Dynamic Collaborative Filtering With Compound Poisson Factorization. - Ghassen Jerfel; Mehmet Emin Basbug; Barbara E. Engelhardt (AISTATS, 2017)

Online gradient-based mixtures for transfer modulation in meta-learning. - Ghassen Jerfel; Erin Grant; Thomas L. Griffiths 001; Katherine A. Heller (CoRR, 2018)

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

Reconciling meta-learning and continual learning with online mixtures of tasks. - Ghassen Jerfel; Erin Grant; Tom Griffiths 001; Katherine A. Heller (NeurIPS, 2019)

AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles. - Charles Weill; Javier Gonzalvo; Vitaly Kuznetsov; Scott Yang; Scott Yak; Hanna Mazzawi; Eugen Hotaj; Ghassen Jerfel; Vladimir Macko; Ben Adlam; Mehryar Mohri; Corinna Cortes (CoRR, 2019)

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)

Variational refinement for importance sampling using the forward Kullback-Leibler divergence. - Ghassen Jerfel; Serena Lutong Wang; Clara Wong-Fannjiang; Katherine A. Heller; Yian Ma; Michael I. Jordan (UAI, 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)

Multimodal Probabilistic Inference for Robust Uncertainty Quantification. - Ghassen Jerfel (2021)

Underspecification Presents Challenges for Credibility in Modern Machine Learning. - Alexander D'Amour; Katherine A. Heller; Dan Moldovan; Ben Adlam; Babak Alipanahi; Alex Beutel; Christina Chen; Jonathan Deaton; Jacob Eisenstein; Matthew D. Hoffman; Farhad Hormozdiari; Neil Houlsby; et al. (J. Mach. Learn. Res., 2022)

Sparse MoEs meet Efficient Ensembles. - James Urquhart Allingham; Florian Wenzel; Zelda E. Mariet; Basil Mustafa; Joan Puigcerver; Neil Houlsby; Ghassen Jerfel; Vincent Fortuin; Balaji Lakshminarayanan; Jasper Snoek; Dustin Tran; Carlos Riquelme Ruiz; et al. (Trans. Mach. Learn. Res., 2022)

A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. - Jeremiah Zhe Liu; Shreyas Padhy; Jie Ren 006; Zi Lin; Yeming Wen; Ghassen Jerfel; Zachary Nado; Jasper Snoek; Dustin Tran; Balaji Lakshminarayanan (J. Mach. Learn. Res., 2023)

Scaling Laws of Motion Forecasting and Planning - A Technical Report. - Mustafa Baniodeh; Kratarth Goel; Scott Ettinger; Carlos Fuertes; Ari Seff; Tim Shen; Cole Gulino; Chenjie Yang; Ghassen Jerfel; Dokook Choe; Rui Wang; Vinutha Kallem; et al. (CoRR, 2025)