\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments | Paper Citation Record

Observation 9356c76a-7dcd-4c01-9dfa-2c0a5e44c342 · outbound

This paper cites Blockchain for deep learning: review and open challenges.Cluster Computing, 26(1):197–221.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Blockchain for deep learning: review and open challenges.Cluster Computing, 26(1):197–221

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.439560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fc41510d-0c34-4200-a7c9-106625b48ff8 · outbound

This paper cites Survey on the convergence of machine learning and blockchain.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Survey on the convergence of machine learning and blockchain

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.427969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a7875005-5cc1-4104-bcfe-b93e3f14d824 · outbound

This paper cites Blockchain meets machine learning: a survey.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Blockchain meets machine learning: a survey

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.430900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2355411a-b1f4-49e7-af48-4a7075e608d9 · outbound

This paper cites Blockchain and machine learning: A critical review on security.Information, 14(5):295.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Blockchain and machine learning: A critical review on security.Information, 14(5):295

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.454403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 34b11854-773e-4c42-a6b7-b7069e6faf12 · outbound

This paper cites Blockchain technology and artificial intelligence together: a critical review on applications.Applied Sciences, 12(24):12948.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Blockchain technology and artificial intelligence together: a critical review on applications.Applied Sciences, 12(24):12948

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.403198Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 590c1916-bc5b-4361-aca5-554621a08357 · outbound

This paper cites Blockchain for AI: A disruptive integration.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Blockchain for AI: A disruptive integration

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.406842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cbcdba1b-bc97-4f13-8e98-cadc808e8a0b · outbound

This paper cites Blockchain for AI: Review and open research challenges.IEEE Access, 7:10127–10149.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Blockchain for AI: Review and open research challenges.IEEE Access, 7:10127–10149

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.433788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b7314e04-a879-488a-9827-e36491b4a1a9 · outbound

This paper cites VeriML: Enabling integrity assurances and fair payments for machine learning as a service.IEEE Transactions on Parallel and Distributed Systems, 32(10):2524–2540.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments VeriML: Enabling integrity assurances and fair payments for machine learning as a service.IEEE Transactions on Parallel and Distributed Systems, 32(10):2524–2540

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.465188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 65347ec5-b72d-434d-b44c-7b1688f57c6c · outbound

This paper cites Blockchains cannot rely on honesty.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Blockchains cannot rely on honesty

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.436902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5b0d1a75-019d-4f44-b7ac-61523664a523 · outbound

This paper cites Fact and fiction: Challenging the honest majority assumption of permissionless blockchains.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Fact and fiction: Challenging the honest majority assumption of permissionless blockchains

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.410308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0f09db0f-6059-404d-8ed9-0c390c60f487 · outbound

This paper cites Zero Cost.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Zero Cost

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.480222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1eb5432f-a4ec-4c7e-b8bb-891b4f3a5b47 · outbound

This paper cites Byzantine-Resilient Non-Convex Stochastic Gradient Descent.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Byzantine-Resilient Non-Convex Stochastic Gradient Descent

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:30:55.245530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1c382ec3-96e6-4f7a-b471-f016a0130d53 · outbound

This paper cites Byzantine machine learning made easy by resilient averaging of momentums.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Byzantine machine learning made easy by resilient averaging of momentums

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.468865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a1910a3f-46c3-4855-8414-dc2cbf387853 · outbound

This paper cites Optimal complexity in byzantine- robust distributed stochastic optimization with data heterogeneity.Journal of Machine Learning Research, 26(268):1–58.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Optimal complexity in byzantine- robust distributed stochastic optimization with data heterogeneity.Journal of Machine Learning Research, 26(268):1–58

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.458083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 560f2aca-cf6a-4422-b224-4ef87b1c9aea · outbound

This paper cites Learning from history for byzantine robust optimization.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Learning from history for byzantine robust optimization

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.473106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a42b1671-0b98-4f58-b8df-35a5e12dab6f · outbound

This paper cites Byzantine machine learning: A primer.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Byzantine machine learning: A primer

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.441812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7592d867-e804-4803-8534-052258d8a865 · outbound

This paper cites Byzantine-robust federated learning with optimal statistical rates.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Byzantine-robust federated learning with optimal statistical rates

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.461621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation caeb044f-c9d1-4500-88ab-ad76b6e1f33f · outbound

