382 citations · 978 across the 50 of their papers we have counts for
4 papers · 2 filters
Ignoring Distractors in the Absence of Labels: Optimal Linear Projection to Remove False Positives During Anomaly Detection
Allison Del Giorno, J. Andrew Bagnell, Martial Hebert
In the anomaly detection setting, the native feature embedding can be a crucial source of bias. We present a technique, Feature Omission using Context in Unsupervised Settings (FOC…
Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing
Hanzhang Hu, Debadeepta Dey, Martial Hebert +1
This work considers the trade-off between accuracy and test-time computational cost of deep neural networks (DNNs) via \emph{anytime} predictions from auxiliary predictions. Specif…
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction
Wen Sun, Arun Venkatraman, Geoffrey J. Gordon +2
Researchers have demonstrated state-of-the-art performance in sequential decision making problems (e.g., robotics control, sequential prediction) with deep neural network models. O…
Gradient Boosting on Stochastic Data Streams
Hanzhang Hu, Wen Sun, Arun Venkatraman +2
Boosting is a popular ensemble algorithm that generates more powerful learners by linearly combining base models from a simpler hypothesis class. In this work, we investigate the p…