Roberto I. Oliveira — accepted AI-conference papers

2024

BlockBoost: Scalable and Efficient Blocking through Boosting

Thiago Ramos, Rodrigo Loro Schuller, Alex Akira Okuno, Lucas Nissenbaum, Roberto I Oliveira, Paulo Orenstein

AISTATS 2024poster

As datasets grow larger, matching and merging entries from different databases has become a costly task in modern data pipelines. To avoid expensive comparisons between entries, blocking similar items is a popular preprocessing step. In this paper, we introduce BlockBoost, a novel boosting-based met…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2023

AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification

Daniel Csillag, Lucas Monteiro Paes, Thiago Ramos, João Vitor Romano, Rodrigo Schuller, Roberto B. Seixas +2

AAAI 2023technical

Accurately predicting the volume of amniotic fluid is fundamental to assessing pregnancy risks, though the task usually requires many hours of laborious work by medical experts. In this paper, we present AmnioML, a machine learning solution that leverages deep learning and conformal prediction to o…

Cited by 11 Source ↗PDF ↗code ↗Scholar ↗

2022

ExactBoost: Directly Boosting the Margin in Combinatorial and Non-decomposable Metrics

Daniel Csillag, Carolina Piazza, Thiago Ramos, João Vitor Romano, Roberto I. Oliveira, Paulo Orenstein

AISTATS 2022poster

Many classification algorithms require the use of surrogate losses when the intended loss function is combinatorial or non-decomposable. This paper introduces a fast and exact stagewise optimization algorithm, dubbed ExactBoost, that boosts stumps to the actual loss function. By developing a novel e…

Cited by 0 Source ↗PDF ↗Scholar ↗