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Jeremy Nixon
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2020 – today
- 2021
- [i7]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:
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR abs/2106.04015 (2021) - [i6]Jonathan Stray, Ivan Vendrov, Jeremy Nixon, Steven Adler, Dylan Hadfield-Menell:
What are you optimizing for? Aligning Recommender Systems with Human Values. CoRR abs/2107.10939 (2021) - 2020
- [c3]Michael W. Dusenberry, Dustin Tran, Edward Choi
, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine A. Heller, Andrew M. Dai:
Analyzing the role of model uncertainty for electronic health records. CHIL 2020: 204-213 - [i5]Jeremy Nixon, Jeremiah Z. Liu, David Berthelot:
Semi-Supervised Class Discovery. CoRR abs/2002.03480 (2020) - [i4]Sergei Volodin, Nevan Wichers, Jeremy Nixon:
Resolving Spurious Correlations in Causal Models of Environments via Interventions. CoRR abs/2002.05217 (2020)
2010 – 2019
- 2019
- [c2]Jeremy Nixon, Michael W. Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran:
Measuring Calibration in Deep Learning. CVPR Workshops 2019: 38-41 - [c1]Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein:
Understanding and correcting pathologies in the training of learned optimizers. ICML 2019: 4556-4565 - [i3]Jeremy Nixon, Mike Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran:
Measuring Calibration in Deep Learning. CoRR abs/1904.01685 (2019) - [i2]Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine A. Heller, Andrew M. Dai:
Analyzing the Role of Model Uncertainty for Electronic Health Records. CoRR abs/1906.03842 (2019) - 2018
- [i1]Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein:
Learned optimizers that outperform SGD on wall-clock and test loss. CoRR abs/1810.10180 (2018)
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