Timezone: »
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 cost-effective way at test time. In this work, we formulate this active feature acquisition as a jointly learning problem of training both the classifier (environment) and the RL agent that decides either to stop and predict' or
collect a new feature' at test time, in a cost-sensitive manner. We also introduce a novel encoding scheme to represent acquired subsets of features by proposing an order-invariant set encoding at the feature level, which also significantly reduces the search space for our agent. We evaluate our model on a carefully designed synthetic dataset for the active feature acquisition as well as several medical datasets. Our framework shows meaningful feature acquisition process for diagnosis that complies with human knowledge, and outperforms all baselines in terms of prediction performance as well as feature acquisition cost.
Author Information
Hajin Shim (KAIST)
Sung Ju Hwang (KAIST, AItrics)
Eunho Yang (Korea Advanced Institute of Science and Technology; AItrics)
More from the Same Authors
-
2022 : Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation »
Hyunsu Rhee · Dongchan Min · Sunil Hwang · Bruno Andreis · Sung Ju Hwang -
2022 : Targeted Adversarial Self-Supervised Learning »
Minseon Kim · Hyeonjeong Ha · Sooel Son · Sung Ju Hwang -
2022 : Few-Shot Transferable Robust Representation Learning via Bilevel Attacks »
Minseon Kim · Hyeonjeong Ha · Sung Ju Hwang -
2023 Poster: GEX: A flexible method for approximating influence via Geometric Ensemble »
SungYub Kim · Kyungsu Kim · Eunho Yang -
2023 Poster: Riemannian SAM: Sharpness-Aware Minimization on Riemannian Manifolds »
Jihun Yun · Eunho Yang -
2021 Poster: Adaptive Proximal Gradient Methods for Structured Neural Networks »
Jihun Yun · Aurelie Lozano · Eunho Yang -
2021 Poster: Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning »
Youngkyu Hong · Eunho Yang -
2020 Poster: Bootstrapping neural processes »
Juho Lee · Yoonho Lee · Jungtaek Kim · Eunho Yang · Sung Ju Hwang · Yee Whye Teh -
2020 Poster: Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning »
Jaehyung Kim · Youngbum Hur · Sejun Park · Eunho Yang · Sung Ju Hwang · Jinwoo Shin -
2020 Poster: Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction »
Jinheon Baek · Dong Bok Lee · Sung Ju Hwang -
2020 Poster: Time-Reversal Symmetric ODE Network »
In Huh · Eunho Yang · Sung Ju Hwang · Jinwoo Shin -
2020 Poster: Neural Complexity Measures »
Yoonho Lee · Juho Lee · Sung Ju Hwang · Eunho Yang · Seungjin Choi -
2020 Poster: Adversarial Self-Supervised Contrastive Learning »
Minseon Kim · Jihoon Tack · Sung Ju Hwang -
2020 Poster: MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures »
Jeong Un Ryu · JaeWoong Shin · Hae Beom Lee · Sung Ju Hwang -
2020 Spotlight: MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures »
Jeong Un Ryu · JaeWoong Shin · Hae Beom Lee · Sung Ju Hwang -
2020 Poster: Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning »
Youngsung Kim · Jinwoo Shin · Eunho Yang · Sung Ju Hwang -
2020 Poster: Attribution Preservation in Network Compression for Reliable Network Interpretation »
Geondo Park · June Yong Yang · Sung Ju Hwang · Eunho Yang -
2018 Poster: Uncertainty-Aware Attention for Reliable Interpretation and Prediction »
Jay Heo · Hae Beom Lee · Saehoon Kim · Juho Lee · Kwang Joon Kim · Eunho Yang · Sung Ju Hwang -
2018 Poster: DropMax: Adaptive Variational Softmax »
Hae Beom Lee · Juho Lee · Saehoon Kim · Eunho Yang · Sung Ju Hwang -
2015 Poster: Closed-form Estimators for High-dimensional Generalized Linear Models »
Eunho Yang · Aurelie Lozano · Pradeep Ravikumar -
2015 Spotlight: Closed-form Estimators for High-dimensional Generalized Linear Models »
Eunho Yang · Aurelie Lozano · Pradeep Ravikumar -
2015 Poster: Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso »
Eunho Yang · Aurelie Lozano -
2014 Poster: Elementary Estimators for Graphical Models »
Eunho Yang · Aurelie Lozano · Pradeep Ravikumar -
2013 Poster: Conditional Random Fields via Univariate Exponential Families »
Eunho Yang · Pradeep Ravikumar · Genevera I Allen · Zhandong Liu -
2013 Poster: On Poisson Graphical Models »
Eunho Yang · Pradeep Ravikumar · Genevera I Allen · Zhandong Liu -
2013 Poster: Dirty Statistical Models »
Eunho Yang · Pradeep Ravikumar -
2012 Poster: Graphical Models via Generalized Linear Models »
Eunho Yang · Pradeep Ravikumar · Genevera I Allen · zhandong Liu -
2012 Oral: Graphical Models via Generalized Linear Models »
Eunho Yang · Pradeep Ravikumar · Genevera I Allen · zhandong Liu