Sebastian Bruch

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 33

Top Topics

Information Retrieval

Scoring Function

Top Venues

ACM Trans. Inf. Syst.

Publications

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) (2024)
Sebastian Bruch
Foundations of Vector Retrieval. CoRR (2024)
Thomas Vecchiato, Claudio Lucchese, Franco Maria Nardini, Sebastian Bruch
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search. SIGIR (2024)
Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini
Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations. CoRR (2024)
Sebastian Bruch, Aditya Krishnan, Franco Maria Nardini
Optimistic Query Routing in Clustering-based Approximate Maximum Inner Product Search. CoRR (2024)
Sebastian Bruch, Siyu Gai, Amir Ingber
An Analysis of Fusion Functions for Hybrid Retrieval. ACM Trans. Inf. Syst. 42 (1) (2024)
Thomas Vecchiato, Claudio Lucchese, Franco Maria Nardini, Sebastian Bruch
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search. CoRR (2024)
Sebastian Bruch, Claudio Lucchese, Maria Maistro, Franco Maria Nardini
Special Section on Efficiency in Neural Information Retrieval. ACM Trans. Inf. Syst. 42 (5) (2024)
Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini
Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations. SIGIR (2024)
Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini
Efficient and Effective Tree-based and Neural Learning to Rank. CoRR (2023)
Sebastian Bruch, Franco Maria Nardini, Amir Ingber, Edo Liberty
Bridging Dense and Sparse Maximum Inner Product Search. CoRR (2023)
Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini
Efficient and Effective Tree-based and Neural Learning to Rank. Found. Trends Inf. Retr. 17 (1) (2023)
Mathieu Guillame-Bert, Sebastian Bruch, Richard Stotz, Jan Pfeifer
Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library. KDD (2023)
Salvatore Orlando, Gabriella Pasi, Sebastian Bruch, Claudio Lucchese, Xiaohui Tao
Preface WI-IAT 2023. WI/IAT (2023)
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)
Sebastian Bruch, Franco Maria Nardini, Amir Ingber, Edo Liberty
An Approximate Algorithm for Maximum Inner Product Search over Streaming Sparse Vectors. CoRR (2023)
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)
Mathieu Guillame-Bert, Sebastian Bruch, Richard Stotz, Jan Pfeifer
Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library. CoRR (2022)
Sebastian Bruch, Siyu Gai, Amir Ingber
An Analysis of Fusion Functions for Hybrid Retrieval. CoRR (2022)
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) (2022)
Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini
ReNeuIR: Reaching Efficiency in Neural Information Retrieval. SIGIR (2022)
Sebastian Bruch
An Alternative Cross Entropy Loss for Learning-to-Rank. WWW (2021)
Sebastian Bruch, Shuguang Han, Michael Bendersky, Marc Najork
A Stochastic Treatment of Learning to Rank Scoring Functions. WSDM (2020)
Mathieu Guillame-Bert, Sebastian Bruch, Petr Mitrichev, Petr Mikheev, Jan Pfeifer
Modeling Text with Decision Forests using Categorical-Set Splits. CoRR (2020)
Sebastian Bruch, Jan Pfeifer, Mathieu Guillame-Bert
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation. CoRR (2020)
Qingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi, Michael Bendersky, Marc Najork
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks. ICTIR (2019)
Sebastian Bruch, Masrour Zoghi, Michael Bendersky, Marc Najork
Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks. SIGIR (2019)
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)
Rama Kumar Pasumarthi, Sebastian Bruch, Michael Bendersky, Xuanhui Wang
Neural Learning to Rank using TensorFlow Ranking: A Hands-on Tutorial. ICTIR (2019)
Sebastian Bruch
An Alternative Cross Entropy Loss for Learning-to-Rank. CoRR (2019)
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)
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)
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 (2018)