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ORCIDSebastian Bruch
Person information
- affiliation: Google Research
- affiliation (former): University of Maryland, College Park, MD, USA
Other persons with the same name
- Nima Asadi — disambiguation page
- Nima Asadi 0002 — Sharif University of Technology, Tehran
Other persons with a similar name
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2020 – today
- 2024
[b2]Sebastian Bruch
:
Foundations of Vector Retrieval. Springer 2024, ISBN 978-3-031-55181-9, pp. 3-185- [j8]Sebastian Bruch, Siyu Gai, Amir Ingber:
An Analysis of Fusion Functions for Hybrid Retrieval. ACM Trans. Inf. Syst. 42(1): 20:1-20:35 (2024) - [j7]Sebastian Bruch, Franco Maria Nardini, Amir Ingber, Edo Liberty:
An Approximate Algorithm for Maximum Inner Product Search over Streaming Sparse Vectors. ACM Trans. Inf. Syst. 42(2): 42:1-42:43 (2024) - [j6]Sebastian Bruch, Claudio Lucchese, Maria Maistro, Franco Maria Nardini:
Special Section on Efficiency in Neural Information Retrieval. ACM Trans. Inf. Syst. 42(5): 113:1-113:4 (2024) - [c25]Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini:
Pairing Clustered Inverted Indexes with κ-NN Graphs for Fast Approximate Retrieval over Learned Sparse Representations. CIKM 2024: 3642-3646 - [c24]Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini:
Seismic: Efficient and Effective Retrieval over Learned Sparse Representation. IIR 2024: 103-106 - [c23]Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini:
Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations. SIGIR 2024: 152-162 - [c22]Thomas Vecchiato, Claudio Lucchese, Franco Maria Nardini, Sebastian Bruch:
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search. SIGIR 2024: 2261-2265 - [i18]Sebastian Bruch:
Foundations of Vector Retrieval. CoRR abs/2401.09350 (2024) - [i17]Thomas Vecchiato, Claudio Lucchese, Franco Maria Nardini, Sebastian Bruch:
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search. CoRR abs/2404.11731 (2024) - [i16]Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini:
Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations. CoRR abs/2404.18812 (2024) - [i15]Sebastian Bruch, Aditya Krishnan, Franco Maria Nardini:
Optimistic Query Routing in Clustering-based Approximate Maximum Inner Product Search. CoRR abs/2405.12207 (2024) - [i14]Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini:
Pairing Clustered Inverted Indexes with kNN Graphs for Fast Approximate Retrieval over Learned Sparse Representations. CoRR abs/2408.04443 (2024) - 2023
- [j5]Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini:
Efficient and Effective Tree-based and Neural Learning to Rank. Found. Trends Inf. Retr. 17(1): 1-123 (2023) - [c21]Mathieu Guillame-Bert, Sebastian Bruch, Richard Stotz, Jan Pfeifer:
Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library. KDD 2023: 4068-4077 - [c20]Sebastian Bruch, Joel Mackenzie, Maria Maistro, Franco Maria Nardini:
ReNeuIR at SIGIR 2023: The Second Workshop on Reaching Efficiency in Neural Information Retrieval. SIGIR 2023: 3456-3459 - [c19]Julius Adelt, Sebastian Bruch, Paula Herber, Mathis Niehage, Anne Remke:
Shielded Learning for Resilience and Performance Based on Statistical Model Checking in Simulink. AISoLA 2023: 94-118 - [c18]Salvatore Orlando, Gabriella Pasi, Sebastian Bruch, Claudio Lucchese, Xiaohui Tao:
Preface WI-IAT 2023. WI/IAT 2023: xviii-xxi - [i13]Sebastian Bruch, Franco Maria Nardini, Amir Ingber, Edo Liberty:
An Approximate Algorithm for Maximum Inner Product Search over Streaming Sparse Vectors. CoRR abs/2301.10622 (2023) - [i12]Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini:
Efficient and Effective Tree-based and Neural Learning to Rank. CoRR abs/2305.08680 (2023) - [i11]Sebastian Bruch, Franco Maria Nardini, Amir Ingber, Edo Liberty:
Bridging Dense and Sparse Maximum Inner Product Search. CoRR abs/2309.09013 (2023) - 2022
- [j4]Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini:
Report on the 1st Workshop on Reaching Efficiency in Neural Information Retrieval (ReNeuIR 2022) at SIGIR 2022. SIGIR Forum 56(2): 12:1-12:14 (2022) - [c17]Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini:
ReNeuIR: Reaching Efficiency in Neural Information Retrieval. SIGIR 2022: 3462-3465 - [i10]Sebastian Bruch, Siyu Gai, Amir Ingber:
An Analysis of Fusion Functions for Hybrid Retrieval. CoRR abs/2210.11934 (2022) - [i9]Mathieu Guillame-Bert, Sebastian Bruch, Richard Stotz, Jan Pfeifer:
Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library. CoRR abs/2212.02934 (2022) - 2021
- [c16]Sebastian Bruch:
