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Active Learning
Can Active Learning Preemptively Mitigate Fairness Issues?
Dataset bias is one of the prevailing causes of unfairness in machine learning. Addressing fairness at the data collection and dataset …
Frédéric Branchaud-Charron
,
Parmida Atighhehchian
,
Pau Rodriguez
,
Grace Abuhamad
,
Alexandre Lacoste
Workshop at the International Conference on Learning Representations (ICLR), 2022.
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Synbols: Probing Learning Algorithms with Synthetic Datasets
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing …
Alexandre Lacoste
,
Pau Rodriguez
,
Frederic Branchaud
,
Parmida Atighhehchian
,
Massimo Caccia
,
Issam H. Laradji
,
Alexandre Drouin
,
Matt Craddock
,
Laurent Charlin
,
David Vazquez
Conference on Neural Information Processing Systems (NeurIPS), 2020.
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Bayesian active learning for production, a systematic study and a reusable library
Active learning is able to reduce the amount of labelling effort by using a machine learning model to query the user for specific …
Parmida Atighhehchian
,
Frederic Branchaud
,
Alexandre Lacoste
Workshop at the International Conference on Machine Learning (ICML), 2020.
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Reinforced Active Learning for Image Segmentation
Learning-based approaches for semantic segmentation have two inherent challenges. First, acquiring pixel-wise labels is expensive and …
Arantxa Casanova
,
Pedro O. Pinheiro
,
Negar Rostamzadeh
,
Christopher Pal
International Conference on Learning Representations (ICLR), 2020.
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Active Domain Randomization
Domain randomization is a popular technique for improving domain transfer, often used in a zero-shot setting when the target domain is …
Bhairav Mehta
,
Manfred Diaz
,
Florian Golemo
,
Christopher Pal
,
Liam Paull
Conference on Robot Learning (CoRL), 2019.
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Synbols: Probing Learning Algorithms with Synthetic Datasets
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing …
Alexandre Lacoste
,
Pau Rodriguez
,
Frédéric Branchaud-Charron
,
Parmida Atighhehchian
,
Massimo Caccia
,
Issam H. Laradji
,
Alexandre Drouin
,
Matt Craddock
,
Laurent Charlin
,
David Vazquez
Montreal AI Symposium (MAIS), 2018.
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