Search Results for author: Eduardo Laber

Found 7 papers, 3 papers with code

Teaching with Limited Information on the Learner's Behaviour

no code implementations ICML 2020 Ferdinando Cicalese, Francisco Sergio de Freitas Filho, Eduardo Laber, Marco Molinaro

We focus on the model of Machine Teaching with a black box learner introduced in [Dasgupta et al., ICML 2019], where the teaching is done interactively without having any knowledge of the Learner's algorithm and class of hypotheses, apart from the fact that it contains the target hypothesis $h^*$.

Time-Constrained Learning

1 code implementation4 Feb 2022 Sergio Filho, Eduardo Laber, Pedro Lazera, Marco Molinaro

Consider a scenario in which we have a huge labeled dataset ${\cal D}$ and a limited time to train some given learner using ${\cal D}$.

Shallow decision trees for explainable $k$-means clustering

1 code implementation29 Dec 2021 Eduardo Laber, Lucas Murtinho, Felipe Oliveira

A number of recent works have employed decision trees for the construction of explainable partitions that aim to minimize the $k$-means cost function.

Clustering

On the price of explainability for some clustering problems

no code implementations5 Jan 2021 Eduardo Laber, Lucas Murtinho

For the $k$-means and $k$-medians problems our upper bounds improve those obtained by [Moshkovitz et.

Clustering

Speeding up Word Mover's Distance and its variants via properties of distances between embeddings

1 code implementation1 Dec 2019 Matheus Werner, Eduardo Laber

This distance proved to be quite effective, obtaining state-of-art error rates for classification tasks, but is also impracticable for large collections/documents due to its computational complexity.

Document Classification General Classification

Binary Partitions with Approximate Minimum Impurity

no code implementations ICML 2018 Eduardo Laber, Marco Molinaro, Felipe Mello Pereira

In practice, decision-tree inducers use heuristics for finding splits with small impurity when they consider nominal attributes with a large number of distinct values.

Decision Trees for Function Evaluation - Simultaneous Optimization of Worst and Expected Cost

no code implementations11 Sep 2013 Ferdinando Cicalese, Eduardo Laber, Aline Medeiros Saettler

In several applications of automatic diagnosis and active learning a central problem is the evaluation of a discrete function by adaptively querying the values of its variables until the values read uniquely determine the value of the function.

Active Learning

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