| Mr. LDA: A flexible large scale topic modeling package using variational inference in mapreduce K Zhai, J Boyd-Graber, SN Bruch, ML Alkhouja Proceedings of the 21st international conference on World Wide Web, 879-888, 2012 | 210 | 2012 |
| TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank RK Pasumarthi, S Bruch, X Wang, C Li, M Bendersky, M Najork, J Pfeifer, ... Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 140 | 2019 |
| Effectiveness/efficiency tradeoffs for candidate generation in multi-stage retrieval architectures SN Bruch, J Lin Proceedings of the 36th international ACM SIGIR conference on Research and …, 2013 | 106 | 2013 |
| Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks Q Ai, X Wang, S Bruch, N Golbandi, M Bendersky, M Najork | 89 | 2019 |
| Runtime optimizations for tree-based machine learning models SN Bruch, J Lin, AP De Vries IEEE transactions on Knowledge and Data Engineering 26 (9), 2281-2292, 2013 | 87 | 2013 |
| An Analysis of the Softmax Cross Entropy Loss for Learning-to-Rank with Binary Relevance S Bruch, X Wang, M Bendersky, M Najork Proceedings of the 2019 ACM SIGIR International Conference on Theory of …, 2019 | 73 | 2019 |
| A Stochastic Treatment of Learning to Rank Scoring Functions S Bruch, S Han, M Bendersky, M Najork Proceedings of the 13th ACM International Conference on Web Search and Data …, 2020 | 63 | 2020 |
| Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks S Bruch, M Zoghi, M Bendersky, M Najork Proceedings of the 42nd International ACM SIGIR Conference on Research and …, 2019 | 62 | 2019 |
| Pseudo test collections for learning web search ranking functions SN Bruch, D Metzler, T Elsayed, J Lin Proceedings of the 34th international ACM SIGIR conference on Research and …, 2011 | 45 | 2011 |
| Training efficient tree-based models for document ranking SN Bruch, J Lin European Conference on Information Retrieval, 146-157, 2013 | 40 | 2013 |
| Document vector representations for feature extraction in multi-stage document ranking SN Bruch, J Lin Information retrieval 16, 747-768, 2013 | 39 | 2013 |
| Fast candidate generation for real-time tweet search with bloom filter chains SN Bruch, J Lin ACM Transactions on Information Systems (TOIS) 31 (3), 1-36, 2013 | 38 | 2013 |
| An Alternative Cross Entropy Loss for Learning-to-Rank S Bruch Proceedings of The Web Conference 2021 (WWW 2021), 2021 | 35 | 2021 |
| Fast candidate generation for two-phase document ranking: Postings list intersection with Bloom filters SN Bruch, J Lin Proceedings of the 21st ACM international conference on Information and …, 2012 | 27 | 2012 |
| Dynamic memory allocation policies for postings in real-time twitter search SN Bruch, J Lin, M Busch Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013 | 15 | 2013 |
| UMD and USC/ISI: TREC 2010 Web Track Experiments with Ivory. T Elsayed, SN Bruch, L Wang, JJ Lin, D Metzler TREC, 2010 | 12 | 2010 |
| Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning C Lucchese, FM Nardini, RK Pasumarthi, S Bruch, M Bendersky, X Wang, ... Proceedings of the 42nd International ACM SIGIR Conference on Research and …, 2019 | 10 | 2019 |
| Multi-stage search architectures for streaming documents SN Bruch | 10 | 2013 |
| An Analysis of Fusion Functions for Hybrid Retrieval S Bruch, S Gai, A Ingber ACM Transactions on Information Systems 42 (1), 1-35, 2023 | 8 | 2023 |
| ReNeuIR: Reaching Efficiency in Neural Information Retrieval S Bruch, C Lucchese, FM Nardini Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 8 | 2022 |
Jimmy LinUniversity of WaterlooVerified email at uwaterloo.ca
