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Adversarial Imitation Learning alternates between learning a discriminator – which tells apart expert’s demonstrations from …
In suitably initialized wide networks, small learning rates transform deep neural networks (DNNs) into neural tangent kernel (NTK) …
Learning a causal directed acyclic graph from data is a challenging task that involves solving a combinatorial problem for which the …
One of the principal scientific challenges in deep learning is explaining generalization, i.e., why the particular way the community …
We are interested in understanding how well Transformer language models (TLMs) can perform reasoning tasks when trained on knowledge …
Continual learning studies agents that learn from streams of tasks without forgetting previous ones while adapting to new ones. Two …
In multi-agent reinforcement learning, discovering successful collective behaviors is challenging as it requires exploring a joint …
The information-theoretic framework of Russo and J. Zou (2016) and Xu and Raginsky (2017) provides bounds on the generalization error …
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing …
Contrastive self-supervised learning has emerged as a promising approach to unsupervised visual representation learning. In general, …