Jeremy Nixon

According to our database, Jeremy Nixon authored at least 9 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Structure Learning via Mutual Information.

Jeremy Nixon

CoRR, 2024

2021

What are you optimizing for? Aligning Recommender Systems with Human Values.

Jonathan Stray, Ivan Vendrov, Jeremy Nixon, Steven Adler, Dylan Hadfield-Menell

CoRR, 2021

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning.

Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Z. Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran

CoRR, 2021

2020

Resolving Spurious Correlations in Causal Models of Environments via Interventions.

Sergei Volodin, Nevan Wichers, Jeremy Nixon

CoRR, 2020

Semi-Supervised Class Discovery.

Jeremy Nixon, Jeremiah Z. Liu, David Berthelot

CoRR, 2020

Analyzing the role of model uncertainty for electronic health records.

Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine A. Heller, Andrew M. Dai

Proceedings of the ACM CHIL '20: ACM Conference on Health, 2020

2019

Understanding and correcting pathologies in the training of learned optimizers.

Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein

Proceedings of the 36th International Conference on Machine Learning, 2019

Measuring Calibration in Deep Learning.

Jeremy Nixon, Michael W. Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019

2018

Learned optimizers that outperform SGD on wall-clock and test loss.

Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein

CoRR, 2018