Fetching the paper…
Reading the bibliography…
Random feature methods have been successful in various machine learning tasks, are easy to compute, and come with theoretical accuracy bounds.
About the descriptive content of quantum theoretical kinematics and mechanics
Heisenberg, W · 1927
Earlier work this paper cites.
The perceptron: A model for brain functioning. i
Block, H.-D · 1962
Earlier work this paper cites.
A simple lemma on greedy approximation in Hilbert space and convergence rates for projection pursuit regression and neural network training
Jones, L. K · 1992
Earlier work this paper cites.
Universal approximation bounds for superpositions of a sigmoidal function
Barron, A. R · 1993
Earlier work this paper cites.
Rademacher and Gaussian complexities: Risk bounds and structural results
Bartlett, P. L., and Mendelson, S · 2002
Earlier work this paper cites.
On the computational power of circuits of spiking neurons
Maass, W., and Markram, H · 2004
Earlier work this paper cites.
Stable signal recovery from incomplete and inaccurate measurements
Candes, E. J., Romberg, J. K., and Tao, T · 2006
Earlier work this paper cites.
Near-optimal signal recovery from random projections: Universal encoding strategies?
Candes, E. J., and Tao, T · 2006
Earlier work this paper cites.
Compressed sensing
Donoho, D. L · 2006
Earlier work this paper cites.
Randomized clustering forests for building fast and discriminative visual vocabularies
Moosmann, F., Triggs, B., and Jurie, F · 2006
Earlier work this paper cites.
Random features for large-scale kernel machines
Rahimi, A., Recht, B., et al · 2007
Earlier work this paper cites.
Detecting disease-causing genes by LASSO-Patternsearch algorithm
Shi, Weiliang and Lee, Kristine E and Wahba, Grace · 2007
Earlier work this paper cites.
l-1 regularization in infinite dimensional feature spaces
Rosset, Saharon and Swirszcz, Grzegorz and Srebro, Nathan and Zhu, Ji · 2007
Earlier work this paper cites.
Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems
Figueiredo, Mário AT and Nowak, Robert D and Wright, Stephen J · 2007
Earlier work this paper cites.
Uniform approximation of functions with random bases
Rahimi, A., and Recht, B · 2008
Earlier work this paper cites.
Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning
Rahimi, A., and Recht, B · 2008
Earlier work this paper cites.
Global sensitivity analysis: the primer
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S · 2008
Earlier work this paper cites.
On recovery of sparse signals via ℓ 1 \ell_{1} minimization
Cai, T. T., Xu, G., and Zhang, J · 2009
Earlier work this paper cites.
Sparse additive models
Ravikumar, Pradeep and Lafferty, John and Liu, Han and Wasserman, Larry · 2009
Earlier work this paper cites.
On decompositions of multivariate functions
Kuo, F., Sloan, I., Wasilkowski, G., and Woźniakowski, H · 2010
Earlier work this paper cites.
Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit
Needell, D. and Vershynin, R · 2010
Earlier work this paper cites.
Introduction to the non-asymptotic analysis of random matrices
Vershynin, R · 2010
Earlier work this paper cites.
Approximation of functions of few variables in high dimensions
DeVore, R., Petrova, G., and Wojtaszczyk, P · 2011
Cited alongside, same era.
A non-adapted sparse approximation of PDEs with stochastic inputs
Doostan, A., and Owhadi, H · 2011
Cited alongside, same era.
Efficient kernel clustering using random Fourier features
Chitta, R., Jin, R., and Jain, A. K · 2012
Cited alongside, same era.
Learning functions of few arbitrary linear parameters in high dimensions
Fornasier, M., Schnass, K., and Vybiral, J · 2012
Cited alongside, same era.
Sparse Legendre expansions via l1-minimization
Rauhut, H., and Ward, R · 2012
Cited alongside, same era.
Nyström method vs random Fourier features: A theoretical and empirical comparison
Yang, T., Li, Y.-F., Mahdavi, M., Jin, R., and Zhou, Z.-H · 2012
Cited alongside, same era.
