On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning | Paper Citation Record

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This paper cites and Szepesv \'a ri, C.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Szepesv \'a ri, C

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning B., and Misiakiewicz, T

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning B., and Misiakiewicz, T

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Optimization algorithms on matrix manifolds

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Pennington, J

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Parrilo, P

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This paper cites When Does Preconditioning Help or Hurt Generalization?.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning When Does Preconditioning Help or Hurt Generalization?

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Locoprop: Enhancing backprop via local loss optimization

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Scalable Second Order Optimization for Deep Learning

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This paper cites Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Implicit regularization in deep matrix factorization

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning B., and Martens, J

Reference 12

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning A., Suzuki, T., Wang, Z., Wu, D., and Yang, G

Reference 13

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This paper cites A., Suzuki, T., Wang, Z., and Wu, D.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning A., Suzuki, T., Wang, Z., and Wu, D

Reference 14

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Scaling laws of optimization, 2024

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This paper cites and Lee, J.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Lee, J

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Hidden progress in deep learning: SGD learns parities near the computational limit

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Online stochastic gradient descent on non-convex losses from high-dimensional inference

Reference 18

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Gradient descent on neurons and its link to approximate second-order optimization

Reference 19

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Modular Duality in Deep Learning

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Old Optimizer, New Norm: An Anthology

Reference 21

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Learning time-scales in two-layers neural networks

Reference 22

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Unresolved cited work

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Mondelli, M

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Privacy for Free in the Overparameterized Regime

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This paper cites Beyond the universal law of robustness: Sharper laws for random features and neural tangent kernels.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Beyond the universal law of robustness: Sharper laws for random features and neural tangent kernels

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Practical Gauss- Netwon optimisation for deep learning

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning H., Hansen, S

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems

Reference 29

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Exploiting shared representations for personalized federated learning

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Provable multi-task representation learning by two-layer ReLU neural networks

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Asymptotics of feature learning in two-layer networks after one gradient-step

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Benchmarking Neural Network Training Algorithms

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Neural networks can learn representations with gradient descent

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning How two-layer neural networks learn, one (giant) step at a time

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning The benefits of reusing batches for gradient descent in two-layer networks: Breaking the curse of information and leap exponents

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Modular block-diagonal curvature approximations for feedforward architectures

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Non-convergence of Adam and other adaptive stochastic gradient descent optimization methods for non-vanishing learning rates

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Unresolved cited work

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Wager, S

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning S., Hu, W., Kakade, S

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Adaptive subgradient methods for online learning and stochastic optimization

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Proximal backpropagation

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Learning Hierarchical Polynomials of Multiple Nonlinear Features with Three-Layer Networks

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Linearized two-layers neural networks in high dimension

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning When do neural networks outperform kernel methods? Journal of Statistical Mechanics: Theory and Experiment, 2021 0 (12), 2021 b

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning A family of variable-metric methods derived by variational means

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Practical quasi- Netwon methods for training deep neural networks

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning The G aussian equivalence of generative models for learning with shallow neural networks

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Spectral Phase Transitions in Non-Linear Wigner Spiked Models

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Unresolved cited work

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Shampoo: Preconditioned stochastic tensor optimization

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Nica, M

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Unresolved cited work

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Javanmard, A

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Unresolved cited work

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning M., and Zhang, T

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Lu, Y

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Szegedy, C

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning On the Parameterization of Second-Order Optimization Effective Towards the Infinite Width

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This paper cites Neural tangent kernel: Convergence and generalization in neural networks.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Neural tangent kernel: Convergence and generalization in neural networks

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:43.176504Z

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Observation 1279429c-1794-4140-be6d-178de71424b7 · outbound

This paper cites Low-rank matrix completion using alternating minimization.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Low-rank matrix completion using alternating minimization

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:43.163081Z

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Observation 026a4f86-c81a-4f80-9bd7-24b01f956401 · outbound

