Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel | Paper Citation Record

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This paper cites B., and Misiakiewicz, T.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel B., and Misiakiewicz, T

Reference 1

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This paper cites A convergence theory for deep learning via over- parameterization.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel A convergence theory for deep learning via over- parameterization

Reference 2

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This paper cites Thinking outside the ball: Optimal learning with gradient descent for generalized linear stochastic convex optimization.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Thinking outside the ball: Optimal learning with gradient descent for generalized linear stochastic convex optimization

Reference 3

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This paper cites Stronger generalization bounds for deep nets via a compression approach.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Stronger generalization bounds for deep nets via a compression approach

Reference 4

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This paper cites Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks

Reference 5

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This paper cites S., Hu, W., Li, Z., Salakhutdinov, R.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel S., Hu, W., Li, Z., Salakhutdinov, R

Reference 6

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This paper cites B., Gheissari, R., and Jagannath, A.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel B., Gheissari, R., and Jagannath, A

Reference 7

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This paper cites On the Rademacher Complexity of Linear Hypothesis Sets.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel On the Rademacher Complexity of Linear Hypothesis Sets

Reference 8

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

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel A., Suzuki, T., Wang, Z., Wu, D., and Yang, G

Reference 9

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This paper cites L., Foster, D.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel L., Foster, D

Reference 11

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This paper cites L., Harvey, N., Liaw, C., and Mehrabian, A.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel L., Harvey, N., Liaw, C., and Mehrabian, A

Reference 12

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This paper cites Stability of stochastic gradient descent on nonsmooth convex losses.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Stability of stochastic gradient descent on nonsmooth convex losses

Reference 13

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This paper cites Reconciling modern machine-learning practice and the classical bias–variance trade-off.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Reconciling modern machine-learning practice and the classical bias–variance trade-off

Reference 14

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This paper cites and Elisseeff, A.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel and Elisseeff, A

Reference 16

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This paper cites and Gu, Q.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel and Gu, Q

Reference 17

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This paper cites and Papailiopoulos, D.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel and Papailiopoulos, D

Reference 18

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This paper cites Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective

Reference 19

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This paper cites M., and Weng, T.-W.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel M., and Weng, T.-W

Reference 20

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This paper cites and Bach, F.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel and Bach, F

Reference 21

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This paper cites Neural networks can learn representations with gradient descent.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Neural networks can learn representations with gradient descent

Reference 22

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Reference 23

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This paper cites Gradient descent finds global minima of deep neural networks.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Gradient descent finds global minima of deep neural networks

Reference 24

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This paper cites Gradient Descent Provably Optimizes Over-parameterized Neural Networks.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Gradient Descent Provably Optimizes Over-parameterized Neural Networks

Reference 25

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This paper cites Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data

Reference 26

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This paper cites S., and Bartlett, P.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel S., and Bartlett, P

Reference 27

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This paper cites D., Soudry, D., and Srebro, N.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel D., Soudry, D., and Srebro, N

Reference 28

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This paper cites Train faster, generalize better: Stability of stochastic gradient descent.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Train faster, generalize better: Stability of stochastic gradient descent

Reference 29

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Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Information-Theoretic Generalization Bounds for Deep Neural Networks

Reference 30

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This paper cites Deep residual learning for image recognition.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Deep residual learning for image recognition

Reference 31

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Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Neural tangent kernel: Convergence and generalization in neural networks

Reference 32

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This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 33

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Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Suprema of chaos processes and the restricted isometry property

Reference 34

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This paper cites Learning multiple layers of features from tiny images.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Learning multiple layers of features from tiny images

Reference 35

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This paper cites On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning

Reference 36

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Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel ridgeless

Reference 37

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This paper cites On the multiple descent of minimum-norm interpolants and restricted lower isometry of kernels.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel On the multiple descent of minimum-norm interpolants and restricted lower isometry of kernels

Reference 38

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This paper cites A Note on the PAC Bayesian Theorem.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel A Note on the PAC Bayesian Theorem

Reference 39

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Reference 40

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Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Foundations of machine learning

Reference 41

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Reference 42

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Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel and Urbani, P

Reference 43

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Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel and Zhong, Y