An Alternative Cross Entropy Loss for Learning-to-Rank. WWW 2021: 118-126 - 2020
- [c15]Sebastian Bruch, Shuguang Han, Michael Bendersky, Marc Najork:
A Stochastic Treatment of Learning to Rank Scoring Functions. WSDM 2020: 61-69 - [i8]Sebastian Bruch, Jan Pfeifer, Mathieu Guillame-Bert:
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation. CoRR abs/2007.14761 (2020) - [i7]Mathieu Guillame-Bert, Sebastian Bruch, Petr Mitrichev, Petr Mikheev, Jan Pfeifer:
Modeling Text with Decision Forests using Categorical-Set Splits. CoRR abs/2009.09991 (2020)
2010 – 2019
- 2019
- [c14]Sebastian Bruch, Xuanhui Wang, Michael Bendersky, Marc Najork:
An Analysis of the Softmax Cross Entropy Loss for Learning-to-Rank with Binary Relevance. ICTIR 2019: 75-78 - [c13]Qingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi, Michael Bendersky, Marc Najork:
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks. ICTIR 2019: 85-92 - [c12]Rama Kumar Pasumarthi, Sebastian Bruch, Michael Bendersky, Xuanhui Wang:
Neural Learning to Rank using TensorFlow Ranking: A Hands-on Tutorial. ICTIR 2019: 253-254 - [c11]Rama Kumar Pasumarthi, Sebastian Bruch, Xuanhui Wang, Cheng Li, Michael Bendersky, Marc Najork, Jan Pfeifer, Nadav Golbandi, Rohan Anil, Stephan Wolf:
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank. KDD 2019: 2970-2978 - [c10]Sebastian Bruch, Masrour Zoghi, Michael Bendersky, Marc Najork:
Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks. SIGIR 2019: 1241-1244 - [c9]Claudio Lucchese, Franco Maria Nardini, Rama Kumar Pasumarthi, Sebastian Bruch, Michael Bendersky, Xuanhui Wang, Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke:
Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning. SIGIR 2019: 1419-1420 - [i6]Sebastian Bruch:
An Alternative Cross Entropy Loss for Learning-to-Rank. CoRR abs/1911.09798 (2019) - 2018
- [i5]Rama Kumar Pasumarthi, Xuanhui Wang, Cheng Li, Sebastian Bruch, Michael Bendersky, Marc Najork, Jan Pfeifer, Nadav Golbandi, Rohan Anil, Stephan Wolf:
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank. CoRR abs/1812.00073 (2018) - 2014
- [j3]Nima Asadi, Jimmy Lin, Arjen P. de Vries:
Runtime Optimizations for Tree-Based Machine Learning Models. IEEE Trans. Knowl. Data Eng. 26(9): 2281-2292 (2014) - 2013
- [b1]Nima Asadi:
Multi-Stage Search Architectures for Streaming Documents. University of Maryland, College Park, MD, USA, 2013 - [j2]Nima Asadi, Jimmy Lin:
Document vector representations for feature extraction in multi-stage document ranking. Inf. Retr. 16(6): 747-768 (2013) - [j1]Nima Asadi, Jimmy Lin:
Fast candidate generation for real-time tweet search with bloom filter chains. ACM Trans. Inf. Syst. 31(3): 13 (2013) - [c8]Nima Asadi, Jimmy Lin:
Training Efficient Tree-Based Models for Document Ranking. ECIR 2013: 146-157 - [c7]Nima Asadi, Jimmy Lin, Michael Busch:
Dynamic memory allocation policies for postings in real-time Twitter search. KDD 2013: 1186-1194 - [c6]Nima Asadi, Jimmy Lin:
Effectiveness/efficiency tradeoffs for candidate generation in multi-stage retrieval architectures. SIGIR 2013: 997-1000 - [i4]Nima Asadi, Jimmy Lin, Michael Busch:
Dynamic Memory Allocation Policies for Postings in Real-Time Twitter Search. CoRR abs/1302.5302 (2013) - [i3]Nima Asadi, Jimmy Lin:
Fast, Incremental Inverted Indexing in Main Memory for Web-Scale Collections. CoRR abs/1305.0699 (2013) - 2012
- [c5]Nima Asadi, Jimmy Lin:
Fast candidate generation for two-phase document ranking: postings list intersection with bloom filters. CIKM 2012: 2419-2422 - [c4]Ke Zhai, Jordan L. Boyd-Graber, Nima Asadi, Mohamad L. Alkhouja:
Mr. LDA: a flexible large scale topic modeling package using variational inference in MapReduce. WWW 2012: 879-888 - [i2]Nima Asadi, Jimmy Lin, Arjen P. de Vries:
Runtime Optimizations for Prediction with Tree-Based Models. CoRR abs/1212.2287 (2012) - 2011
- [c3]Nima Asadi, Donald Metzler, Tamer Elsayed, Jimmy Lin:
Pseudo test collections for learning web search ranking functions. SIGIR 2011: 1073-1082 - [c2]Nima Asadi, Donald Metzler, Jimmy Lin:
Cross-corpus relevance projection. SIGIR 2011: 1163-1164 - [i1]Ke Zhai, Jordan L. Boyd-Graber, Nima Asadi:
Using Variational Inference and MapReduce to Scale Topic Modeling. CoRR abs/1107.3765 (2011) - 2010
- [c1]Tamer Elsayed, Nima Asadi, Lidan Wang, Jimmy Lin, Donald Metzler:
UMD and USC/ISI: TREC 2010 Web Track Experiments with Ivory. TREC 2010
Coauthor Index
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last updated on 2024-12-02 21:32 CET by the dblp team
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