Guilherme R. Borges

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dblp:155/8945 · also Guilherme Resende Borges · DBLP profile ↗

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2ranked-venue papers

1as first author

0since 2021 · last 2020

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 1 paper		Probabilistic and Bayesian machine learning · 100%
Databases, data mining, and information retrieval 1 paper		Data mining · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Probabilistic and Bayesian machine learning › causal inference › causal discovery granger causality	0.3	1	2018	Fast Estimation of Causal Interactions using Wold Processes · NeurIPS 2018
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes › point process multivariate point process	0.3	1	2018	Fast Estimation of Causal Interactions using Wold Processes · NeurIPS 2018
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes point process	0.3	1	2018	Fast Estimation of Causal Interactions using Wold Processes · NeurIPS 2018
Data mining › causal inference › causal modeling causal discovery	0.3	1	2018	Fast Estimation of Causal Interactions using Wold Processes · NeurIPS 2018
Data mining › causal inference › causal modeling › causal discovery granger causality	0.3	1	2018	Fast Estimation of Causal Interactions using Wold Processes · NeurIPS 2018

Methods — techniques the papers use, named apart from their topics

wold process · 0.7MCMC · 0.7

Year	Publication	Venue	Position
2020	Networked Point Process Models Under the Lens of Scrutiny Guilherme R. Borges, Flavio Figueiredo, Renato Assunção, Pedro O. S. Vaz de Melo	ECML/PKDD (1)	1
2018	Fast Estimation of Causal Interactions using Wold Processes abstract We here focus on the task of learning Granger causality matrices for multivariate point processes. In order to accomplish this task, our work is the first to explore the use of Wold processes. By doing so, we are able to develop asymptotically fast MCMC learning algorithms. With $N$ being the total number of events and $K$ the number of processes, our learning algorithm has a $O(N(\,\log(N)\,+\,\log(K)))$ cost per iteration. This is much faster than the $O(N^3\,K^2)$ or $O(K^3)$ for the state of the art. Our approach, called GrangerBusca, is validated on nine datasets. This is an advance in relation to most prior efforts which focus mostly on subsets of the Memetracker data. Regarding accuracy, GrangerBusca is three times more accurate (in Precision@10) than the state of the art for the commonly explored subsets Memetracker. Due to GrangerBusca's much lower training complexity, our approach is the only one able to train models for larger, full, sets of data. Flavio Figueiredo, Guilherme R. Borges, Pedro O. S. Vaz de Melo, Renato Assunção	NeurIPS	2