924 citations · 938 across the 6 of their papers we have counts for
6 papers
The Vizier Gaussian Process Bandit Algorithm
Xingyou Song, Qiuyi Zhang, Chansoo Lee +8
Google Vizier has performed millions of optimizations and accelerated numerous research and production systems at Google, demonstrating the success of Bayesian optimization as a la…
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling
Gianluigi Silvestri, Emily Fertig, Dave Moore +1
Normalizing flows have shown great success as general-purpose density estimators. However, many real world applications require the use of domain-specific knowledge, which normaliz…
Likelihood Ratios for Out-of-Distribution Detection
Jie Ren, Peter J. Liu, Emily Fertig +5
Discriminative neural networks offer little or no performance guarantees when deployed on data not generated by the same process as the training distribution. On such out-of-distri…
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Yaniv Ovadia, Emily Fertig, Jie Ren +6
Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful…
Dueling Decoders: Regularizing Variational Autoencoder Latent Spaces
Bryan Seybold, Emily Fertig, Alex Alemi +1
Variational autoencoders learn unsupervised data representations, but these models frequently converge to minima that fail to preserve meaningful semantic information. For example,…
-VAEs can retain label information even at high compression
Emily Fertig, Aryan Arbabi, Alexander A. Alemi
In this paper, we investigate the degree to which the encoding of a -VAE captures label information across multiple architectures on Binary Static MNIST and Omniglot. Even thoug…