5.7k citations · 8.4k across the 42 of their papers we have counts for
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stat.ML2019★ 8 cited
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…
stat.ML2019★ 924 cited
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…
stat.ML2019★ 2 cited
DPPNet: Approximating Determinantal Point Processes with Deep Networks
Zelda Mariet, Yaniv Ovadia, Jasper Snoek
Determinantal Point Processes (DPPs) provide an elegant and versatile way to sample sets of items that balance the point-wise quality with the set-wise diversity of selected items.…