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Can Active Learning Preemptively Mitigate Fairness Issues?
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 …
Workshop at the International Conference on Learning Representations (ICLR),  2022.
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Synbols: Probing Learning Algorithms with Synthetic Datasets
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 …
Conference on Neural Information Processing Systems (NeurIPS),  2020.
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Bayesian active learning for production, a systematic study and a reusable library
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 …
Workshop at the International Conference on Machine Learning (ICML),  2020.
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Reinforced Active Learning for Image Segmentation
Reinforced Active Learning for Image Segmentation
Learning-based approaches for semantic segmentation have two inherent challenges. First, acquiring pixel-wise labels is expensive and …
International Conference on Learning Representations (ICLR),  2020.
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Active Domain Randomization
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 …
Conference on Robot Learning (CoRL),  2019.
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Synbols: Probing Learning Algorithms with Synthetic Datasets
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 …
Montreal AI Symposium (MAIS),  2018.
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