Search Results for author: Giulio Ermanno Pibiri

Found 8 papers, 7 papers with code

Verifiable Boosted Tree Ensembles

no code implementations22 Feb 2024 Stefano Calzavara, Lorenzo Cazzaro, Claudio Lucchese, Giulio Ermanno Pibiri

Verifiable learning advocates for training machine learning models amenable to efficient security verification.

Verifiable Learning for Robust Tree Ensembles

1 code implementation5 May 2023 Stefano Calzavara, Lorenzo Cazzaro, Giulio Ermanno Pibiri, Nicola Prezza

In this paper, we identify a restricted class of decision tree ensembles, called large-spread ensembles, which admit a security verification algorithm running in polynomial time.

Efficient and Effective Query Auto-Completion

1 code implementation13 May 2020 Simon Gog, Giulio Ermanno Pibiri, Rossano Venturini

Query Auto-Completion (QAC) is an ubiquitous feature of modern textual search systems, suggesting possible ways of completing the query being typed by the user.

Techniques for Inverted Index Compression

2 code implementations28 Aug 2019 Giulio Ermanno Pibiri, Rossano Venturini

The aim of this article is twofold: first, surveying the encoding algorithms suitable for inverted index compression and, second, characterizing the performance of the inverted index through experimentation.

On Slicing Sorted Integer Sequences

1 code implementation1 Jul 2019 Giulio Ermanno Pibiri

Representing sorted integer sequences in small space is a central problem for large-scale retrieval systems such as Web search engines.


Compressed Indexes for Fast Search of Semantic Data

1 code implementation16 Apr 2019 Raffaele Perego, Giulio Ermanno Pibiri, Rossano Venturini

The sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is devising a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations.

Handling Massive N-Gram Datasets Efficiently

1 code implementation25 Jun 2018 Giulio Ermanno Pibiri, Rossano Venturini

This paper deals with the two fundamental problems concerning the handling of large n-gram language models: indexing, that is compressing the n-gram strings and associated satellite data without compromising their retrieval speed; and estimation, that is computing the probability distribution of the strings from a large textual source.

Language Modelling Retrieval

On Optimally Partitioning Variable-Byte Codes

1 code implementation29 Apr 2018 Giulio Ermanno Pibiri, Rossano Venturini

The ubiquitous Variable-Byte encoding is one of the fastest compressed representation for integer sequences.

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