Paper Citation Record · LEDGER

Exploring the Generalization Capabilities of AID-based Bi-level Optimization

As of 14 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2411.16081.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.16081 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:40:10.137269Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T01:23:58.565480Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T13:36:08.407242Z

Reference resolution

49 of 49 outbound references displayed

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External citation measurements

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Outbound references

Observation 810dde3c-1958-48a8-ac88-2c2ecc8363d3 · outbound

This paper cites Stability and generalization of bilevel programming in hyperpa- rameter optimization,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Stability and generalization of bilevel programming in hyperpa- rameter optimization,

Reference 1

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Observation 4e2dd1f5-2d18-4b86-8276-9ec6caa965e7 · outbound

This paper cites Gradient-based optimization of hyperparam- eters,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Gradient-based optimization of hyperparam- eters,

Reference 2

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Observation 30264943-f9e4-4f6c-a5d8-c02bf1c4ef35 · outbound

This paper cites Forward and reverse gradient-based hyperparameter optimization,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Forward and reverse gradient-based hyperparameter optimization,

Reference 3

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Observation ecf9987a-6fd1-40c3-8a28-93f0728bbc1e · outbound

This paper cites Bilevel programming for hyperparameter optimization and meta-learning,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Bilevel programming for hyperparameter optimization and meta-learning,

Reference 4

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Observation a657c160-aab2-4b40-8b0e-6659683a1e4e · outbound

This paper cites Optimizing millions of hyperparameters by implicit differentiation,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Optimizing millions of hyperparameters by implicit differentiation,

Reference 5

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Observation 5e4c1f7c-a879-4320-aae9-096d9a238781 · outbound

This paper cites On the iteration complexity of hypergradient computation,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization On the iteration complexity of hypergradient computation,

Reference 6

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Observation 7ca1a3a5-ca89-49d9-8bf6-1ff0b10efe9f · outbound

This paper cites Implicit differentiation of lasso-type models for hyperparameter optimization,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Implicit differentiation of lasso-type models for hyperparameter optimization,

Reference 7

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Observation e255f097-eb38-4b19-892e-19acbdd22198 · outbound

This paper cites Meta-learning with differentiable closed-form solvers.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Meta-learning with differentiable closed-form solvers

Reference 8

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Observation fd1e4d62-a83a-411b-9331-544bd75f81fb · outbound

This paper cites Convergence of meta-learning with task-specific adaptation over partial parameters,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Convergence of meta-learning with task-specific adaptation over partial parameters,

Reference 9

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Observation decec390-9073-41ac-858d-9fc4869aa832 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Model-agnostic meta-learning for fast adaptation of deep networks,

Reference 10

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Observation fce679c0-fb63-4768-8649-1c0c1b970207 · outbound

This paper cites Meta-learning with implicit gradients,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Meta-learning with implicit gradients,

Reference 11

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Observation 994e5c15-1522-4697-b6da-4b1e857b3457 · outbound

This paper cites Autoaugment: Learning augmentation strate- gies from data,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Autoaugment: Learning augmentation strate- gies from data,

Reference 12

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Observation 547c7943-9cdd-4976-b83d-b537e8ea6c84 · outbound

This paper cites CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals

Reference 13

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This paper cites DARTS: Differentiable Architecture Search.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization DARTS: Differentiable Architecture Search

Reference 14

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Observation 2a049a47-4f0a-4e61-b2b7-752722ecfb83 · outbound

This paper cites Deep bilevel learning,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Deep bilevel learning,

Reference 15

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Observation f19ebfd0-57a2-4169-a553-8d11ae0c503f · outbound

This paper cites Automatic design of cnns via differentiable neural architecture search for polsar image classification,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Automatic design of cnns via differentiable neural architecture search for polsar image classification,

Reference 16

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Observation 1e72b47e-c42e-47f4-858c-f5a2261f694d · outbound

This paper cites Advancing model pruning via bi-level optimization,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Advancing model pruning via bi-level optimization,

Reference 17

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Observation 07c744ff-f983-4b34-9d35-856c5f93b288 · outbound

This paper cites Anti-makeup: Learning a bi-level adversarial network for makeup- invariant face verification,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Anti-makeup: Learning a bi-level adversarial network for makeup- invariant face verification,

Reference 18

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Observation 2c0ecfb0-962a-4de2-a9ba-6bc87779ebf6 · outbound

This paper cites Connecting Generative Adversarial Networks and Actor-Critic Methods.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Connecting Generative Adversarial Networks and Actor-Critic Methods

Reference 19

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This paper cites On the global optimality of model-agnostic meta-learning,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization On the global optimality of model-agnostic meta-learning,

Reference 20

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This paper cites A two- timescale stochastic algorithm framework for bilevel op- timization: Complexity analysis and application to actor- critic,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization A two- timescale stochastic algorithm framework for bilevel op- timization: Complexity analysis and application to actor- critic,

Reference 21

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Observation c7f56323-0ca7-4093-9518-eb5edc116808 · outbound

This paper cites Randomized stochastic variance- reduced methods for stochastic bilevel optimization,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Randomized stochastic variance- reduced methods for stochastic bilevel optimization,

