Deep neural networks can make overconfident errors and assign high confidence predictions to inputs far away from the training data. Well-calibrated predictive uncertainty estimates are important to know when to trust a model's predictions, especially for safe deployment of models in applications where the train and test distributions can be different. I'll first present some concrete examples that motivate the need for uncertainty and out-of-distribution (OOD) robustness in deep learning. Next, I'll present an overview of our recent work focused on building neural networks that know what they don’t know: this includes methods which improve single model uncertainty (e.g. spectral-normalized neural Gaussian processes), methods which average over multiple neural network predictions such as Bayesian neural nets and deep ensembles, and methods that leverage better representations (e.g. improving “near-OOD” detection).
Balaji Lakshminarayanan is a staff research scientist at Google Brain. His recent research is focused on probabilistic deep learning, specifically, uncertainty estimation, out-of-distribution robustness and applications. Before joining Google Brain, he was a research scientist at DeepMind. He received his PhD from the Gatsby Unit, University College London and Master’s degree from Oregon State University. He has co-organized several workshops on "Uncertainty and Robustness in deep learning" and served as Area Chair for NeurIPS, ICML, ICLR and AISTATS.