Paper Citation Record · LEDGER

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning

As of 21 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2506.19883.

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

pith.paper-citation-record.v1
2506.19883 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:44:45.489284Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 578d0cc5-8189-4b35-8338-c18e428f2d75 · outbound

This paper cites Uncertainty-aware search framework for multi-objective bayesian optimization.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Uncertainty-aware search framework for multi-objective bayesian optimization

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:46.982430Z

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Observation 16994075-b9f8-409c-9a68-3a954d422a32 · outbound

This paper cites Convex optimization.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Convex optimization

Reference 2

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Observation 4b20ed7b-08c7-413d-9530-f643ee853b82 · outbound

This paper cites An analysis of the softmax cross entropy loss for learning-to-rank with binary relevance.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning An analysis of the softmax cross entropy loss for learning-to-rank with binary relevance

Reference 3

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Observation b5a7fecd-1d9a-4e90-be84-127036f27f1e · outbound

This paper cites Co-attentive multi-task learning for explainable recommendation.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Co-attentive multi-task learning for explainable recommendation

Reference 4

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Observation 3ef0ebca-3ab9-4f77-8dc7-c04ca73acae0 · outbound

This paper cites A fast and elitist multiobjective genetic algorithm: Nsga-ii.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning A fast and elitist multiobjective genetic algorithm: Nsga-ii

Reference 5

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Observation 47e12c30-bb42-4b73-81fa-15a33c5dba2d · outbound

This paper cites Multiple-gradient descent algorithm (mgda) for multiobjective optimization.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Multiple-gradient descent algorithm (mgda) for multiobjective optimization

Reference 6

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Observation b01a1901-1b7c-4f96-aa7b-9b9e2e61236a · outbound

This paper cites Multicriteria optimization, volume 491.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Multicriteria optimization, volume 491

Reference 7

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Observation 682f7f61-9197-468c-b419-b50b72e5da92 · outbound

This paper cites Spider: Near-optimal non-convex optimization via stochastic path-integrated differential estimator.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Spider: Near-optimal non-convex optimization via stochastic path-integrated differential estimator

Reference 8

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Observation d36e84d6-c568-4ce0-8392-7db6826a2023 · outbound

This paper cites Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach

Reference 9

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Observation 8c0079d2-5428-4aff-8d9b-c5c25426a303 · outbound

This paper cites Variance reduction can improve trade-off in multi-objective learning.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Variance reduction can improve trade-off in multi-objective learning

Reference 10

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Observation 3ac8218c-109f-4c88-8343-753f8f35615f · outbound

This paper cites Steepest descent methods for multicriteria optimization.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Steepest descent methods for multicriteria optimization

Reference 11

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Observation 3a43b0e1-cbbe-4188-803c-cac647a9d1fd · outbound

This paper cites Complexity of gradient descent for multiobjective optimization.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Complexity of gradient descent for multiobjective optimization

Reference 12

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Observation d54ed3ee-7a42-420d-bd33-e41e53e3fa86 · outbound

This paper cites Stochastic first-and zeroth-order methods for nonconvex stochastic programming.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Stochastic first-and zeroth-order methods for nonconvex stochastic programming

Reference 13

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Observation 3733dee5-db06-4c5e-88c5-a28d1ff7fbbc · outbound

This paper cites Deep Residual Learning for Image Recognition.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Deep Residual Learning for Image Recognition

Reference 14

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Observation 80ab2b85-5c32-4dba-83f8-e73ad3141bb7 · outbound

This paper cites Fine-grained fashion representation learning by online deep clustering.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Fine-grained fashion representation learning by online deep clustering

Reference 15

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Observation 8dd63bbb-cf0a-463f-a1b4-561314b07f66 · outbound

This paper cites Learning attribute and class-specific representation duet for fine-grained fashion analysis.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Learning attribute and class-specific representation duet for fine-grained fashion analysis

Reference 16

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Observation dddf22cb-1943-4f46-89bd-280c4f9f23cb · outbound

This paper cites A deep survival analysis method based on ranking.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning A deep survival analysis method based on ranking

Reference 17

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Observation 448859c5-9d98-4189-b496-f1cdea7a8aab · outbound

This paper cites SCAFFOLD : Stochastic controlled averaging for federated learning.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning SCAFFOLD : Stochastic controlled averaging for federated learning

Reference 18

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Observation 83af25b0-9b91-484d-8761-5393fc1f8bf3 · outbound

This paper cites Deep attentive ranking networks for learning to order sentences.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Deep attentive ranking networks for learning to order sentences

Reference 19

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Observation a6942fe9-18a3-43b9-9d19-e62c00838e25 · outbound

This paper cites Bayesian optimization algorithms for multi-objective optimization.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Bayesian optimization algorithms for multi-objective optimization

