JMLR Proceedings

The proceedings of our workshop are published in JMLR W&CP, Volume 44: Proceedings of The 1st International Workshop on “Feature Extraction: Modern Questions and Challenges”, NIPS. You can also view them in the list below.

Accepted Papers

A Survey of Modern Questions and Challenges in Feature Extraction

Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar

[abs] [pdf]

A Dimension-Independent Generalization Bound for Kernel Supervised Principal Component Analysis

Hassan Ashtiani, Ali Ghodsi

[abs] [pdf]

Learning Sparse Metrics, One Feature at a Time

Yuval, Atzmon, Uri Shalit, Gal Chechik

[abs] [pdf]

Stage-wise Training: An Improved Feature Learning Strategy for Deep Models

Elnaz Barshan, Paul Fieguth

[abs] [pdf]

Learning Multi-channel Deep Feature Representations for Face Recognition

Xue-wen Chen, Melih Aslan, Kunlei Zhang, Thomas Huang

[abs] [pdf]

Kernel Extraction via Voted Risk Minimization

Corinna Cortes, Prasoon Goyal, Vitaly Kuznetsov, Mehryar Mohri

[abs] [pdf]

A Computationally Efficient Method for Estimating Semi Parametric Regression Functions

Xia Cui, Ying Lu, Heng Peng

[abs] [pdf]

Spatiotemporal Feature Extraction with Data-Driven Koopman Operators

Dimitrios Giannakis, Joanna Slawinska, Zhizhen Zhao

[abs] [pdf]

Convolutional Dictionary Learning through Tensor Factorization

Furong Huang, Animashree Anandkumar

[abs] [pdf]

FEAST at Play: Feature ExtrAction using Score function Tensors

Majid Janzamin, Hanie Sedghi, U.N. Niranjan, Animashree Anandkumar

[abs] [pdf]

The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors

Melih Kandemir, Fred A. Hamprecht

[abs] [pdf]

Deep Clustered Convolutional Kernels

Minyoung Kim, Luca Rigazio

[abs] [pdf]

Theory and Algorithms for the Localized Setting of Learning Kernels

Yunwen Lei, Alexander Binder, Ürün Dogan, Marius Kloft

[abs] [pdf]

Convergent Learning: Do different neural networks learn the same representations?

Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, John Hopcroft

[abs] [pdf]

Hierarchical Feature Extraction for Efficient Design of Microfluidic Flow Patterns

Kin Gwn Lore, Daniel Stoecklein, Michael Davies, Baskar Ganapathysubramanian, Soumik Sarkar

[abs] [pdf]

Generalization Bounds for Supervised Dimensionality Reduction

Mehryar Mohri, Afshin Rostamizadeh, Dmitry Storcheus

[abs] [pdf]

Modular Autoencoders for Ensemble Feature Extraction

Henry Reeve, Gavin Brown

[abs] [pdf]

Minimum description length (MDL) regularization for online learning

Gil I. Shamir

[abs] [pdf]

Covariance Selection in the Linear Mixed Effect Mode

Jonathan P. Williams, Ying Lu

[abs] [pdf]

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