Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
1208.2015
Cited By
v1
v2
v3 (latest)
Sharp analysis of low-rank kernel matrix approximations
Annual Conference Computational Learning Theory (COLT), 2012
9 August 2012
Francis R. Bach
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Sharp analysis of low-rank kernel matrix approximations"
50 / 159 papers shown
Estimating Dimensionality of Neural Representations from Finite Samples
Chanwoo Chun
Abdulkadir Canatar
SueYeon Chung
Daniel D. Lee
129
1
0
30 Sep 2025
Uniform convergence for Gaussian kernel ridge regression
Paul Dommel
Rajmadan Lakshmanan
188
0
0
15 Aug 2025
Diagonally-Weighted Generalized Method of Moments Estimation for Gaussian Mixture Modeling
Liu Zhang
Oscar Mickelin
Sheng Xu
A. Singer
258
0
0
28 Jul 2025
Faster Low-Rank Approximation and Kernel Ridge Regression via the Block-Nyström Method
Annual Conference Computational Learning Theory (COLT), 2025
Sachin Garg
Michał Dereziński
278
1
0
21 Jun 2025
Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning
Wanyun Xie
F. Tonin
Volkan Cevher
254
7
0
30 May 2025
Autoencoding Random Forests
Binh Duc Vu
Jan Kapar
Marvin N. Wright
David S. Watson
450
0
0
27 May 2025
Computational Efficiency under Covariate Shift in Kernel Ridge Regression
Andrea Della Vecchia
Arnaud Mavakala Watusadisi
Ernesto De Vito
Lorenzo Rosasco
251
2
0
20 May 2025
Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project
Pratik Rathore
Zachary Frangella
Sachin Garg
Shaghayegh Fazliani
Michał Dereziński
Madeleine Udell
470
3
0
19 May 2025
Decision Making under Model Misspecification: DRO with Robust Bayesian Ambiguity Sets
Charita Dellaporta
Patrick O'Hara
Theodoros Damoulas
366
1
0
06 May 2025
Learning with Exact Invariances in Polynomial Time
Ashkan Soleymani
B. Tahmasebi
Stefanie Jegelka
Patrick Jaillet
455
1
0
27 Feb 2025
CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks
Neural Information Processing Systems (NeurIPS), 2024
Miria Feng
Zachary Frangella
Mert Pilanci
BDL
465
4
0
02 Nov 2024
Convergence Analysis of regularised Nyström method for Functional Linear Regression
Inverse Problems (IP), 2024
Naveen Gupta
Sivananthan Sampath
303
0
0
25 Oct 2024
Estimating the Spectral Moments of the Kernel Integral Operator from Finite Sample Matrices
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Chanwoo Chun
SueYeon Chung
Daniel D. Lee
452
2
0
23 Oct 2024
Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression
Pratik Rathore
Zachary Frangella
Madeleine Udell
Michał Dereziński
Madeleine Udell
391
0
0
14 Jul 2024
A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data
Efthymios Costa
Ioanna Papatsouma
Angelos Markos
272
3
0
03 Jul 2024
Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning
Michał Dereziński
Michael W. Mahoney
330
21
0
17 Jun 2024
Deep Sketched Output Kernel Regression for Structured Prediction
T. Ahmad
Junjie Yang
Pierre Laforgue
Florence dÁlché-Buc
UQCV
324
1
0
13 Jun 2024
On the Approximation of Kernel functions
Paul Dommel
Alois Pichler
315
4
0
11 Mar 2024
A Bound on the Maximal Marginal Degrees of Freedom
Paul Dommel
389
1
0
20 Feb 2024
Challenges in Training PINNs: A Loss Landscape Perspective
Pratik Rathore
Weimu Lei
Zachary Frangella
Lu Lu
Madeleine Udell
AI4CE
PINN
ODL
295
128
0
02 Feb 2024
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning
Jian Li
Yong Liu
Wei Wang
Haoran Wu
Weiping Wang
FedML
309
7
0
05 Jan 2024
Estimating Koopman operators with sketching to provably learn large scale dynamical systems
Neural Information Processing Systems (NeurIPS), 2023
Giacomo Meanti
Antoine Chatalic
Vladimir Kostic
P. Novelli
Massimiliano Pontil
Lorenzo Rosasco
345
16
0
07 Jun 2023
Dropout Drops Double Descent
Japanese Journal of Statistics and Data Science (JSDS), 2023
Tianbao Yang
J. Suzuki
333
1
0
25 May 2023
Lp- and Risk Consistency of Localized SVMs
Neurocomputing (Neurocomputing), 2023
Hannes Köhler
266
0
0
16 May 2023
Robust, randomized preconditioning for kernel ridge regression
Mateo Díaz
Ethan N. Epperly
Zachary Frangella
J. Tropp
R. Webber
646
19
0
24 Apr 2023
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
T. Ahmad
Luc Brogat-Motte
Pierre Laforgue
