Fetching the paper…
Reading the bibliography…
The problem of learning one task using samples from another task is central to transfer learning.
Averaging fluctuations in resolvents of random band matrices
L. Erdős, A. Knowles, and H.-T. Yau · 1926
Earlier work this paper cites.
Distribution of eigenvalues for some sets of random matrices
V. A. Marčenko and L. A. Pastur · 1967
Earlier work this paper cites.
Multitask learning
R. Caruana · 1997
Earlier work this paper cites.
No eigenvalues outside the support of the limiting spectral distribution of large-dimensional sample covariance matrices
Z. D. Bai and J. W. Silverstein · 1998
Earlier work this paper cites.
A model of inductive bias learning
J. Baxter · 2000
Earlier work this paper cites.
An Introduction to Multivariate Statistical Analysis
T. W. Anderson · 2003
Earlier work this paper cites.
Exploiting task relatedness for multiple task learning
S. Ben-David and R. Schuller · 2003
Earlier work this paper cites.
Random matrix theory and wireless communications
A. M. Tulino and S. Verdú · 2004
Earlier work this paper cites.
Bounds for linear multi-task learning
A. Maurer · 2006
Earlier work this paper cites.
Lectures on the combinatorics of free probability , volume 13
A. Nica and R. Speicher · 2006
Earlier work this paper cites.
A new approach to subordination results in free probability
S. T. Belinschi and H. Bercovici · 2007
Earlier work this paper cites.
Learning from multiple sources
K. Crammer, M. Kearns, and J. Wortman · 2008
Earlier work this paper cites.
Spectral analysis of large dimensional random matrices
Z. Bai and J. W. Silverstein · 2010
Earlier work this paper cites.
The arithmetic of distributions in free probability theory
G. P. Chistyakov and F. Götze · 2011
Earlier work this paper cites.
Oracle inequalities and optimal inference under group sparsity
K. Lounici, M. Pontil, S. Van De Geer, and A. B. Tsybakov · 2011
Earlier work this paper cites.
Minimax rates of estimation for high-dimensional linear regression over ℓ q \ell_{q} -balls
G. Raskutti, M. J. Wainwright, and B. Yu · 2011
Earlier work this paper cites.
Topics in Random Matrix Theory , volume 132
T. Tao · 2012
Cited alongside, same era.
Isotropic local laws for sample covariance and generalized Wigner matrices
A. Bloemendal, L. Erdős, A. Knowles, H.-T. Yau, and J. Yin · 2014
Cited alongside, same era.
Universality of covariance matrices
N. S. Pillai and J. Yin · 2014
Cited alongside, same era.
Anisotropic local laws for random matrices
A. Knowles and J. Yin · 2016
Cited alongside, same era.
Local law for random Gram matrices
J. Alt, L. Erdős, and T. Krüger · 2017
Cited alongside, same era.
A dynamical approach to random matrix theory
L. Erdos and H.-T. Yau · 2017
Cited alongside, same era.
Local circular law for the product of a deterministic matrix with a random matrix
Edge universality of separable covariance matrices
F. Yang · 2019
Later among the works it cites.
Benign overfitting in linear regression
P. L. Bartlett, P. M. Long, G. Lugosi, and A. Tsigler · 2020
Closest in time.
Wonder: Weighted one-shot distributed ridge regression in high dimensions
E. Dobriban and Y. Sheng · 2020
Closest in time.
Local laws for polynomials of Wigner matrices
L. Erdős, T. Krüger, and Y. Nemish · 2020
Closest in time.
Minimax lower bounds for transfer learning with linear and one-hidden layer neural networks
S. M. M. Kalan, Z. Fabian, A. S. Avestimehr, and M. Soltanolkotabi · 2020
Closest in time.
A precise high-dimensional asymptotic theory for boosting and min-l1-norm interpolated classifiers
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
H. Xi, F. Yang, and J. Yin · 2017
Cited alongside, same era.
CLT for eigenvalue statistics of large-dimensional general Fisher matrices with applications
S. Zheng, Z. Bai, and J. Yao · 2017
Cited alongside, same era.
A necessary and sufficient condition for edge universality at the largest singular values of covariance matrices
X. Ding and F. Yang · 2018
Cited alongside, same era.
High-dimensional asymptotics of prediction: Ridge regression and classification
E. Dobriban and S. Wager · 2018
Cited alongside, same era.
Reconciling modern machine-learning practice and the classical bias–variance trade-off
M. Belkin, D. Hsu, S. Ma, and S. Mandal · 2019
Cited alongside, same era.
Eigenvalue distributions of variance components estimators in high-dimensional random effects models
Z. Fan and I. M. Johnstone · 2019
Cited alongside, same era.
T. Liang and P. Sur · 2020
Closest in time.
Just interpolate: Kernel “ridgeless” regression can generalize
T. Liang, A. Rakhlin, et al · 2020
Closest in time.
Understanding and improving information transfer in multi-task learning
S. Wu, H. R. Zhang, and C. Ré · 2020
Closest in time.
Transfer learning for nonparametric classification: Minimax rate and adaptive classifier
T. T. Cai and H. Wei · 2021
Closest in time.
Finite-sample analysis of interpolating linear classifiers in the overparameterized regime
N. S. Chatterji and P. M. Long · 2021
Closest in time.
Spiked separable covariance matrices and principal components
X. Ding and F. Yang · 2021
Closest in time.
Near-optimal linear regression under distribution shift
Q. Lei, W. Hu, and J. Lee · 2021
Closest in time.
Adaptive and robust multi-task learning
Y. Duan and K. Wang · 2022
Closest in time.
A no-free-lunch theorem for multitask learning
S. Hanneke and S. Kpotufe · 2022
Closest in time.
Transfer learning for high-dimensional linear regression: Prediction, estimation, and minimax optimality
S. Li, T. T. Cai, and H. Li · 2022
Closest in time.
Pseudo-labeling for kernel ridge regression under covariate shift
K. Wang · 2023
Closest in time.