#NeurIPS2021 invited talks round-up: part two – benign overfitting, optimal transport, and human and machine intelligence

The 35th conference on Neural Information Processing Systems (NeurIPS2021) featured eight invited talks. Continuing our series of round-ups, we give a flavour of the next three presentations. In his talk, Peter focussed on the phenomenon of benign overfitting, one of the surprises to arise from deep learning: that deep neural networks seem to predict well, even with a perfect fit to noisy training data. The presentation began with a broader perspective on theoretical progress inspired by large-scale machine learning problems. Peter took us back to 1988, and to a NeurIPS paper by Eric Baum and David Haussler who were interested in the question of generalization for neural networks.