Reference 44

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This paper cites Generalization bounds of sgld for non-convex learning: Two theoretical viewpoints.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Generalization bounds of sgld for non-convex learning: Two theoretical viewpoints

Reference 45

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This paper cites K., Khisti, A., and Roy, D.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel K., Khisti, A., and Roy, D

Reference 46

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Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel K., Haghifam, M., and Roy, D

Reference 47

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This paper cites In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning

Reference 48

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This paper cites Norm-based capacity control in neural networks.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Norm-based capacity control in neural networks

Reference 49

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This paper cites Exploring generalization in deep learning.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Exploring generalization in deep learning

Reference 50

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This paper cites A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks

Reference 51

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This paper cites The role of over- parametrization in generalization of neural networks.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel The role of over- parametrization in generalization of neural networks

Reference 52

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Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Unresolved cited work

Reference 53

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Observation f3407976-de60-4078-a9f9-d9b08a0d3eef · outbound

This paper cites Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD

Reference 54

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Observation 73d19ffc-db37-4ddf-9117-ef1567de21cc · outbound

This paper cites Generalization guarantees for neural architecture search with train-validation split.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Generalization guarantees for neural architecture search with train-validation split

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:45.775299Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 122f9b08-5e91-42e5-bc7f-206c9fb5968a · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Pytorch: An imperative style, high-performance deep learning library

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T04:21:45.378107Z

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Observation 3cdca47c-dbe0-4e9f-b9eb-5b755d2382cc · outbound

This paper cites Generalization error bounds for noisy, iterative algorithms.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Generalization error bounds for noisy, iterative algorithms

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:45.758393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8ef0bf33-015f-4a95-9f6f-bf8965b49558 · outbound

This paper cites and Zou, J.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel and Zou, J

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:45.747157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 45d8ade6-d35c-4da9-befe-1d5d132a45ef · outbound

This paper cites How do infinite width bounded norm networks look in function space? In Conference on Learning Theory, pp.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel How do infinite width bounded norm networks look in function space? In Conference on Learning Theory, pp

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:45.737072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0db4d8ca-7764-42b0-8ab1-0d3cf7f1a157 · outbound

This paper cites S., Gunasekar, S., and Srebro, N.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel S., Gunasekar, S., and Srebro, N

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:45.726544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 30afc771-6e87-4fb0-9f74-5b2557b7d5f9 · outbound

This paper cites and Christmann, A.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel and Christmann, A

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:45.716420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ec1c99a2-fd27-4749-a6f5-6840edca0834 · outbound

This paper cites and Chervonenkis, A.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel and Chervonenkis, A

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:45.706620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 70721486-663f-42dd-b3c2-4b93cb9a63d3 · outbound

This paper cites High-dimensional probability: An introduction with applications in data science, volume 47.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel High-dimensional probability: An introduction with applications in data science, volume 47

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T04:21:45.398921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0f5326a-3526-4ef8-9856-3705868674ca · outbound

This paper cites an unresolved cited work.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:21:45.687433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cbb6de04-dd05-4b20-8a24-ab1b7616ea95 · outbound

This paper cites and Zhu, Y.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel and Zhu, Y

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:45.675261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 31ac1225-c2bc-4f3c-8bd1-066ce7d757c3 · outbound

This paper cites and Raginsky, M.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel and Raginsky, M

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:45.665453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bdf0b6be-c120-443b-81b5-8abc08591ead · outbound

This paper cites nX i=1 KT (zi, zi; S) # + L2T + L2βT 2 r 2n log 2 δ′ ! + δ′nL2T Take δ′ = 1 n, ln E S e Pn i=1 KT (zi,zi;S) ≤ E S.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel nX i=1 KT (zi, zi; S) # + L2T + L2βT 2 r 2n log 2 δ′ ! + δ′nL2T Take δ′ = 1 n, ln E S e Pn i=1 KT (zi,zi;S) ≤ E S

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:45.655197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b2fdb6d2-51cf-45d4-ab5a-80ba48624bb6 · outbound

This paper cites Theorem G.2 (Theorem 6.1 in Bietti et al.

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Theorem G.2 (Theorem 6.1 in Bietti et al

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:45.642485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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