A near-stationary subspace for ridge approximation
Constantine, P. G., Eftekhari, A., Hokanson, J., and Ward, R. A · 2017
Later among the works it cites.
UCI machine learning repository, 2017
Dua, D., and Graff, C · 2017
Later among the works it cites.
Group sparse additive machine
Chen, Hong and Wang, Xiaoqian and Deng, Cheng and Huang, Heng · 2017
Later among the works it cites.
Generalization Properties of Learning with Random Features
Rudi, Alessandro and Rosasco, Lorenzo · 2017
Later among the works it cites.
Infinite-dimensional compressed sensing and function interpolation
Adcock, B · 2018
Later among the works it cites.
Polynomial approximation via compressed sensing of high-dimensional functions on lower sets
Chkifa, A., Dexter, N, Tran, H. and Webster, C · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Bayesian lasso for semiparametric structural equation models
Guo, Ruixin and Zhu, Hongtu and Chow, Sy-Miin and Ibrahim, Joseph G · 2012
Cited alongside, same era.
A Mathematical Introduction to Compressive Sensing
Foucart, S., and Rauhut, H · 2013
Cited alongside, same era.
Probability in Banach Spaces: isoperimetry and processes
Ledoux, M., and Talagrand, M · 2013
Cited alongside, same era.
Learning with infinitely many features
Rakotomamonjy, Alain and Flamary, Rémi and Yger, Florian · 2013
Cited alongside, same era.
Active subspace methods in theory and practice: applications to kriging surfaces
Constantine, P. G., Dow, E., and Wang, Q · 2014
Cited alongside, same era.
Tight bounds for the expected risk of linear classifiers and PAC-Bayes finite-sample guarantees
Honorio, J., and Jaakkola, T · 2014
Cited alongside, same era.
Gradient descent provably optimizes over-parameterized neural networks
Du, S. S., Zhai, X., Poczos, B., and Singh, A · 2018
Later among the works it cites.
Neural tangent kernel: Convergence and generalization in neural networks
Jacot, A., Gabriel, F., and Hongler, C · 2018
Later among the works it cites.
Learning overparameterized neural networks via stochastic gradient descent on structured data
Li, Y., and Liang, Y · 2018
Later among the works it cites.
Extracting sparse high-dimensional dynamics from limited data
Schaeffer, H., Tran, G., and Ward, R · 2018
Later among the works it cites.
Streaming Kernel PCA with O ( n ) O(\sqrt{n}) Random Features
Ullah, Enayat and Mianjy, Poorya and Marinov, Teodor Vanislavov and Arora, Raman · 2018
Later among the works it cites.
Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
Arora, S., Du, S., Hu, W., Li, Z., and Wang, R · 2019
Later among the works it cites.
Additive function approximation in the brain
Harris, K. D · 2019
Later among the works it cites.
Towards a unified analysis of random Fourier features
Li, Z., Ton, J.-F., Oglic, D., and Sejdinovic, D · 2019
Later among the works it cites.
Approximation of high-dimensional periodic functions with Fourier-based methods
Potts, D. and Schmischke, M · 2019
Later among the works it cites.
Learning multivariate functions with low-dimensional structures using polynomial bases
Potts, D. and Schmischke, M · 2019
Later among the works it cites.
On kernel derivative approximation with random Fourier features
Szabó, Zoltán and Sriperumbudur, Bharath · 2019
Later among the works it cites.
The generalization error of random features regression: Precise asymptotics and double descent curve
Mei, S., and Montanari, A · 2020
Later among the works it cites.
Extracting structured dynamical systems using sparse optimization with very few samples
Schaeffer, H., Tran, G., Ward, R., and Zhang, L · 2020
Later among the works it cites.
Few-shot learning via learning the representation, provably
Du, Simon S and Hu, Wei and Kakade, Sham M and Lee, Jason D and Lei, Qi · 2020
Later among the works it cites.
Sparse Shrunk Additive Models
Liu, Guodong and Chen, Hong and Huang, Heng · 2020
Later among the works it cites.