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Emily Fertig
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Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift
Y Ovadia, E Fertig, J Ren, Z Nado, D Sculley, S Nowozin, J Dillon, ...
Advances in neural information processing systems 32, 2019
15992019
Likelihood ratios for out-of-distribution detection
J Ren, PJ Liu, E Fertig, J Snoek, R Poplin, M Depristo, J Dillon, ...
Advances in neural information processing systems 32, 2019
6752019
The variability of interconnected wind plants
W Katzenstein, E Fertig, J Apt
Energy policy 38 (8), 4400-4410, 2010
2682010
Economics of compressed air energy storage to integrate wind power: A case study in ERCOT
E Fertig, J Apt
Energy Policy 39 (5), 2330-2342, 2011
2142011
The effect of long-distance interconnection on wind power variability
E Fertig, J Apt, P Jaramillo, W Katzenstein
Environmental research letters 7 (3), 034017, 2012
882012
Can you trust your model’s uncertainty
Y Ovadia, E Fertig, J Ren, Z Nado, D Sculley, S Nowozin, JV Dillon, ...
Evaluating predictive uncertainty under dataset shift, 2019
622019
Automatic structured variational inference
L Ambrogioni, K Lin, E Fertig, S Vikram, M Hinne, D Moore, M van Gerven
International Conference on Artificial Intelligence and Statistics, 676-684, 2021
282021
Optimal investment timing and capacity choice for pumped hydropower storage
E Fertig, AM Heggedal, G Doorman, J Apt
Energy Systems 5, 285-306, 2014
272014
Rare breakthroughs vs. incremental development in R&D strategy for an early-stage energy technology
E Fertig
Energy policy 123, 711-721, 2018
112018
Simulating sub-hourly variability of wind power output
E Fertig
Wind Energy (in press), 2019
102019
Smart integration of variable and intermittent renewables
J Apt, E Fertig, W Katzenstein
2012 45th Hawaii International Conference on System Sciences, 1997-2001, 2012
82012
Dueling decoders: Regularizing variational autoencoder latent spaces
B Seybold, E Fertig, A Alemi, I Fischer
arXiv preprint arXiv:1905.07478, 2019
62019
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling
G Silvestri, E Fertig, D Moore, L Ambrogioni
arXiv preprint arXiv:2110.06021, 2021
52021
-VAEs can retain label information even at high compression
E Fertig, A Arbabi, AA Alemi
arXiv preprint arXiv:1812.02682, 2018
52018
Facilitating the development and integration of low-carbon energy technologies
E Fertig
Carnegie Mellon University Pittsburgh, PA, 2013
22013
PROTOLITH AND TECTONIC SETTING OF QUARTZOFELDSPATHIC GNEISSES OF THE HIGHLAND MOUNTAINS, GREENHORN RANGE AND ALDER GULCH; SOUTHWEST MONTANA
E Fertig, C Siddoway, TA Harms
22006
Optimal Investment Strategy in Low-Carbon Energy R&D with Uncertain Payoff
E Fertig, J Apt
Transition to a Sustainable Energy Era: Opportunities & Challenges,,, 2012
12012
The Role of Energy Storage in Renewable Power Integration
E Fertig, S Wagner
Integration and Policy Workshop for RenewElec Project. Carnegie Mellon …, 2010
12010
Likelihood Ratios for Out-of-Distribution Detection
J Ren, B Lakshminarayanan, PJ Liu, JV Dillon, RJ Snoek, R Poplin, ...
US Patent App. 17/616,494, 2022
2022
Dynamic programming vs. robust optimization for managing a system with an uncertain threshold response
E Fertig, M Webster
2015
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