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2020 – today
- 2023
[j3]Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan:
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. J. Mach. Learn. Res. 24: 42:1-42:63 (2023)- 2022
- [j2]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, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley:
Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 23: 226:1-226:61 (2022) - [j1]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, Rodolphe Jenatton:
Sparse MoEs meet Efficient Ensembles. Trans. Mach. Learn. Res. 2022 (2022) - [i13]Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zack Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan:
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. CoRR abs/2205.00403 (2022) - [i12]Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W. Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal:
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. CoRR abs/2211.12717 (2022) - 2021
- [b1]Ghassen Jerfel:
Multimodal Probabilistic Inference for Robust Uncertainty Quantification. Duke University, Durham, NC, USA, 2021 - [c8]Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran:
Combining Ensembles and Data Augmentation Can Harm Your Calibration. ICLR 2021 - [c7]Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Mike Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal:
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. NeurIPS Datasets and Benchmarks 2021 - [c6]Ghassen Jerfel, Serena Lutong Wang, Clara Wong-Fannjiang, Katherine A. Heller, Yian Ma, Michael I. Jordan:
Variational refinement for importance sampling using the forward Kullback-Leibler divergence. UAI 2021: 1819-1829 - [i11]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, 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:
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR abs/2106.04015 (2021) - [i10]Ghassen Jerfel, Serena Lutong Wang, Clara Fannjiang, Katherine A. Heller, Yi-An Ma, Michael I. Jordan:
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence. CoRR abs/2106.15980 (2021) - [i9]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, Rodolphe Jenatton:
Sparse MoEs meet Efficient Ensembles. CoRR abs/2110.03360 (2021) - 2020
- [c5]Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine A. Heller, Andrew M. Dai:
Analyzing the role of model uncertainty for electronic health records. CHIL 2020: 204-213 - [c4]Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran:
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. ICML 2020: 2782-2792 - [i8]Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran:
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. CoRR abs/2005.07186 (2020) - [i7]Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran:
Combining Ensembles and Data Augmentation can Harm your Calibration. CoRR abs/2010.09875 (2020) - [i6]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, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley:
Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR abs/2011.03395 (2020)
2010 – 2019
- 2019
- [c3]Jeremy Nixon, Michael W. Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran:
Measuring Calibration in Deep Learning. CVPR Workshops 2019: 38-41 - [c2]Ghassen Jerfel, Erin Grant, Tom Griffiths, Katherine A. Heller:
Reconciling meta-learning and continual learning with online mixtures of tasks. NeurIPS 2019: 9119-9130 - [i5]Jeremy Nixon, Mike Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran:
Measuring Calibration in Deep Learning. CoRR abs/1904.01685 (2019) - [i4]Charles Weill, Javier Gonzalvo, Vitaly Kuznetsov, Scott Yang, Scott Yak, Hanna Mazzawi, Eugen Hotaj, Ghassen Jerfel, Vladimir Macko, Ben Adlam, Mehryar Mohri, Corinna Cortes:
AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles. CoRR abs/1905.00080 (2019) - [i3]Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine A. Heller, Andrew M. Dai:
Analyzing the Role of Model Uncertainty for Electronic Health Records. CoRR abs/1906.03842 (2019) - 2018
- [i2]Ghassen Jerfel, Erin Grant, Thomas L. Griffiths, Katherine A. Heller:
Online gradient-based mixtures for transfer modulation in meta-learning. CoRR abs/1812.06080 (2018) - 2017
- [c1]Ghassen Jerfel, Mehmet Emin Basbug, Barbara E. Engelhardt:
Dynamic Collaborative Filtering With Compound Poisson Factorization. AISTATS 2017: 738-747 - 2016
- [i1]Ghassen Jerfel, Mehmet Emin Basbug, Barbara E. Engelhardt:
Dynamic Collaborative Filtering with Compound Poisson Factorization. CoRR abs/1608.04839 (2016)
Coauthor Index
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