28 citations · 67 across the 9 of their papers we have counts for
5 papers · 1 filter
Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget
Henghui Zhu, Feng Nan, Ioannis Paschalidis +1
Deep neural network (DNN) based approaches hold significant potential for reinforcement learning (RL) and have already shown remarkable gains over state-of-art methods in a number…
Adaptive Classification for Prediction Under a Budget
Feng Nan, Venkatesh Saligrama
We propose a novel adaptive approximation approach for test-time resource-constrained prediction. Given an input instance at test-time, a gating function identifies a prediction mo…
Comments on the proof of adaptive submodular function minimization
Feng Nan, Venkatesh Saligrama
We point out an issue with Theorem 5 appearing in "Group-based active query selection for rapid diagnosis in time-critical situations". Theorem 5 bounds the expected number of quer…
Pruning Random Forests for Prediction on a Budget
Feng Nan, Joseph Wang, Venkatesh Saligrama
We propose to prune a random forest (RF) for resource-constrained prediction. We first construct a RF and then prune it to optimize expected feature cost & accuracy. We pose prunin…
Feature-Budgeted Random Forest
Feng Nan, Joseph Wang, Venkatesh Saligrama
We seek decision rules for prediction-time cost reduction, where complete data is available for training, but during prediction-time, each feature can only be acquired for an addit…