Search Results for author: Jorge Piazentin Ono

Found 5 papers, 1 papers with code

AttributionScanner: A Visual Analytics System for Model Validation with Metadata-Free Slice Finding

no code implementations12 Jan 2024 Xiwei Xuan, Jorge Piazentin Ono, Liang Gou, Kwan-Liu Ma, Liu Ren

Data slice finding is an emerging technique for validating machine learning (ML) models by identifying and analyzing subgroups in a dataset that exhibit poor performance, often characterized by distinct feature sets or descriptive metadata.

Descriptive

AlphaD3M: Machine Learning Pipeline Synthesis

no code implementations3 Nov 2021 Iddo Drori, Yamuna Krishnamurthy, Remi Rampin, Raoni de Paula Lourenco, Jorge Piazentin Ono, Kyunghyun Cho, Claudio Silva, Juliana Freire

We introduce AlphaD3M, an automatic machine learning (AutoML) system based on meta reinforcement learning using sequence models with self play.

AutoML BIG-bench Machine Learning +4

PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines

1 code implementation arXiv 2020 Jorge Piazentin Ono, Sonia Castelo, Roque Lopez, Enrico Bertini, Juliana Freire, Claudio Silva

In recent years, a wide variety of automated machine learning (AutoML) methods have been proposed to search and generate end-to-end learning pipelines.

Human-Computer Interaction

Visus: An Interactive System for Automatic Machine Learning Model Building and Curation

no code implementations5 Jul 2019 Aécio Santos, Sonia Castelo, Cristian Felix, Jorge Piazentin Ono, Bowen Yu, Sungsoo Hong, Cláudio T. Silva, Enrico Bertini, Juliana Freire

In this paper, we present Visus, a system designed to support the model building process and curation of ML data processing pipelines generated by AutoML systems.

AutoML BIG-bench Machine Learning

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