Paper Citation Record · LEDGER

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions

As of 21 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 4 inbound Pith citation observations for arXiv:2502.06443.

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

pith.paper-citation-record.v1
2502.06443 v2

Coverage vector

measured 53 of 53 reference resolution

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measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T05:31:24.127049Z

Reference resolution

53 of 53 outbound references displayed

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External citation measurements

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Outbound references

Observation 3413796e-a243-4d74-b9db-a1c7de98cf60 · outbound

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

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics

Reference 1

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Observation 54a40a25-9e73-4387-8654-8b7482ae7cf0 · 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.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions The merged-staircase property: a necessary and nearly sufficient condition for sgd learning of sparse functions on two-layer neural networks

Reference 2

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This paper cites Provable advantage of curriculum learning on parity targets with mixed inputs.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Provable advantage of curriculum learning on parity targets with mixed inputs

Reference 3

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Observation d891adcf-4063-4d6b-beea-bcf753a75f3a · outbound

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

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions

Reference 4

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This paper cites On the universality of deep learning.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions On the universality of deep learning

Reference 5

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Observation 16c83e81-ae2c-43c9-b4cb-f97ff89df101 · outbound

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

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Online stochastic gradient descent on non-convex losses from high-dimensional inference

Reference 6

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Observation df28bd48-c375-4ffe-97f9-7276990a1693 · outbound

This paper cites High-dimensional limit theorems for SGD : Effective dynamics and critical scaling.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions High-dimensional limit theorems for SGD : Effective dynamics and critical scaling

Reference 7

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Observation 2230fce1-742b-4d12-a5c6-8ffb0cd67864 · outbound

This paper cites On Learning Gaussian Multi-index Models with Gradient Flow.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions On Learning Gaussian Multi-index Models with Gradient Flow

Reference 8

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

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Learning single-index models with shallow neural networks

Reference 9

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Observation c68a95ba-b8f1-4912-96e8-abfb8f342c59 · outbound

This paper cites Id3 learns juntas for smoothed product distributions.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Id3 learns juntas for smoothed product distributions

Reference 10

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Observation 7f726ba0-ece0-4ead-a770-3acad5f9d011 · outbound

This paper cites Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit

Reference 11

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This paper cites High-dimensional asymptotics of feature learning: How one gradient step improves the representation.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions High-dimensional asymptotics of feature learning: How one gradient step improves the representation

Reference 12

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Observation 5a9d4ad8-a5c0-4aa3-a899-4a2fae77dc92 · outbound

This paper cites Learning in the presence of low-dimensional structure: a spiked random matrix perspective.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Learning in the presence of low-dimensional structure: a spiked random matrix perspective

Reference 13

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Observation d4f5879d-e842-4356-a0b7-77b7ec874038 · outbound

This paper cites Learning time-scales in two-layers neural networks.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Learning time-scales in two-layers neural networks

Reference 14

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Observation 00ff46dd-da5f-4bc0-aa77-8f746c0d313e · outbound

This paper cites The Distribution of Values of Analytic Functions on Convex Bodies.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions The Distribution of Values of Analytic Functions on Convex Bodies

Reference 15

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Observation 07956966-6f13-4bef-9684-209f0064e743 · outbound

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

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Learning narrow one-hidden-layer ReLU networks

Reference 16

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Observation f37d323b-f737-477b-b7fb-6fbc2621d801 · outbound

This paper cites Learning Juntas under Markov Random Fields.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Learning Juntas under Markov Random Fields

Reference 17

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Observation 8485b09d-721c-4b6c-b0b9-9ed1b35fb49a · outbound

This paper cites A mathematical model for curriculum learning for parities.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions A mathematical model for curriculum learning for parities

Reference 18

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This paper cites Distributional and L^q norm inequalities for polynomials over convex bodies in R^n.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Distributional and L^q norm inequalities for polynomials over convex bodies in R^n

Reference 19

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This paper cites Learning single-index models in G aussian space.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Learning single-index models in G aussian space

Reference 20

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Observation a261ff52-bf6c-4edb-a572-9f73bdb300c6 · outbound

This paper cites How Two-Layer Neural Networks Learn, One (Giant) Step at a Time.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions How Two-Layer Neural Networks Learn, One (Giant) Step at a Time

Reference 21

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

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Neural networks can learn representations with gradient descent

Reference 22

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Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Learning parities with neural networks

