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Combining Ensembles and Data Augmentation can Harm your Calibration

Oct 19, 2020
Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran


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Training independent subnetworks for robust prediction

Oct 13, 2020
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran


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Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

Jul 17, 2020
Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek


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Bayesian Deep Ensembles via the Neural Tangent Kernel

Jul 11, 2020
Bobby He, Balaji Lakshminarayanan, Yee Whye Teh


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Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks

Jul 10, 2020
Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Liu, Jasper Snoek, Balaji Lakshminarayanan


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Density of States Estimation for Out-of-Distribution Detection

Jun 22, 2020
Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon

* Submitted to NeurIPS. Corrected footnote from: "34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada" to "Preprint. Under review." 

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Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness

Jun 17, 2020
Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan


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Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors

May 14, 2020
Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-an Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran

* Code available at https://github.com/google/edward2 

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AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Dec 05, 2019
Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan

* Code available at https://github.com/google-research/augmix 

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Normalizing Flows for Probabilistic Modeling and Inference

Dec 05, 2019
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan

* Review article. 60 pages, 4 figures 

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Deep Ensembles: A Loss Landscape Perspective

Dec 05, 2019
Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan


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Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality

Jun 07, 2019
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Balaji Lakshminarayanan


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Likelihood Ratios for Out-of-Distribution Detection

Jun 07, 2019
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan


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

Jun 06, 2019
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek


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Hybrid Models with Deep and Invertible Features

Feb 07, 2019
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan


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Adapting Auxiliary Losses Using Gradient Similarity

Dec 05, 2018
Yunshu Du, Wojciech M. Czarnecki, Siddhant M. Jayakumar, Razvan Pascanu, Balaji Lakshminarayanan


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Do Deep Generative Models Know What They Don't Know?

Oct 22, 2018
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan


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Learning from Delayed Outcomes with Intermediate Observations

Jul 24, 2018
Timothy A. Mann, Sven Gowal, Ray Jiang, Huiyi Hu, Balaji Lakshminarayanan, Andras Gyorgy


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Distribution Matching in Variational Inference

Jun 12, 2018
Mihaela Rosca, Balaji Lakshminarayanan, Shakir Mohamed


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Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step

Feb 20, 2018
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian Goodfellow

* 18 pages 

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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Nov 04, 2017
Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell

* NIPS 2017 

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Variational Approaches for Auto-Encoding Generative Adversarial Networks

Oct 21, 2017
Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley, Shakir Mohamed


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Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server

Sep 07, 2017
Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh

* Journal of Machine Learning Research 18 (2017) 1-37 
* 37 pages, 7 figures 

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The Cramer Distance as a Solution to Biased Wasserstein Gradients

May 30, 2017
Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, Rémi Munos


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Comparison of Maximum Likelihood and GAN-based training of Real NVPs

May 15, 2017
Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, Peter Dayan


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Learning Deep Nearest Neighbor Representations Using Differentiable Boundary Trees

Feb 28, 2017
Daniel Zoran, Balaji Lakshminarayanan, Charles Blundell


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Learning in Implicit Generative Models

Feb 27, 2017
Shakir Mohamed, Balaji Lakshminarayanan


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The Mondrian Kernel

Jun 16, 2016
Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh

* Accepted for presentation at the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016) 

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