Reference 20

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Observation b25ea2c1-ea68-45ae-a027-ffce48139073 · outbound

This paper cites Mnist handwritten digit database.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Mnist handwritten digit database

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:46.456380Z

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Observation f9383946-7e6f-47e4-a75e-d776e1d667ce · outbound

This paper cites Pareto multi-task learning.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Pareto multi-task learning

Reference 22

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Observation d3063cda-6d25-41c0-b334-cdaafe29be90 · outbound

This paper cites The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:46.413336Z

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Observation 164ab3a1-9b63-470c-bd31-498846fc9a07 · outbound

This paper cites Deep learning face attributes in the wild.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Deep learning face attributes in the wild

Reference 24

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Observation 51437907-27ac-4cf1-a79e-550df9b32956 · outbound

This paper cites Deep match to rank model for personalized click-through rate prediction.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Deep match to rank model for personalized click-through rate prediction

Reference 25

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Observation c4a6d31d-bb16-4c6d-a6d3-a515fc829c1f · outbound

This paper cites Multi-label learning to rank through multi-objective optimization.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Multi-label learning to rank through multi-objective optimization

Reference 26

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Observation 6efffcd9-cedc-4ec9-8b29-60355faa30e8 · outbound

This paper cites Querywise fair learning to rank through multi-objective optimization.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Querywise fair learning to rank through multi-objective optimization

Reference 27

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Observation 9961cba0-983b-4cb4-8703-e3b771344d51 · outbound

This paper cites A stochastic multiple gradient descent algorithm.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning A stochastic multiple gradient descent algorithm

Reference 28

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Observation 7817ff58-caee-40e5-8676-7d9d79501078 · outbound

This paper cites Nonlinear multiobjective optimization, volume 12.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Nonlinear multiobjective optimization, volume 12

Reference 29

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STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Algorithms for multicriterion optimization

Reference 30

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This paper cites Image retrieval with attribute-associated auxiliary references.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Image retrieval with attribute-associated auxiliary references

Reference 31

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Observation 26bd4994-f090-4f8b-b0a7-5c730341c12c · outbound

This paper cites Policy gradient approaches for multi-objective sequential decision making.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Policy gradient approaches for multi-objective sequential decision making

Reference 32

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Observation 042d57e6-1be9-4c98-90ff-8511be4c78f3 · outbound

This paper cites Multi-task video captioning with video and entailment generation.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Multi-task video captioning with video and entailment generation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:46.114651Z

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Observation c2d2d529-e953-47c3-b172-891ca5e69943 · outbound

This paper cites Learning with average precision: Training image retrieval with a listwise loss.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Learning with average precision: Training image retrieval with a listwise loss

Reference 34

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Observation 7615682f-e85c-47f7-9dc0-932f1d48060d · outbound

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STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Dynamic routing between capsules

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STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Multi-task learning as multi-objective optimization

Reference 36

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This paper cites Pairwise Learning for Neural Link Prediction.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Pairwise Learning for Neural Link Prediction

Reference 37

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This paper cites Deep multi-interest network for click-through rate prediction.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Deep multi-interest network for click-through rate prediction

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:46.012401Z

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Observation de381ba6-1d75-4f37-84d8-703bf2c3f25e · outbound

This paper cites Empirically testing deep and shallow ranking models for click-through rate (ctr) prediction.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Empirically testing deep and shallow ranking models for click-through rate (ctr) prediction

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.982579Z

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Observation b33a0fa4-ced7-46dd-b75d-f75418b24c57 · outbound

This paper cites Pareto policy pool for model-based offline reinforcement learning.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Pareto policy pool for model-based offline reinforcement learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.943217Z

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Observation 318221b0-de0d-43cb-bcfe-e9f2ed12098d · outbound

This paper cites Wassrank: Listwise document ranking using optimal transport theory.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Wassrank: Listwise document ranking using optimal transport theory

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.901184Z

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Observation 5a96f855-af54-46ac-9377-c6b100a630c8 · outbound

This paper cites Moea/d: A multiobjective evolutionary algorithm based on decomposition.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Moea/d: A multiobjective evolutionary algorithm based on decomposition

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.866888Z

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Observation d9da26d5-cb45-4b5a-a74d-10ac1ff830b2 · outbound

This paper cites On the convergence of stochastic multi-objective gradient manipulation and beyond.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning On the convergence of stochastic multi-objective gradient manipulation and beyond

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.833981Z

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Observation 45239a21-97b8-4894-8c5b-95985fab4883 · outbound

This paper cites Multi-task learning on heterogeneous graph neural network for substitute recommendation.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Multi-task learning on heterogeneous graph neural network for substitute recommendation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.799362Z

Source-reported events for the cited work

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