Statistical Learning
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Towards a Unified Framework of Contrastive Learning for Disentangled Representations
Neural Information Processing Systems
Contrastive learning has recently emerged as a promising approach for learning data representations that discover and disentangle the explanatory factors of the data.
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Dynamic Conditional Optimal Transport through Simulation-Free Flows
Neural Information Processing Systems
One way to achieve this is through triangular mappings [Baptista et al., 2020, Spantini et al., 2022],
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Selective Sampling and Imitation Learning via Online Regression
Neural Information Processing Systems
We consider the problem of Imitation Learning (IL) by actively querying noisy expert for feedback.
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- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.92)
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- Europe > France (0.04)
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- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.45)
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