117 citations · 148 across the 12 of their papers we have counts for
24 papers
Is margin all you need? An extensive empirical study of active learning on tabular data
Dara Bahri, Heinrich Jiang, Tal Schuster +1
Given a labeled training set and a collection of unlabeled data, the goal of active learning (AL) is to identify the best unlabeled points to label. In this comprehensive study, we…
Predicting on the Edge: Identifying Where a Larger Model Does Better
Taman Narayan, Heinrich Jiang, Sen Zhao +1
Much effort has been devoted to making large and more accurate models, but relatively little has been put into understanding which examples are benefiting from the added complexity…
Active Covering
Heinrich Jiang, Afshin Rostamizadeh
We analyze the problem of active covering, where the learner is given an unlabeled dataset and can sequentially label query examples. The objective is to label query all of the pos…
MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking
Jennifer Jang, Heinrich Jiang
MeanShift is a popular mode-seeking clustering algorithm used in a wide range of applications in machine learning. However, it is known to be prohibitively slow, with quadratic run…
Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection
Dara Bahri, Heinrich Jiang, Yi Tay +1
Detecting out-of-distribution (OOD) examples is critical in many applications. We propose an unsupervised method to detect OOD samples using a -NN density estimate with respect…
Locally Adaptive Label Smoothing for Predictive Churn
Dara Bahri, Heinrich Jiang
Training modern neural networks is an inherently noisy process that can lead to high \emph{prediction churn} -- disagreements between re-trainings of the same model due to factors…