Joint active feature acquisition and classification with variable-size set encoding
We consider the problem of active feature acquisition where the goal is to sequentially select
the subset of features in order to achieve the maximum prediction performance in the most …
the subset of features in order to achieve the maximum prediction performance in the most …
Adaptive classification for prediction under a budget
F Nan, V Saligrama - Advances in neural information …, 2017 - proceedings.neurips.cc
We propose a novel adaptive approximation approach for test-time resource-constrained
prediction motivated by Mobile, IoT, health, security and other applications, where …
prediction motivated by Mobile, IoT, health, security and other applications, where …
Pruning random forests for prediction on a budget
F Nan, J Wang, V Saligrama - Advances in neural …, 2016 - proceedings.neurips.cc
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 …
construct a RF and then prune it to optimize expected feature cost & accuracy. We pose …
Active feature acquisition with generative surrogate models
Many real-world situations allow for the acquisition of additional relevant information when
making an assessment with limited or uncertain data. However, traditional ML approaches …
making an assessment with limited or uncertain data. However, traditional ML approaches …
Efficient learning by directed acyclic graph for resource constrained prediction
J Wang, K Trapeznikov… - Advances in neural …, 2015 - proceedings.neurips.cc
We study the problem of reducing test-time acquisition costs in classification systems. Our
goal is to learn decision rules that adaptively select sensors for each example as necessary …
goal is to learn decision rules that adaptively select sensors for each example as necessary …
Anytime recognition with routing convolutional networks
Achieving an automatic trade-off between accuracy and efficiency for a single deep neural
network is highly desired in time-sensitive computer vision applications. To achieve anytime …
network is highly desired in time-sensitive computer vision applications. To achieve anytime …
Towards robust active feature acquisition
Truly intelligent systems are expected to make critical decisions with incomplete and
uncertain data. Active feature acquisition (AFA), where features are sequentially acquired to …
uncertain data. Active feature acquisition (AFA), where features are sequentially acquired to …
A clustering based selection framework for cost aware and test-time feature elicitation
Most learning algorithms are optimized with generalization and predictive performance as
the goal. However, in most real-world machine learning applications, obtaining features at …
the goal. However, in most real-world machine learning applications, obtaining features at …
Dynamic feature selection for joint probabilistic recognition
N Mehrseresht - US Patent 10,380,173, 2019 - Google Patents
A method of jointly classifying a plurality of objects in an image using a feature type selected
from a plurality of feature types determines classification information for each of the plurality …
from a plurality of feature types determines classification information for each of the plurality …
Dynamic Feature Acquisition with Arbitrary Conditional Flows
Many real-world situations allow for the acquisition of additional relevant information when
making an assessment with limited or uncertain data. However, traditional ML approaches …
making an assessment with limited or uncertain data. However, traditional ML approaches …