Reference 22

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Observation f06ff210-3656-4c22-bae3-97c08a6e66c4 · outbound

This paper cites Approximation Methods for Bilevel Programming.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Approximation Methods for Bilevel Programming

Reference 23

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This paper cites A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic

Reference 24

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This paper cites Closing the gap: Tighter analysis of alternating stochastic gradient methods for bilevel problems,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Closing the gap: Tighter analysis of alternating stochastic gradient methods for bilevel problems,

Reference 25

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This paper cites Provably faster algorithms for bilevel optimization,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Provably faster algorithms for bilevel optimization,

Reference 26

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Observation 1541c1eb-d14d-48c8-bc55-3973d4a72141 · outbound

This paper cites A Single-Timescale Method for Stochastic Bilevel Optimization.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization A Single-Timescale Method for Stochastic Bilevel Optimization

Reference 27

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This paper cites Bilevel optimization: Con- vergence analysis and enhanced design,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Bilevel optimization: Con- vergence analysis and enhanced design,

Reference 28

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Observation 0e178f38-b1ae-4b92-894b-6e213398ffbd · outbound

This paper cites A framework for bilevel optimization that enables stochas- tic and global variance reduction algorithms,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization A framework for bilevel optimization that enables stochas- tic and global variance reduction algorithms,

Reference 29

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Observation f69c0baf-7359-4937-bc17-8beb69bfe1bb · outbound

This paper cites Amortized Implicit Differentiation for Stochastic Bilevel Optimization.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Amortized Implicit Differentiation for Stochastic Bilevel Optimization

Reference 30

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Exploring the Generalization Capabilities of AID-based Bi-level Optimization Projection-free stochastic bi-level optimization,

Reference 31

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Observation 56b0d415-b81a-45a1-b995-f97f43ac7c2a · outbound

This paper cites Fednest: Federated bilevel, minimax, and com- positional optimization,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Fednest: Federated bilevel, minimax, and com- positional optimization,

Reference 32

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This paper cites Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity

Reference 33

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Exploring the Generalization Capabilities of AID-based Bi-level Optimization Stability and generaliza- tion,

Reference 34

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Exploring the Generalization Capabilities of AID-based Bi-level Optimization Stability of randomized learning algorithms

Reference 35

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This paper cites Train faster, gener- alize better: Stability of stochastic gradient descent,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Train faster, gener- alize better: Stability of stochastic gradient descent,

Reference 36

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Exploring the Generalization Capabilities of AID-based Bi-level Optimization Stability and Convergence Trade-off of Iterative Optimization Algorithms

Reference 37

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This paper cites What is a Good Metric to Study Generalization of Minimax Learners?.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization What is a Good Metric to Study Generalization of Minimax Learners?

Reference 38

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Exploring the Generalization Capabilities of AID-based Bi-level Optimization Stability Analysis and Generalization Bounds of Adversarial Training

Reference 39

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Exploring the Generalization Capabilities of AID-based Bi-level Optimization Stability and Generalization of the Decentralized Stochastic Gradient Descent Ascent Algorithm

Reference 40

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Exploring the Generalization Capabilities of AID-based Bi-level Optimization Stability and generalization of decentralized stochastic gradient descent,

Reference 41

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Exploring the Generalization Capabilities of AID-based Bi-level Optimization Topology-aware generalization of decentralized sgd,

Reference 42

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Observation de882bdb-84d6-43d3-8206-eef98f7cb6cf · outbound

This paper cites A closer look at the training strategy for modern meta-learning,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization A closer look at the training strategy for modern meta-learning,

Reference 43

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Observation e5dbc439-2d3b-41e8-acb6-462fc9c66180 · outbound

This paper cites Generalization of model-agnostic meta-learning algorithms: Recurring and unseen tasks,.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Generalization of model-agnostic meta-learning algorithms: Recurring and unseen tasks,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:40:10.393466Z

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Observation 02d3c9c8-9d51-4040-b41c-4f8fb2758408 · outbound

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Exploring the Generalization Capabilities of AID-based Bi-level Optimization Will Bilevel Optimizers Benefit from Loops

Reference 45

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Observation dfb13727-e791-4f85-a574-1c71b4a221fb · outbound

This paper cites Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions

Reference 46

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Observation 318d8425-bb6d-47ac-9e20-c1552e7b64c7 · outbound

This paper cites The mnist database of handwritten digit im- ages for machine learning research [best of the web],.

Exploring the Generalization Capabilities of AID-based Bi-level Optimization The mnist database of handwritten digit im- ages for machine learning research [best of the web],

Reference 47

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Exploring the Generalization Capabilities of AID-based Bi-level Optimization Gradient-based learning applied to document recogni- tion,

Reference 48

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Exploring the Generalization Capabilities of AID-based Bi-level Optimization Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 49

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Pith citing papers

Observation 911f881c-7f83-4280-8387-e33a3a2b9bda · inbound

On the Stability and Generalization of First-order Bilevel Minimax Optimization cites this paper.

On the Stability and Generalization of First-order Bilevel Minimax Optimization Exploring the Generalization Capabilities of AID-based Bi-level Optimization

Reference 31

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