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

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

* Originally presented at the ICML 2020 Participatory Approaches to Machine Learning workshop 

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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 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

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Semi-Supervised Class Discovery

Jeremy Nixon , Jeremiah Liu , David Berthelot

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Resolving Spurious Correlations in Causal Models of Environments via Interventions

Sergei Volodin , Nevan Wichers , Jeremy Nixon

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Analyzing the Role of Model Uncertainty for Electronic Health Records

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

* Presented at the ICML 2019 Workshop on Uncertainty & Robustness in Deep Learning. Code to be open-sourced 

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Measuring Calibration in Deep Learning

Jeremy Nixon , Mike Dusenberry , Linchuan Zhang , Ghassen Jerfel , Dustin Tran

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

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

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