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Underspecification Presents Challenges for Credibility in Modern Machine Learning

A. D'Amour, K. Heller, D. Moldovan, B. Adlam, B. Alipanahi, A. Beutel, C. Chen, J. Deaton, J. Eisenstein, M. Hoffman, F. Hormozdiari, N. Houlsby, S. Hou, G. Jerfel, A. Karthikesalingam, M. Lucic, Y. Ma, C. McLean, D. Mincu, A. Mitani, A. Montanari, Z. Nado, V. Natarajan, C. Nielson, T. Osborne, R. Raman, K. Ramasamy, R. Sayres, J. Schrouff, M. Seneviratne, S. Sequeira, H. Suresh, V. Veitch, M. Vladymyrov, X. Wang, K. Webster, S. Yadlowsky, T. Yun, X. Zhai, and D. Sculley. CoRR, (2020)

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Nado Gladi

Zachary Dotscheff

Zachary Evenson

Zachary Hayes

Zachary Taylor

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Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks.S. Padhy, Z. Nado, J. Ren, J. Liu, J. Snoek, and B. Lakshminarayanan. CoRR, (2020)Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning.Z. Nado, N. Band, M. Collier, J. Djolonga, M. Dusenberry, S. Farquhar, A. Filos, M. Havasi, R. Jenatton, G. Jerfel and 14 other author(s). CoRR, (2021)Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach.S. Ariafar, J. Gilmer, Z. Nado, J. Snoek, R. Jenatton, and G. Dahl. CoRR, (2021)A Loss Curvature Perspective on Training Instability in Deep Learning.J. Gilmer, B. Ghorbani, A. Garg, S. Kudugunta, B. Neyshabur, D. Cardoze, G. Dahl, Z. Nado, and O. Firat. CoRR, (2021)Pre-training helps Bayesian optimization too.Z. Wang, G. Dahl, K. Swersky, C. Lee, Z. Mariet, Z. Nado, J. Gilmer, J. Snoek, and Z. Ghahramani. CoRR, (2022)Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks.N. Band, T. Rudner, Q. Feng, A. Filos, Z. Nado, M. Dusenberry, G. Jerfel, D. Tran, and Y. Gal. NeurIPS Datasets and Benchmarks, (2021)Adaptive Gradient Methods at the Edge of Stability.J. Cohen, B. Ghorbani, S. Krishnan, N. Agarwal, S. Medapati, M. Badura, D. Suo, D. Cardoze, Z. Nado, G. Dahl and 1 other author(s). CoRR, (2022)Benchmarking Neural Network Training Algorithms.G. Dahl, F. Schneider, Z. Nado, N. Agarwal, C. Sastry, P. Hennig, S. Medapati, R. Eschenhagen, P. Kasimbeg, D. Suo and 15 other author(s). CoRR, (2023)Underspecification Presents Challenges for Credibility in Modern Machine Learning.A. D'Amour, K. Heller, D. Moldovan, B. Adlam, B. Alipanahi, A. Beutel, C. Chen, J. Deaton, J. Eisenstein, M. Hoffman and 30 other author(s). J. Mach. Learn. Res., (2022)Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift.J. Snoek, Y. Ovadia, E. Fertig, B. Lakshminarayanan, S. Nowozin, D. Sculley, J. Dillon, J. Ren, and Z. Nado. NeurIPS, page 13969-13980. (2019)

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Disambiguation of "Nado, Zachary"

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Underspecification Presents Challenges for Credibility in Modern Machine Learning

Please choose a person to relate this publication to

Nado Gladi

Zachary Dotscheff

Zachary Evenson

Zachary Hayes

Zachary Taylor

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Nado, Zachary"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Underspecification Presents Challenges for Credibility in Modern Machine Learning

Please choose a person to relate this publication to

Nado Gladi

Zachary Dotscheff

Zachary Evenson

Zachary Hayes

Zachary Taylor

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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URL
DOI
BibTeX
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APA
Chicago
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Underspecification Presents Challenges for Credibility in Modern Machine Learning