[NeurIPS Meetup] Practical Uncertainty Estimation in Deep Learning

On day 1/5 of our NeurIPS Meetup week, we are pleased to present the NeurIPS Tutorial “Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning” by Dustin Tran (Google Brain), Balaji Lakshminarayanan (Google Deepmind) and Jasper Snoek (Google Brain).

Our agenda starts at 7:00pm:
Introduction by freiburg.ai and heidelberg.ai hosts
Talk (with interactive chat) about “Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning”
Discussion and Q&A session with domain expert

Bio Dustin Tran (Google Brain):
Dustin Tran is a research scientist at Google Brain. His research contributions examine the intersection of probability and deep learning, particularly in the areas of probabilistic programming, variational inference, giant models, and Bayesian neural networks. He completed his Ph.D. at Columbia under David Blei. He’s received awards such as the John M. Chambers Statistical Software award and the Google Ph.D. Fellowship in Machine Learning. He served as Area Chair at NeurIPS, ICML, ICLR, IJCAI, and AISTATS and organized "Approximate Inference" and "Uncertainty & Robustness" workshops at NeurIPS and UAI.

Bio Balaji Lakshminarayanan (Google Deepmind):
Balaji Lakshminarayanan is a research scientist at Google Brain. Prior to that, he was a research scientist at DeepMind. He received his PhD from the Gatsby Unit, University College London where he worked with Yee Whye Teh. His recent research has focused on probabilistic deep learning, specifically, uncertainty estimation, out-of-distribution robustness and deep generative models. Notable contributions relevant to the tutorial include developing state-of-the-art methods for calibration under dataset shift (such as deep ensembles and AugMix) and showing that deep generative models do not always know what they don't know. He has co-organized several workshops on "Uncertainty and Robustness in deep learning" and served as Area Chair for NeurIPS, ICML, ICLR and AISTATS.

Bio Jasper Snoek (Google Brain):
Jasper Snoek is a research scientist at Google Brain. His research has touched a variety of topics at the intersection of Bayesian methods and deep learning. He completed his PhD in machine learning at the University of Toronto. He subsequently held postdoctoral fellowships at the University of Toronto, under Geoffrey Hinton and Ruslan Salakhutdinov, and at the Harvard Center for Research on Computation and Society, under Ryan Adams. Jasper co-founded a Bayesian optimization focused startup, Whetlab, which was acquired by Twitter. He has served as an Area Chair for NeurIPS, ICML, AISTATS and ICLR, and organized a variety of workshops at ICML and NeurIPS.

Zoom-link to event: https://zoom.us/j/94929450871