This paper cites Muon: An optimizer for hidden layers in neural networks, 2024.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Muon: An optimizer for hidden layers in neural networks, 2024

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:43.147831Z

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Observation e77cab59-85e7-4356-b651-1126434c3d34 · outbound

This paper cites Improving Generalization Performance by Switching from Adam to SGD.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Improving Generalization Performance by Switching from Adam to SGD

Reference 65

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Unresolved cited work

Reference 66

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Observation 869a9e7f-5c29-4588-8006-ceb7733b6b02 · outbound

This paper cites M., Ma, T., and Liang, P.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning M., Ma, T., and Liang, P

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:43.123219Z

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Observation 72e137fb-aec8-4d12-af33-abbead0af4b2 · outbound

This paper cites Scalable Optimization in the Modular Norm.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Scalable Optimization in the Modular Norm

Reference 68

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Observation 836c0b76-eeb2-4ac8-8076-2ae655001634 · outbound

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Massart, P

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:43.109668Z

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Observation f975a99a-dee8-4548-9e66-394fcce7a653 · outbound

This paper cites Demystifying disagreement-on-the-line in high dimensions.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Demystifying disagreement-on-the-line in high dimensions

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:43.096942Z

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Observation 4a46ccb1-c848-4c3b-ae50-394da2df9736 · outbound

This paper cites Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit

Reference 71

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Observation c7f7b1aa-9db5-41af-b91c-70601291b98c · outbound

This paper cites S., Tajwar, F., Kumar, A., Yao, H., Liang, P., and Finn, C.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning S., Tajwar, F., Kumar, A., Yao, H., Liang, P., and Finn, C

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:43.084316Z

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Observation 367733f4-e4ea-4cee-bbcc-fb3d3563caa0 · outbound

This paper cites and Dobriban, E.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Dobriban, E

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:43.070732Z

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Observation b12ff897-c3ca-4236-9049-ae560dbd4a27 · outbound

This paper cites E., and Makhzani, A.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning E., and Makhzani, A

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:43.057485Z

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Observation 388f6a38-5390-42c8-9c6d-1631db713a18 · outbound

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Unresolved cited work

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-09T14:47:40.632426Z

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This paper cites Deep learning via Hessian -free optimization.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Deep learning via Hessian -free optimization

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:43.037180Z

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Observation a8a14744-97ca-4df6-982b-504df2350ef7 · outbound

This paper cites New insights and perspectives on the natural gradient method.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning New insights and perspectives on the natural gradient method

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-09T14:47:40.640176Z

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Observation d26dd43c-c00c-44eb-99e2-7541db46b928 · outbound

This paper cites and Grosse, R.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Grosse, R

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:43.016984Z

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Observation ea6facc4-7413-47de-9477-bb75f5272864 · outbound

This paper cites The benefit of multitask representation learning.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning The benefit of multitask representation learning

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:43.004539Z

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Observation ca064cd8-24a5-4eff-a8e5-82afb1ec2bed · outbound

This paper cites and Montanari, A.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Montanari, A

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-09T14:47:40.651377Z

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Observation 72c85b7c-06ff-4720-8cb5-7bc8c702ff7c · outbound

This paper cites Announcing the results of the inaugural AlgoPerf : Training algorithms benchmark competition, 2024.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Announcing the results of the inaugural AlgoPerf : Training algorithms benchmark competition, 2024

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:42.984276Z

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Observation dfc3bb58-f64e-4136-8191-d2ceed24bcf8 · outbound

This paper cites Asymptotics of Linear Regression with Linearly Dependent Data.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Asymptotics of Linear Regression with Linearly Dependent Data

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-09T14:47:40.658970Z

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Observation 49b4fddf-ba51-4679-b613-98ca54407e0b · outbound

This paper cites Signal-Plus-Noise Decomposition of Nonlinear Spiked Random Matrix Models.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Signal-Plus-Noise Decomposition of Nonlinear Spiked Random Matrix Models

