Paper Citation Record · LEDGER

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models

As of 8 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 3 inbound Pith citation observations for arXiv:2506.05500.

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

pith.paper-citation-record.v1
2506.05500 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:32:00.346155Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T23:35:23.879187Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-04T23:35:28.240868Z

Reference resolution

59 of 59 outbound references displayed

verified exact1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eec43b36-4820-4efa-9d04-407c345d2f11 · outbound

This paper cites The merged-staircase property: a necessary and nearly sufficient condition for sgd learning of sparse functions on two-layer neural networks.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models The merged-staircase property: a necessary and nearly sufficient condition for sgd learning of sparse functions on two-layer neural networks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:01.002797Z

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Observation 257976cf-7d17-4436-ac13-48fe899219de · outbound

This paper cites Sgd learning on neural networks: leap complexity and saddle-to-saddle dynamics.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Sgd learning on neural networks: leap complexity and saddle-to-saddle dynamics

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.995061Z

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Observation 84b87fea-f866-4788-b652-15c8d20bd32e · outbound

This paper cites The staircase property: How hierarchical structure can guide deep learning.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models The staircase property: How hierarchical structure can guide deep learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.987145Z

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Observation 86c0a450-f32f-4a29-8499-cf181b2d5c85 · outbound

This paper cites Repetita iuvant: Data repetition allows sgd to learn high-dimensional multi-index functions, 2024.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Repetita iuvant: Data repetition allows sgd to learn high-dimensional multi-index functions, 2024

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.979228Z

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Observation 3609ea4f-db77-47db-bfd8-0b12e496d5d9 · outbound

This paper cites Online stochastic gradient descent on non-convex losses from high-dimensional inference.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Online stochastic gradient descent on non-convex losses from high-dimensional inference

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.971677Z

Source-reported events for the cited work

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Observation 57297d56-37aa-4b92-bffa-dbff2155a784 · outbound

This paper cites Stochastic gradient descent in high dimensions for multi-spiked tensor pca, 2024.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Stochastic gradient descent in high dimensions for multi-spiked tensor pca, 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.963874Z

Source-reported events for the cited work

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Observation 88df70a5-556d-42d2-95d2-3a2b10696291 · outbound

This paper cites Online stochastic gradient descent on non-convex losses from high-dimensional inference.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Online stochastic gradient descent on non-convex losses from high-dimensional inference

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.956100Z

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Observation c99a00eb-242d-4941-9c83-e5e92e7fce63 · outbound

This paper cites Statistical query algorithms and low-degree tests are almost equivalent.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Statistical query algorithms and low-degree tests are almost equivalent

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.948237Z

Source-reported events for the cited work

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Observation bca692ca-2517-42a5-b4cc-707dec7309d8 · outbound

This paper cites On learning gaussian multi-index models with gradient flow.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models On learning gaussian multi-index models with gradient flow

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.940359Z

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Observation 201ebe87-ec25-48b9-893d-b2e7cc7ef58b · outbound

This paper cites Learning single-index models with shallow neural networks.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Learning single-index models with shallow neural networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.932864Z

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Observation 1d0475b4-2f0b-46f5-9128-80a748d53ba0 · outbound

This paper cites Bandeira, March T.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Bandeira, March T

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.924906Z

Source-reported events for the cited work

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Observation 3b187f0c-4568-4347-835e-12f6dc3d37de · outbound

This paper cites The franz-parisi criterion and computational trade-offs in high dimensional statistics.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models The franz-parisi criterion and computational trade-offs in high dimensional statistics

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.917458Z

Source-reported events for the cited work

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Observation a7f9ffab-57fd-42bd-9aff-088f1a92a856 · outbound

This paper cites High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation

Reference 13

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Observation 27d09cad-76a7-47d8-b749-ab992d15e743 · outbound

This paper cites Survey on Algorithms for multi-index models.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Survey on Algorithms for multi-index models

Reference 14

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Observation 6f42b07d-3485-4f25-a001-a81b648ef3fd · outbound

This paper cites Universality and sharp matrix concentration inequalities.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Universality and sharp matrix concentration inequalities

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.909809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 23bff638-7d78-4c98-8d63-fa4f57e17111 · outbound

This paper cites Towards understanding hierarchical learning: Benefits of neural representations.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Towards understanding hierarchical learning: Benefits of neural representations

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.902365Z

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Observation 5989068b-b910-45c6-8e38-6e0de9056971 · outbound

This paper cites Learning narrow one-hidden-layer relu networks.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Learning narrow one-hidden-layer relu networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.895030Z

Source-reported events for the cited work

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Observation 33e970e5-f984-4d67-b618-e0d0ba8aaefb · outbound

This paper cites Learning deep relu networks is fixed-parameter tractable.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Learning deep relu networks is fixed-parameter tractable

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.887272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d6271a0b-96c4-4dfd-a7ce-b6645372b8d5 · outbound

This paper cites Dimension reduction for conditional mean in regression.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Dimension reduction for conditional mean in regression

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.879560Z

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Observation 504eeb40-4838-4f38-8991-fc2201a74d16 · outbound

