Verlag, p. 25-36 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9385).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    4 Citations (SciVal)
  • 2014

    Re-ranking approach to classification in large-scale power-law distributed category systems

    Babbar, R., Partalas, I., Gaussier, E. & Amini, M. R., 3 Jul 2014, SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, p. 1059-1062 4 p. (SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    6 Citations (SciVal)
  • 2013

    Comparative classifier evaluation for web-scale taxonomies using power law

    Babbar, R., Partalas, I., Metzig, C., Gaussier, E. & Amini, M. R., 26 May 2013, The Semantic Web: ESWC 2013 Satellite Events, Revised Selected Papers. Springer Heidelberg, Vol. 7955. p. 310-311 2 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 7955 LNCS).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    Open Access
  • Maximum-margin framework for training data synchronization in large-scale hierarchical classification

    Babbar, R., Partalas, I., Gaussier, E. & Amini, M. R., 3 Nov 2013, Neural Information Processing : 20th International Conference, ICONIP 2013, Proceedings. PART 1 ed. Springer Verlag, Vol. 8826. p. 336-343 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 8226 LNCS, no. PART 1).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    8 Citations (SciVal)
  • 2012

    Adaptive classifier selection in large-scale hierarchical classification

    Partalas, I., Babbar, R., Gaussier, E. & Amblard, C., 12 Nov 2012, Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings. PART 3 ed. Vol. 7665. p. 612-619 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 7665 LNCS, no. PART 3).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    1 Citation (SciVal)
  • On empirical tradeoffs in large scale hierarchical classification

    Babbar, R., Partalas, I., Gaussier, E. & Amblard, C., 2012, CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management. p. 2299-2302 4 p. (ACM International Conference Proceeding Series).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    1 Citation (SciVal)
  • 2010

    Clustering based approach to learning regular expressions over large alphabet for noisy unstructured text

    Babbar, R. & Singh, N., 26 Oct 2010, AND '10: Proceedings of the fourth workshop on Analytics for noisy unstructured text data. Association for Computing Machinery, p. 43-50 8 p. (CIKM '10: International Conference on Information and Knowledge Management).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    24 Citations (SciVal)