Sculley, D.

Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J. V., Lakshminarayanan, B., & Snoek, J. (2019) · arXiv

Adversarial Robustness in Machine LearningGaussian Processes and Bayesian Inference

Cited by 989

Underspecification Presents Challenges for Credibility in Modern Machine Learning

D'Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al. (2020) · arXiv

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

Cited by 398

Using deep learning to annotate the protein universe

Bileschi, M. L., Belanger, D., Bryant, D. H., Sanderson, T., Carter, B., Sculley, D., Bateman, A., DePristo, M. A., & Colwell, L. J. (2022) · Nature Biotechnology

Machine Learning in BioinformaticsGenomics and Phylogenetic Studies

Cited by 332

The ML test score: A rubric for ML production readiness and technical debt reduction

Breck, E., Cai, S., Nielsen, E., Salib, M., & Sculley, D. (2017) · 2017 IEEE International Conference on Big Data (Big Data)

Software Testing and Debugging TechniquesSoftware System Performance and Reliability

Cited by 310