This paper cites Learning polynomials in few relevant dimensions.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Learning polynomials in few relevant dimensions

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.872566Z

Source-reported events for the cited work

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Observation 2ce5ca44-d344-47fe-8c8a-30be026b2198 · outbound

This paper cites Dennis Cook.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Dennis Cook

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.864988Z

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Observation 03dff563-4ab1-4999-b226-a7bf2501dc57 · outbound

This paper cites Optimal Spectral Transitions in High-Dimensional Multi-Index Models.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Optimal Spectral Transitions in High-Dimensional Multi-Index Models

Reference 22

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Observation e9d0ca6e-4164-48cd-ae36-5913ada8a864 · outbound

This paper cites Learning single-index models in gaussian space.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Learning single-index models in gaussian space

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.858247Z

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Observation f9e330ca-1bbf-450c-be02-d2afdd4730a3 · outbound

This paper cites Statistical query lower bounds for tensor pca.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Statistical query lower bounds for tensor pca

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.850672Z

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Observation 233c4883-cb12-4d23-957f-0b6030336e66 · outbound

This paper cites Statistical-computational trade-offs in tensor PCA and related problems via communication complexity.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Statistical-computational trade-offs in tensor PCA and related problems via communication complexity

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.843110Z

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Observation 8f925cf8-b735-4551-8a26-cb1b7f03ecce · outbound

This paper cites Kane, and Lisheng Ren.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Kane, and Lisheng Ren

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.835104Z

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Observation 1415d91e-cad6-4f78-8e11-50729745ec5d · outbound

This paper cites Kane, and Nikos Zarifis.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Kane, and Nikos Zarifis

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.827749Z

Source-reported events for the cited work

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Observation abf464eb-3ab4-4a6a-894a-28225a1b6189 · outbound

This paper cites Efficiently learning one-hidden-layer relu networks via schur polynomials.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Efficiently learning one-hidden-layer relu networks via schur polynomials

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.819992Z

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Observation 63b5b56e-517e-40da-b966-4d4baa2021ae · outbound

This paper cites How two-layer neural networks learn, one (giant) step at a time.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models How two-layer neural networks learn, one (giant) step at a time

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.812405Z

Source-reported events for the cited work

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Observation 0d85ef35-82b6-4b48-a555-ff258cb5430a · outbound

This paper cites Kane, and Alistair Stewart.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Kane, and Alistair Stewart

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.804091Z

Source-reported events for the cited work

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Observation 5e5660d5-c2c5-44bf-93c5-bb1bda3b84d5 · outbound

This paper cites de la Pe \ n a and Evarist Gin \'e.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models de la Pe \ n a and Evarist Gin \'e

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.796305Z

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Observation d85d7806-5b29-4084-867f-64c6444fd538 · outbound

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

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Neural networks can learn representations with gradient descent

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.787569Z

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Observation 400b9ef6-9c70-4f0f-8ff6-31cbb1dd5e43 · outbound

This paper cites Online Learning of Neural Networks.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Online Learning of Neural Networks

Reference 33

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Observation b54ab9b3-b9c9-4a24-a8b3-b395342fc9ca · outbound

This paper cites Smoothing the landscape boosts the signal for sgd: Optimal sample complexity for learning single index models.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Smoothing the landscape boosts the signal for sgd: Optimal sample complexity for learning single index models

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.779861Z

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Observation d2e146b9-81ec-4ec5-8b46-edb9d28b9aa4 · outbound

This paper cites Computational-statistical gaps in gaussian single-index models.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Computational-statistical gaps in gaussian single-index models

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.771943Z

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Observation 7a4afc0f-35aa-452a-ab00-94b011c61dda · outbound

This paper cites The benefits of reusing batches for gradient descent in two-layer networks: breaking the curse of information and leap exponents.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models The benefits of reusing batches for gradient descent in two-layer networks: breaking the curse of information and leap exponents

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.764646Z

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Observation ee1d80cc-9761-4933-80ec-dc9b9717cd4b · outbound

This paper cites Structure adaptive approach for dimension reduction.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Structure adaptive approach for dimension reduction

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.756049Z

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Observation 08163a78-bbae-4164-bca7-7ff7e4727703 · outbound

This paper cites The power of sum-of-squares for detecting hidden structures.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models The power of sum-of-squares for detecting hidden structures

Reference 38

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Observation 0082ebdd-63c4-4d9e-b7da-56b55fcaa0f5 · outbound

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The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Statistical inference and the sum of squares method

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.742665Z

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Observation 84f1a3a9-af5d-4b84-a00e-dabcc419b21a · outbound

This paper cites Tensor principal component analysis via sum-of-square proofs.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Tensor principal component analysis via sum-of-square proofs

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.734553Z

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Observation 119a0d30-e85e-4791-b0c1-2fb6e359055b · outbound

This paper cites On the complexity of learning sparse functions with statistical and gradient queries.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models On the complexity of learning sparse functions with statistical and gradient queries

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.726785Z

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Observation a99e5686-e035-49fa-a02b-a81445b392eb · outbound

