71 citations · 124 across the 7 of their papers we have counts for
5 papers · 1 filter
Measuring and Improving Model-Moderator Collaboration using Uncertainty Estimation
Ian D. Kivlichan, Zi Lin, Jeremiah Liu +1
Content moderation is often performed by a collaboration between humans and machine learning models. However, it is not well understood how to design the collaborative process so a…
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
Jie Ren, Stanislav Fort, Jeremiah Liu +3
Mahalanobis distance (MD) is a simple and popular post-processing method for detecting out-of-distribution (OOD) inputs in neural networks. We analyze its failure modes for near-OO…
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks
Shreyas Padhy, Zachary Nado, Jie Ren +3
Accurate estimation of predictive uncertainty in modern neural networks is critical to achieve well calibrated predictions and detect out-of-distribution (OOD) inputs. The most pro…
Semi-Supervised Class Discovery
Jeremy Nixon, Jeremiah Liu, David Berthelot
One promising approach to dealing with datapoints that are outside of the initial training distribution (OOD) is to create new classes that capture similarities in the datapoints p…
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Jeremiah Zhe Liu, John Paisley, Marianthi-Anna Kioumourtzoglou +1
Ensemble learning is a standard approach to building machine learning systems that capture complex phenomena in real-world data. An important aspect of these systems is the complet…