SystemML: Declarative Machine Learning on Spark. - Matthias Boehm 001; Michael Dusenberry; Deron Eriksson; Alexandre V. Evfimievski; Faraz Makari Manshadi; Niketan Pansare; Berthold Reinwald; Frederick Reiss 001; Prithviraj Sen; Arvind Surve; Shirish Tatikonda (Proc. VLDB Endow., 2016)

Deep Learning with Apache SystemML. - Niketan Pansare; Michael Dusenberry; Nakul Jindal; Matthias Boehm 001; Berthold Reinwald; Prithviraj Sen (CoRR, 2018)

Measuring Calibration in Deep Learning. - Jeremy Nixon; Michael W. Dusenberry; Linchuan Zhang; Ghassen Jerfel; Dustin Tran (CVPR Workshops, 2019)

Bayesian Layers: A Module for Neural Network Uncertainty. - Dustin Tran; Mike Dusenberry; Mark van der Wilk; Danijar Hafner (NeurIPS, 2019)

Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records. - Edward Choi; Zhen Xu 006; Yujia Li; Michael W. Dusenberry; Gerardo Flores; Yuan Xue; Andrew M. Dai (CoRR, 2019)

Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer. - Edward Choi; Zhen Xu 006; Yujia Li; Michael Dusenberry; Gerardo Flores; Emily Xue; Andrew M. Dai (AAAI, 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 (CHIL, 2020)

Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. - Michael Dusenberry; Ghassen Jerfel; Yeming Wen; Yi-An Ma; Jasper Snoek; Katherine A. Heller; Balaji Lakshminarayanan; Dustin Tran (ICML, 2020)

Combining Ensembles and Data Augmentation Can Harm Your Calibration. - Yeming Wen; Ghassen Jerfel; Rafael Muller; Michael W. Dusenberry; Jasper Snoek; Balaji Lakshminarayanan; Dustin Tran (ICLR, 2021)

Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. - Neil Band; Tim G. J. Rudner; Qixuan Feng; Angelos Filos; Zachary Nado; Mike Dusenberry; Ghassen Jerfel; Dustin Tran; Yarin Gal (NeurIPS Datasets and Benchmarks, 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; et al. (CoRR, 2021)

A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan. - Sercan Ö. Arik; Joel Shor; Rajarishi Sinha; Jinsung Yoon; Joseph R. Ledsam; Long T. Le; Michael W. Dusenberry; Nathanael C. Yoder; Kris Popendorf; Arkady Epshteyn; Johan Euphrosine; Elli Kanal; et al. (npj Digit. Medicine, 2021)

Plex: Towards Reliability using Pretrained Large Model Extensions. - Dustin Tran; Jeremiah Z. Liu; Michael W. Dusenberry; Du Phan; Mark Collier; Jie Ren 006; Kehang Han; Zi Wang; Zelda Mariet; Huiyi Hu; Neil Band; Tim G. J. Rudner; et al. (CoRR, 2022)

Neural Spline Search for Quantile Probabilistic Modeling. - Ruoxi Sun 002; Chun-Liang Li; Sercan Ö. Arik; Michael W. Dusenberry; Chen-Yu Lee; Tomas Pfister (AAAI, 2023)

A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. - James Urquhart Allingham; Jie Ren 006; Michael W. Dusenberry; Xiuye Gu; Yin Cui; Dustin Tran; Jeremiah Zhe Liu; Balaji Lakshminarayanan (ICML, 2023)

Morse Neural Networks for Uncertainty Quantification. - Benoit Dherin; Huiyi Hu; Jie Ren 006; Michael W. Dusenberry; Balaji Lakshminarayanan (CoRR, 2023)