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-09T14:47:40.662641Z

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This paper cites A theory of non-linear feature learning with one gradient step in two-layer neural networks.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning A theory of non-linear feature learning with one gradient step in two-layer neural networks

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:42.971707Z

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Observation cd2205b1-aa50-48c0-b8f3-5eb254c2b041 · outbound

This paper cites The generalization error of max-margin linear classifiers: Benign overfitting and high dimensional asymptotics in the overparametrized regime.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning The generalization error of max-margin linear classifiers: Benign overfitting and high dimensional asymptotics in the overparametrized regime

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-09T14:47:40.669988Z

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Observation c3a67bcd-ac56-446b-b883-d372f0cb6099 · outbound

This paper cites A New Perspective on Shampoo's Preconditioner.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning A New Perspective on Shampoo's Preconditioner

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-09T14:47:40.673899Z

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Observation 5c6387ee-d047-4a34-8b20-226e3634757a · outbound

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Unresolved cited work

Reference 87

Resolution: unresolved; raw_fallback, observed 2026-08-09T14:47:42.959520Z

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Observation cd8868c1-1a99-41ee-9b96-5036ab4b028c · outbound

This paper cites The Effects of Multi-Task Learning on ReLU Neural Network Functions.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning The Effects of Multi-Task Learning on ReLU Neural Network Functions

Reference 88

Resolution: verified exact; local_arxiv, observed 2026-08-09T14:47:42.212659Z

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Observation e8b91276-6fb6-4dcf-b223-3a0ebf4c5970 · outbound

This paper cites and Vaswani, N.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Vaswani, N

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:42.947872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 231319a4-4634-44bb-b4b2-d86129a81411 · outbound

This paper cites an unresolved cited work.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Unresolved cited work

Reference 90

Resolution: unresolved; raw_fallback, observed 2026-08-09T14:47:42.935618Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 842238ef-a75e-4a88-8da8-3d2c4826019e · outbound

This paper cites an unresolved cited work.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Unresolved cited work

Reference 91

Resolution: unresolved; raw_fallback, observed 2026-08-09T14:47:42.922094Z

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Observation 59412849-2ae1-4f39-9488-9994150bfc71 · outbound

This paper cites and Wright, S.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Wright, S

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-09T14:47:40.696612Z

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Observation c8c6eed3-3c23-4b61-98ce-4e57ab071899 · outbound

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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Unresolved cited work

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-09T14:47:40.700012Z

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Observation cb2ddf47-a413-4315-88d7-fc3902713327 · outbound

This paper cites and Recht, B.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Recht, B

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:42.896569Z

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Observation d88406f9-136f-404b-a8b6-3ccc80639d69 · outbound

This paper cites J., Kale, S., and Kumar, S.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning J., Kale, S., and Kumar, S

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:42.884738Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation af7d01e1-2641-4f1c-a187-18c9bc667570 · outbound

This paper cites and Vershynin, R.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning and Vershynin, R

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:42.872327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fd2c4366-ab72-4c83-92f8-fe022a0627a5 · outbound

This paper cites M., Schneider, F., and Hennig, P.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning M., Schneider, F., and Hennig, P

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:42.861461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6e58c8ab-04e5-466d-9273-3cf1e012a7ed · outbound

This paper cites an unresolved cited work.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Unresolved cited work

Reference 98

Resolution: unresolved; raw_fallback, observed 2026-08-09T14:47:42.850858Z

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Observation 5ce83e7f-3c9c-4240-9c0a-ed5c29df1bf1 · outbound

This paper cites A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-09T14:47:40.724257Z

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Observation c59315d4-4c51-4e1b-a509-6f4a23834c4f · outbound

This paper cites A theoretical analysis on feature learning in neural networks: Emergence from inputs and advantage over fixed features.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning A theoretical analysis on feature learning in neural networks: Emergence from inputs and advantage over fixed features

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:47:42.838365Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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