no code implementations • 22 Jul 2021 • Jonathan Stray, Ivan Vendrov, Jeremy Nixon, Steven Adler, Dylan Hadfield-Menell
We describe cases where real recommender systems were modified in the service of various human values such as diversity, fairness, well-being, time well spent, and factual accuracy.
3 code implementations • 7 Jun 2021 • Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Qixuan Feng, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Faris Sbahi, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran
In this paper we introduce Uncertainty Baselines: high-quality implementations of standard and state-of-the-art deep learning methods on a variety of tasks.
no code implementations • NeurIPS Workshop ICBINB 2020 • Jeremy Nixon, Balaji Lakshminarayanan, Dustin Tran
Ensemble methods have consistently reached state of the art across predictive, uncertainty, and out-of-distribution robustness benchmarks.
no code implementations • 12 Feb 2020 • Sergei Volodin, Nevan Wichers, Jeremy Nixon
We consider the problem of inferring a causal model of a reinforcement learning environment and we propose a method to deal with spurious correlations.
no code implementations • 10 Feb 2020 • Jeremy Nixon, Jeremiah Liu, David Berthelot
One promising approach to dealing with datapoints that are outside of the initial training distribution (OOD) is to create new classes that capture similarities in the datapoints previously rejected as uncategorizable.
1 code implementation • 10 Jun 2019 • Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine Heller, Andrew M. Dai
We further show that RNNs with only Bayesian embeddings can be a more efficient way to capture model uncertainty compared to ensembles, and we analyze how model uncertainty is impacted across individual input features and patient subgroups.
no code implementations • ICLR 2019 • Luke Metz, Niru Maheswaranathan, Jeremy Nixon, Daniel Freeman, Jascha Sohl-Dickstein
We demonstrate these results on problems where our learned optimizer trains convolutional networks in a fifth of the wall-clock time compared to tuned first-order methods, and with an improvement
2 code implementations • 2 Apr 2019 • Jeremy Nixon, Mike Dusenberry, Ghassen Jerfel, Timothy Nguyen, Jeremiah Liu, Linchuan Zhang, Dustin Tran
In this paper, we perform a comprehensive empirical study of choices in calibration measures including measuring all probabilities rather than just the maximum prediction, thresholding probability values, class conditionality, number of bins, bins that are adaptive to the datapoint density, and the norm used to compare accuracies to confidences.
1 code implementation • 24 Oct 2018 • Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein
Deep learning has shown that learned functions can dramatically outperform hand-designed functions on perceptual tasks.