Fetching the paper…
Reading the bibliography…
Good generalization performance on high-dimensional data crucially hinges on a simple structure of the ground truth and a corresponding strong inductive bias of the estimator.
On Milman’s inequality and random subspaces which escape through a mesh in ℝ n \mathbb{R}^{n}
Gordon, Y · 1988
Earlier work this paper cites.
On the volume of the intersection of two l p n l_{p}^{n} balls
Schechtman, G. and Zinn, J · 1989
Earlier work this paper cites.
The Tight Constant in the Dvoretzky-Kiefer-Wolfowitz Inequality
Massart, P · 1990
Earlier work this paper cites.
A heat semigroup approach to concentration on the sphere and on a compact riemannian manifold
Ledoux, M · 1992
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
Tibshirani, R · 1996
Earlier work this paper cites.
Atomic decomposition by basis pursuit
Chen, S. S., Donoho, D. L., and Saunders, M. A · 1998
Earlier work this paper cites.
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring
Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al · 1999
Earlier work this paper cites.
Duality and geometry in svm classifiers
Bennett, K. P. and Bredensteiner, E. J · 2000
Earlier work this paper cites.
A tail inequality for suprema of unbounded empirical processes with applications to markov chains
Adamczak, R · 2008
Earlier work this paper cites.
1-bit compressive sensing
Boufounos, P. T. and Baraniuk, R. G · 2008
Earlier work this paper cites.
High-dimensional generalized linear models and the lasso
Van de Geer, S. A · 2008
Earlier work this paper cites.
Information-theoretic limits on sparsity recovery in the high-dimensional and noisy setting
Wainwright, M. J · 2009
Earlier work this paper cites.
Stability and instance optimality for Gaussian measurements in compressed sensing
Wojtaszczyk, P · 2010
Earlier work this paper cites.
Minimax rates of estimation for high-dimensional linear regression over ℓ q \ell_{q} -balls
Raskutti, G., Wainwright, M. J., and Yu, B · 2011
Earlier work this paper cites.
Concentration inequalities for order statistics
Boucheron, S. and Thomas, M · 2012
Earlier work this paper cites.
On the robustness of minimum-norm interpolators
Chinot, G., Löffler, M., and van de Geer, S · 2012
Cited alongside, same era.
The mnist database of handwritten digit images for machine learning research
Deng, L · 2012
Cited alongside, same era.
Robust 1-bit compressed sensing and sparse logistic regression: A convex programming approach
Plan, Y. and Vershynin, R · 2012
Cited alongside, same era.
Learning without concentration
Mendelson, S · 2014
Cited alongside, same era.
Bounding the smallest singular value of a random matrix without concentration
Koltchinskii, V. and Mendelson, S · 2015
Cited alongside, same era.
Regularized linear regression: A precise analysis of the estimation error
Thrampoulidis, C., Oymak, S., and Hassibi, B · 2015
On lp-support vector machines and multidimensional kernels
Blanco, V., Puerto, J., and Rodriguez-Chia, A. M · 2020
Later among the works it cites.
Implicit bias of gradient descent for wide two-layer neural networks trained with the logistic loss
Chizat, L. and Bach, F · 2020
Later among the works it cites.
Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree
Ju, P., Lin, X., and Liu, J · 2020
Later among the works it cites.
Finite versus infinite neural networks: an empirical study
Lee, J., Schoenholz, S., Pennington, J., Adlam, B., Xiao, L., Novak, R., and Sohl-Dickstein, J · 2020
Later among the works it cites.
Gradient descent maximizes the margin of homogeneous neural networks
Lyu, K. and Li, J · 2020
Later among the works it cites.
Harmless interpolation of noisy data in regression
Muthukumar, V., Vodrahalli, K., Subramanian, V., and Sahai, A · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Regularization and the small-ball method II: complexity dependent error rates
Lecué, G. and Mendelson, S · 2017
Cited alongside, same era.
Random version of dvoretzky’s theorem in ℓ p n \ell_{p}^{n}
Paouris, G., Valettas, P., and Zinn, J · 2017
Cited alongside, same era.
High-dimensional classification by sparse logistic regression
Abramovich, F. and Grinshtein, V · 2018
Cited alongside, same era.
High-dimensional asymptotics of prediction: Ridge regression and classification
Dobriban, E. and Wager, S · 2018
Cited alongside, same era.
Neural tangent kernel: Convergence and generalization in neural networks
Jacot, A., Gabriel, F., and Hongler, C · 2018
Cited alongside, same era.
The implicit bias of gradient descent on separable data
Soudry, D., Hoffer, E., Nacson, M. S., Gunasekar, S., and Srebro, N · 2018
Cited alongside, same era.
Later among the works it cites.
Neural tangents: Fast and easy infinite neural networks in python
Novak, R., Xiao, L., Hron, J., Lee, J., Alemi, A. A., Sohl-Dickstein, J., and Schoenholz, S. S · 2020
Later among the works it cites.
Benign overfitting in ridge regression
Tsigler, A. and Bartlett, P. L · 2020
Later among the works it cites.
A model of double descent for high-dimensional binary linear classification
Deng, Z., Kammoun, A., and Thrampoulidis, C · 2021
Later among the works it cites.
Uniform convergence of interpolators: Gaussian width, norm bounds and benign overfitting
Koehler, F., Zhou, L., Sutherland, D. J., and Srebro, N · 2021
Later among the works it cites.
Minimum ℓ 1 \ell_{1} -norm interpolators: Precise asymptotics and multiple descent
Li, Y. and Wei, Y · 2021
Later among the works it cites.
Classification vs regression in overparameterized regimes: Does the loss function matter?
Muthukumar, V., Narang, A., Subramanian, V., Belkin, M., Hsu, D., and Sahai, A · 2021
Later among the works it cites.
Zhou, L., Koehler, F., Sutherland, D. J., and Srebro, N · 2021
Later among the works it cites.
Foolish crowds support benign overfitting
Chatterji, N. S. and Long, P. M · 2022
Closest in time.
Tight bounds for minimum l1-norm interpolation of noisy data
Wang, G., Donhauser, K., and Yang, F · 2022
Closest in time.