Reference 23

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

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Smoothing the landscape boosts the signal for SGD : Optimal sample complexity for learning single index models

Reference 24

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

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Computational-statistical gaps in G aussian single-index models

Reference 25

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This paper cites The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents

Reference 26

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Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Unresolved cited work

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This paper cites Agnostic learning of a single neuron with gradient descent.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Agnostic learning of a single neuron with gradient descent

Reference 28

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This paper cites Superpolynomial lower bounds for learning one-layer neural networks using gradient descent.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Superpolynomial lower bounds for learning one-layer neural networks using gradient descent

Reference 29

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This paper cites Modeling the influence of data structure on learning in neural networks: The hidden manifold model.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Modeling the influence of data structure on learning in neural networks: The hidden manifold model

Reference 30

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This paper cites On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries

Reference 31

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This paper cites Matching the Statistical Query Lower Bound for $k$-Sparse Parity Problems with Sign Stochastic Gradient Descent.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Matching the Statistical Query Lower Bound for $k$-Sparse Parity Problems with Sign Stochastic Gradient Descent

Reference 32

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This paper cites Moment-Matching Polynomials.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Moment-Matching Polynomials

Reference 33

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Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Learning and smoothed analysis

Reference 34

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This paper cites Decision trees are PAC-learnable from most product distributions: a smoothed analysis.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Decision trees are PAC-learnable from most product distributions: a smoothed analysis

Reference 35

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This paper cites In\'egalit\'es isop\'erim\'etriques en analyse et probabilit\'es.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions In\'egalit\'es isop\'erim\'etriques en analyse et probabilit\'es

Reference 36

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

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit

Reference 37

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Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Gradient-based feature learning under structured data

Reference 38

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Observation b5a2bf39-2c45-4581-9f20-7566c727d531 · outbound

This paper cites Quantifying the benefit of using differentiable learning over tangent kernels.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Quantifying the benefit of using differentiable learning over tangent kernels

Reference 39

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This paper cites Learning functions of k relevant variables.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Learning functions of k relevant variables

Reference 40

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This paper cites Concentration inequalities under sub- G aussian and sub-exponential conditions.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Concentration inequalities under sub- G aussian and sub-exponential conditions

Reference 41

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This paper cites Improved statistical and computational complexity of the mean-field L angevin dynamics under structured data.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Improved statistical and computational complexity of the mean-field L angevin dynamics under structured data

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:34:17.257092Z

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Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Nazarov, M

Reference 43

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Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Analysis of Boolean Functions

Reference 44

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Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Distribution-specific hardness of learning neural networks

Reference 45

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This paper cites Understanding machine learning: From theory to algorithms.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Understanding machine learning: From theory to algorithms

Reference 46

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This paper cites Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:34:17.199475Z

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Observation da7c7c31-73f0-497c-9602-dc602e4cdebd · outbound

This paper cites On the cryptographic hardness of learning single periodic neurons.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions On the cryptographic hardness of learning single periodic neurons

Reference 48

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Observation b3580926-7514-4c0c-87f7-36a62cb99ca3 · outbound

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

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Fundamental limits of weak learnability in high-dimensional multi-index models

Reference 49

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This paper cites Finding correlations in subquadratic time, with applications to learning parities and juntas.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Finding correlations in subquadratic time, with applications to learning parities and juntas

Reference 50

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This paper cites Learning a single neuron for non-monotonic activation functions.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Learning a single neuron for non-monotonic activation functions

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:34:17.163944Z

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Observation b509c07e-1dd2-4af9-b201-fe406fa9287c · outbound

This paper cites Learning a single neuron with gradient methods.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Learning a single neuron with gradient methods

Reference 52

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This paper cites On single-index models beyond G aussian data.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions On single-index models beyond G aussian data

Reference 53

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Pith citing papers

Observation e7bb62c4-ac09-4066-92b8-de67823e272f · inbound

Online Learning of Neural Networks cites this paper.

Online Learning of Neural Networks Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions

Reference 2023

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The Power of Power Law: Asymmetry Enables Compositional Reasoning cites this paper.

The Power of Power Law: Asymmetry Enables Compositional Reasoning Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions

Reference 13

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Observation 8ce4de0f-6d99-4261-b741-38c20ec46912 · inbound

The Power of Power Law: Asymmetry Enables Compositional Reasoning cites this paper.

The Power of Power Law: Asymmetry Enables Compositional Reasoning Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions

Reference 13

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The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently cites this paper.

The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions

Reference 158

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