Timezone: »
We introduce the concrete autoencoder, an end-to-end differentiable method for global feature selection, which efficiently extracts a sparse set of the most informative features and simultaneously learns a neural network to reconstruct the input data from the selected features. Our method is unsupervised, and is based on using a concrete selector layer as the encoder and using a standard neural network as the decoder. During the training phase, the temperature of the concrete selector layer is gradually decreased, which encourages a user-specified number of discrete features to be learned. During test time, the selected features can be used with the decoder network to reconstruct the remaining input features. We evaluate concrete autoencoders on a variety of datasets, where they significantly outperform state-of-the-art methods for feature selection and data reconstruction. In particular, on a large-scale gene expression dataset, the concrete autoencoder selects a small subset of genes whose expression levels can be use to impute the expression levels of the remaining genes. In doing so, it improves on the current widely-used expert-curated L1000 landmark genes, potentially saving experimental costs. The concrete autoencoder can be implemented by adding just a few lines of code to a standard autoencoder.
Author Information
Muhammed Fatih Balın (Bogazici )
Abubakar Abid (Stanford)
James Zou (Stanford University)
Related Events (a corresponding poster, oral, or spotlight)
-
2019 Poster: Concrete Autoencoders: Differentiable Feature Selection and Reconstruction »
Fri. Jun 14th 01:30 -- 04:00 AM Room Pacific Ballroom #188
More from the Same Authors
-
2021 : Meaningfully Explaining a Model's Mistakes »
· Abubakar Abid · James Zou -
2021 : Meaningfully Explaining a Model's Mistakes »
Abubakar Abid · James Zou -
2021 : MetaDataset: A Dataset of Datasets for Evaluating Distribution Shifts and Training Conflicts »
Weixin Liang · James Zou · Weixin Liang -
2021 : Have the Cake and Eat It Too? Higher Accuracy and Less Expense when Using Multi-label ML APIs Online »
Lingjiao Chen · James Zou · Matei Zaharia -
2021 : Machine Learning API Shift Assessments: Change is Coming! »
Lingjiao Chen · James Zou · Matei Zaharia -
2021 : Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions »
Kailas Vodrahalli · James Zou -
2022 : On the nonlinear correlation of ML performance across data subpopulations »
Weixin Liang · Yining Mao · Yongchan Kwon · Xinyu Yang · James Zou -
2023 : Improve Model Inference Cost with Image Gridding »
Shreyas Krishnaswamy · Lisa Dunlap · Lingjiao Chen · Matei Zaharia · James Zou · Joseph Gonzalez -
2023 : Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value »
Yongchan Kwon · James Zou -
2022 : GSCLIP : A Framework for Explaining Distribution Shifts in Natural Language »
Zhiying Zhu · Weixin Liang · James Zou -
2022 : Evaluation of ML in Health/Science »
James Zou -
2022 : Data Sculpting: Interpretable Algorithm for End-to-End Cohort Selection »
Ruishan Liu · James Zou -
2022 : Data Budgeting for Machine Learning »
Weixin Liang · James Zou -
2022 Poster: When and How Mixup Improves Calibration »
Linjun Zhang · Zhun Deng · Kenji Kawaguchi · James Zou -
2022 Poster: Efficient Online ML API Selection for Multi-Label Classification Tasks »
Lingjiao Chen · Matei Zaharia · James Zou -
2022 Poster: Improving Out-of-Distribution Robustness via Selective Augmentation »
Huaxiu Yao · Yu Wang · Sai Li · Linjun Zhang · Weixin Liang · James Zou · Chelsea Finn -
2022 Spotlight: Efficient Online ML API Selection for Multi-Label Classification Tasks »
Lingjiao Chen · Matei Zaharia · James Zou -
2022 Spotlight: Improving Out-of-Distribution Robustness via Selective Augmentation »
Huaxiu Yao · Yu Wang · Sai Li · Linjun Zhang · Weixin Liang · James Zou · Chelsea Finn -
2022 Spotlight: When and How Mixup Improves Calibration »
Linjun Zhang · Zhun Deng · Kenji Kawaguchi · James Zou -
2022 Poster: Meaningfully debugging model mistakes using conceptual counterfactual explanations »
Abubakar Abid · Mert Yuksekgonul · James Zou -
2022 Spotlight: Meaningfully debugging model mistakes using conceptual counterfactual explanations »
Abubakar Abid · Mert Yuksekgonul · James Zou -
2021 Poster: Improving Generalization in Meta-learning via Task Augmentation »
Huaxiu Yao · Long-Kai Huang · Linjun Zhang · Ying WEI · Li Tian · James Zou · Junzhou Huang · Zhenhui (Jessie) Li -
2021 Spotlight: Improving Generalization in Meta-learning via Task Augmentation »
Huaxiu Yao · Long-Kai Huang · Linjun Zhang · Ying WEI · Li Tian · James Zou · Junzhou Huang · Zhenhui (Jessie) Li -
2021 Poster: How to Learn when Data Reacts to Your Model: Performative Gradient Descent »
Zachary Izzo · Lexing Ying · James Zou -
2021 Spotlight: How to Learn when Data Reacts to Your Model: Performative Gradient Descent »
Zachary Izzo · Lexing Ying · James Zou -
2020 Poster: A Distributional Framework For Data Valuation »
Amirata Ghorbani · Michael Kim · James Zou -
2019 Poster: Discovering Conditionally Salient Features with Statistical Guarantees »
Jaime Roquero Gimenez · James Zou -
2019 Oral: Discovering Conditionally Salient Features with Statistical Guarantees »
Jaime Roquero Gimenez · James Zou -
2019 Poster: Data Shapley: Equitable Valuation of Data for Machine Learning »
Amirata Ghorbani · James Zou -
2019 Oral: Data Shapley: Equitable Valuation of Data for Machine Learning »
Amirata Ghorbani · James Zou -
2018 Poster: CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions »
Kevin Tian · Teng Zhang · James Zou -
2018 Oral: CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions »
Kevin Tian · Teng Zhang · James Zou