This paper cites Learning geometric concepts via gaussian surface area.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Learning geometric concepts via gaussian surface area

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.719142Z

Source-reported events for the cited work

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Observation 07169a0a-d3ef-4ad8-b039-036057db024f · outbound

This paper cites Learning intersections of halfspaces with distribution shift: Improved algorithms and sq lower bounds.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Learning intersections of halfspaces with distribution shift: Improved algorithms and sq lower bounds

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.711005Z

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Observation a2ae5aa5-b462-460e-a0d1-8e6290538250 · outbound

This paper cites Notes on Computational Hardness of Hypothesis Testing: Predictions using the Low-Degree Likelihood Ratio.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Notes on Computational Hardness of Hypothesis Testing: Predictions using the Low-Degree Likelihood Ratio

Reference 44

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Observation 930647d0-faf3-4a32-b813-ef21ea8f7ac9 · outbound

This paper cites Spectral Estimators for Multi-Index Models: Precise Asymptotics and Optimal Weak Recovery.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Spectral Estimators for Multi-Index Models: Precise Asymptotics and Optimal Weak Recovery

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:00.311902Z

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Observation f0c2be20-ebae-494d-b9f0-5a5e538673e1 · outbound

This paper cites Sliced inverse regression for dimension reduction.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Sliced inverse regression for dimension reduction

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:00.314452Z

Source-reported events for the cited work

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Observation a61fc7bc-5652-43cd-9faf-e7a8dd541e2c · outbound

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

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Neural network learns low-dimensional polynomials with sgd near the information-theoretic limit

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.697707Z

Source-reported events for the cited work

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Observation 86b53d4f-e51f-4a24-9b31-477378159798 · outbound

This paper cites The Zero Set of a Real Analytic Function.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models The Zero Set of a Real Analytic Function

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:00.319028Z

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Observation 21388ebb-5932-40b8-85d3-068c92ab4edf · outbound

This paper cites Learning juntas.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Learning juntas

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.690372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6fb7510c-3d7a-480a-9fcc-493105184215 · outbound

This paper cites A statistical model for tensor pca.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models A statistical model for tensor pca

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.682934Z

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This paper cites Learning orthogonal multi-index models: A fine-grained information exponent analysis.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Learning orthogonal multi-index models: A fine-grained information exponent analysis

Reference 51

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Observation 606d6f5e-4a2a-4d79-a54b-c9b2a5751032 · outbound

This paper cites Emergence and scaling laws in sgd learning of shallow neural networks.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Emergence and scaling laws in sgd learning of shallow neural networks

Reference 52

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Observation 223a1637-072f-43a7-8c54-3027862d8a82 · outbound

This paper cites Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Sch \" o lkopf, and Gert R.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Sch \" o lkopf, and Gert R

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.675831Z

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Observation 1ffdd41f-b9ce-43ec-8108-6b9767cf7dee · outbound

This paper cites Fundamental limits of weak learnability in high-dimensional multi-index models, 2024.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Fundamental limits of weak learnability in high-dimensional multi-index models, 2024

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.667371Z

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Observation 3a7f9810-6a22-41f6-a1ed-129218466098 · outbound

This paper cites Learning convex concepts from gaussian distributions with pca.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Learning convex concepts from gaussian distributions with pca

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.658193Z

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Observation 5f543766-1e69-4ef1-bb20-23e0e2353bfe · outbound

This paper cites Vempala and Ying Xiao.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Vempala and Ying Xiao

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.650082Z

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Observation 94b8ee27-65ca-45a2-9618-f7d206e6ae89 · outbound

This paper cites A multiple-index model and dimension reduction.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models A multiple-index model and dimension reduction

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.641718Z

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Observation 17dc52b7-d696-4108-998f-ddf0ca1040d8 · outbound

This paper cites An adaptive estimation of dimension reduction space.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models An adaptive estimation of dimension reduction space

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.633412Z

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Observation 1a703740-8f24-475a-b3fc-6bfe48f01856 · outbound

This paper cites Interpolating convex and non-convex tensor decompositions via the subspace norm.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models Interpolating convex and non-convex tensor decompositions via the subspace norm

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:00.625696Z

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Pith citing papers

Observation 24064e4f-84f0-4154-999e-cc4a6c8349dc · inbound

Limitations of SGD for Multi-Index Models Beyond Statistical Queries cites this paper.

Limitations of SGD for Multi-Index Models Beyond Statistical Queries The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models

Reference 19

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Observation 11139e03-0d24-46c7-b45c-3d606d79053e · inbound

The Multiscale Single-Index Model: A Stylized Model for Hierarchical Feature Learning cites this paper.

The Multiscale Single-Index Model: A Stylized Model for Hierarchical Feature Learning The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models

Reference 26

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Observation a910d7c5-3557-4041-b76d-b910404b7846 · inbound

Approximate Message Passing with Random Initialization for Phase Retrieval cites this paper.

Approximate Message Passing with Random Initialization for Phase Retrieval The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models

Reference 24

Resolution: metadata mismatch; local_arxiv, observed 2026-08-04T23:35:28.248246Z

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