Florence dÁlché-Buc
BDL
389
6
0
20 Feb 2023
Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence
Journal of Chemical Theory and Computation (JCTC), 2022
Stefan Blücher
Klaus-Robert Muller
Stefan Chmiela
369
5
0
24 Dec 2022
Overparameterized random feature regression with nearly orthogonal data
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Zhichao Wang
Yizhe Zhu
437
9
0
11 Nov 2022
A kernel-based quantum random forest for improved classification
Quantum Machine Intelligence (QMI), 2022
Maiyuren Srikumar
C. Hill
Lloyd C. L. Hollenberg
349
28
0
05 Oct 2022
Target alignment in truncated kernel ridge regression
Neural Information Processing Systems (NeurIPS), 2022
Arash A. Amini
R. Baumgartner
Dai Feng
302
4
0
28 Jun 2022
Fast Kernel Methods for Generic Lipschitz Losses via
p
p
p
-Sparsified Sketches
T. Ahmad
Pierre Laforgue
Florence dÁlché-Buc
520
8
0
08 Jun 2022
Semi-Parametric Inducing Point Networks and Neural Processes
International Conference on Learning Representations (ICLR), 2022
R. Rastogi
Yair Schiff
Alon Hacohen
Zhaozhi Li
I-Hsiang Lee
Yuntian Deng
M. Sabuncu
Volodymyr Kuleshov
3DPC
321
8
0
24 May 2022
Generalized Reference Kernel for One-class Classification
IEEE International Joint Conference on Neural Network (IJCNN), 2022
Jenni Raitoharju
Alexandros Iosifidis
370
3
0
01 May 2022
Learning new physics efficiently with nonparametric methods
Marco Letizia
Gianvito Losapio
Marco Rando
Gaia Grosso
A. Wulzer
M. Pierini
M. Zanetti
Lorenzo Rosasco
OOD
347
40
0
05 Apr 2022
The Spectral Bias of Polynomial Neural Networks
International Conference on Learning Representations (ICLR), 2022
Moulik Choraria
L. Dadi
Grigorios G. Chrysos
Julien Mairal
Volkan Cevher
267
25
0
27 Feb 2022
Random Gegenbauer Features for Scalable Kernel Methods
International Conference on Machine Learning (ICML), 2022
Insu Han
A. Zandieh
H. Avron
193
5
0
07 Feb 2022
An Improved Frequent Directions Algorithm for Low-Rank Approximation via Block Krylov Iteration
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021
Chenhao Wang
Qianxin Yi
Xiuwu Liao
Yao Wang
337
2
0
24 Sep 2021
Sharp Analysis of Random Fourier Features in Classification
AAAI Conference on Artificial Intelligence (AAAI), 2021
Zhu Li
234
6
0
22 Sep 2021
Molecular Energy Learning Using Alternative Blackbox Matrix-Matrix Multiplication Algorithm for Exact Gaussian Process
Jiace Sun
Lixue Cheng
Thomas F. Miller
198
3
0
20 Sep 2021
Deformed semicircle law and concentration of nonlinear random matrices for ultra-wide neural networks
Zhichao Wang
Yizhe Zhu
365
27
0
20 Sep 2021
Fast Sketching of Polynomial Kernels of Polynomial Degree
Zhao Song
David P. Woodruff
Zheng Yu
Lichen Zhang
370
48
0
21 Aug 2021
Neural Operator: Learning Maps Between Function Spaces
Nikola B. Kovachki
Zong-Yi Li
Burigede Liu
Kamyar Azizzadenesheli
K. Bhattacharya
Andrew M. Stuart
Anima Anandkumar
AI4CE
986
590
0
19 Aug 2021
Uniform Function Estimators in Reproducing Kernel Hilbert Spaces
Paul Dommel
Alois Pichler
84
1
0
16 Aug 2021
Training very large scale nonlinear SVMs using Alternating Direction Method of Multipliers coupled with the Hierarchically Semi-Separable kernel approximations
EURO Journal on Computational Optimization (EJCO), 2021
S. Cipolla
J. Gondzio
399
15
0
09 Aug 2021
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions
Luigi Carratino
Stefano Vigogna
Daniele Calandriello
Lorenzo Rosasco
225
7
0
23 Jun 2021
Exponential Error Convergence in Data Classification with Optimized Random Features: Acceleration by Quantum Machine Learning
H. Yamasaki
Sho Sonoda
297
8
0
16 Jun 2021
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes
Veit Wild
Motonobu Kanagawa
Dino Sejdinovic
259
18
0
02 Jun 2021
Uncertainty quantification for distributed regression
V. Avanesov
UQCV
231
0
0
24 May 2021
Statistical Optimality and Computational Efficiency of Nyström Kernel PCA
Journal of machine learning research (JMLR), 2021
Nicholas Sterge
Bharath K. Sriperumbudur
319
18
0
19 May 2021
Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality
International Conference on Machine Learning (ICML), 2021
Jonathan Lacotte
Yifei Wang
Mert Pilanci
264
18
0
15 May 2021
1
2
3
4
Next
Page 1 of 4