112 citations · 386 across the 16 of their papers we have counts for
17 papers
Forward Index Compression for Learned Sparse Retrieval
Sebastian Bruch, Martino Fontana, Franco Maria Nardini +2
Text retrieval using learned sparse representations of queries and documents has, over the years, evolved into a highly effective approach to search. It is thanks to recent advance…
Efficient Sketching and Nearest Neighbor Search Algorithms for Sparse Vector Sets
Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli +1
Sparse embeddings of data form an attractive class due to their inherent interpretability: Every dimension is tied to a term in some vocabulary, making it easy to visually decipher…
Investigating the Scalability of Approximate Sparse Retrieval Algorithms to Massive Datasets
Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli +2
Learned sparse text embeddings have gained popularity due to their effectiveness in top-k retrieval and inherent interpretability. Their distributional idiosyncrasies, however, hav…
Pairing Clustered Inverted Indexes with kNN Graphs for Fast Approximate Retrieval over Learned Sparse Representations
Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli +1
Learned sparse representations form an effective and interpretable class of embeddings for text retrieval. While exact top-k retrieval over such embeddings faces efficiency challen…
Optimistic Query Routing in Clustering-based Approximate Maximum Inner Product Search
Sebastian Bruch, Aditya Krishnan, Franco Maria Nardini
Clustering-based nearest neighbor search is an effective method in which points are partitioned into geometric shards to form an index, with only a few shards searched during query…
Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations
Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli +1
Learned sparse representations form an attractive class of contextual embeddings for text retrieval. That is so because they are effective models of relevance and are interpretable…
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search
Thomas Vecchiato, Claudio Lucchese, Franco Maria Nardini +1
A critical piece of the modern information retrieval puzzle is approximate nearest neighbor search. Its objective is to return a set of data points that are closest to a query…
Bridging Dense and Sparse Maximum Inner Product Search
Sebastian Bruch, Franco Maria Nardini, Amir Ingber +1
Maximum inner product search (MIPS) over dense and sparse vectors have progressed independently in a bifurcated literature for decades; the latter is better known as top- retrie…
Efficient and Effective Tree-based and Neural Learning to Rank
Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini
This monograph takes a step towards promoting the study of efficiency in the era of neural information retrieval by offering a comprehensive survey of the literature on efficiency…
An Approximate Algorithm for Maximum Inner Product Search over Streaming Sparse Vectors
Sebastian Bruch, Franco Maria Nardini, Amir Ingber +1
Maximum Inner Product Search or top-k retrieval on sparse vectors is well-understood in information retrieval, with a number of mature algorithms that solve it exactly. However, al…
Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library
Mathieu Guillame-Bert, Sebastian Bruch, Richard Stotz +1
Yggdrasil Decision Forests is a library for the training, serving and interpretation of decision forest models, targeted both at research and production work, implemented in C++, a…
An Analysis of Fusion Functions for Hybrid Retrieval
Sebastian Bruch, Siyu Gai, Amir Ingber
We study hybrid search in text retrieval where lexical and semantic search are fused together with the intuition that the two are complementary in how they model relevance. In part…
Modeling Text with Decision Forests using Categorical-Set Splits
Mathieu Guillame-Bert, Sebastian Bruch, Petr Mitrichev +2
Decision forest algorithms typically model data by learning a binary tree structure recursively where every node splits the feature space into two sub-regions, sending examples int…
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation
Sebastian Bruch, Jan Pfeifer, Mathieu Guillame-bert
Axis-aligned decision forests have long been the leading class of machine learning algorithms for modeling tabular data. In many applications of machine learning such as learning-t…
An Alternative Cross Entropy Loss for Learning-to-Rank
Sebastian Bruch
Listwise learning-to-rank methods form a powerful class of ranking algorithms that are widely adopted in applications such as information retrieval. These algorithms learn to rank…
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank
Rama Kumar Pasumarthi, Sebastian Bruch, Xuanhui Wang +7
Learning-to-Rank deals with maximizing the utility of a list of examples presented to the user, with items of higher relevance being prioritized. It has several practical applicati…
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks
Qingyao Ai, Xuanhui Wang, Sebastian Bruch +3
While in a classification or a regression setting a label or a value is assigned to each individual document, in a ranking setting we determine the relevance ordering of the entire…