This paper cites Byzantine- robust learning on heterogeneous data via gradient splitting.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Byzantine- robust learning on heterogeneous data via gradient splitting

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.421576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7831e7d8-27b3-42b2-90c2-f5a1bd32323c · outbound

This paper cites Byzantine-resilient stochastic gradient descent for distributed learning: A lipschitz-inspired coordinate-wise median approach.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Byzantine-resilient stochastic gradient descent for distributed learning: A lipschitz-inspired coordinate-wise median approach

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.483391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e4ae4c8a-75f9-4abd-ad26-06bf24a9897f · outbound

This paper cites Detox: A redundancy-based framework for faster and more robust gradient aggregation.Advances in Neural Information Processing Systems, 32.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Detox: A redundancy-based framework for faster and more robust gradient aggregation.Advances in Neural Information Processing Systems, 32

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.444564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 791a17fa-be67-44ee-813e-593877e70ea2 · outbound

This paper cites Byzantine-robust federated learning through spatial-temporal analysis of local model updates.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Byzantine-robust federated learning through spatial-temporal analysis of local model updates

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.398574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 383e31e8-3269-4d97-9e50-2bbc56b3e393 · outbound

This paper cites Ro- bust distributed learning: Tight error bounds and breakdown point under data heterogeneity.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Ro- bust distributed learning: Tight error bounds and breakdown point under data heterogeneity

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.424874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation dfd07e97-ecd5-42c6-8208-219961f4ff26 · outbound

This paper cites Byzantine stochastic gradient descent.Advances in Neural Information Processing Systems, 31.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Byzantine stochastic gradient descent.Advances in Neural Information Processing Systems, 31

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.413978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a3ce1e66-4207-4edd-9eee-49011cfb03f0 · outbound

This paper cites an unresolved cited work.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-05-14T12:35:14.447882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 76bd5bd9-ab03-4bb2-a40f-b32d158a68e9 · outbound

This paper cites The hidden vulnerability of distributed learning in Byzantium.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments The hidden vulnerability of distributed learning in Byzantium

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.477076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 033ccf93-b8d6-4bd9-b5ff-77caea98049c · outbound

This paper cites Byzantine-robust distributed learning: Towards optimal statistical rates.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Byzantine-robust distributed learning: Towards optimal statistical rates

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.450932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 360375b6-48e6-4d2e-9937-8038c8d8fff8 · outbound

This paper cites Machine learn- ing with adversaries: Byzantine tolerant gradient descent.Advances in Neural Information Processing Systems, 30.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Machine learn- ing with adversaries: Byzantine tolerant gradient descent.Advances in Neural Information Processing Systems, 30

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.417755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0ff6523c-863a-40c7-a0ce-4c522404d41f · outbound

This paper cites Cadambe, and Mohammad Ali Maddah-Ali.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Cadambe, and Mohammad Ali Maddah-Ali

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:30:55.163237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f53f424c-1365-4e2e-8ae6-67751fb9e743 · outbound

This paper cites Game of Coding for Vector-Valued Computations.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Game of Coding for Vector-Valued Computations

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-11T02:30:55.264022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 480ef62c-6adb-440c-8d6f-c5b1eaa758ec · outbound

This paper cites Game of coding: Sybil resistant decentralized machine learning with minimal trust assumption.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Game of coding: Sybil resistant decentralized machine learning with minimal trust assumption

Reference 30

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T02:30:55.303172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8644eac1-f7a0-47ae-a0ac-9ffa27847f36 · outbound

This paper cites Game of coding with an unknown adversary.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Game of coding with an unknown adversary

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.567103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a60e73ed-b249-449d-a028-3b3b38add23c · outbound

This paper cites Game of coding: Coding theory in the presence of rational adversaries, motivated by decentralized machine learning.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Game of coding: Coding theory in the presence of rational adversaries, motivated by decentralized machine learning

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:30:55.271660Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7413a77e-54d6-47ef-b51d-c5677254e01d · outbound

This paper cites Springer Science & Business Media.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Springer Science & Business Media

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.570818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 472a4295-f512-4406-97ac-334efdafdff6 · outbound

This paper cites Adam: A method for stochastic optimization.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Adam: A method for stochastic optimization

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.574094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e2ecb280-f485-426e-b6d9-752e786dc912 · outbound

This paper cites ADADELTA: An Adaptive Learning Rate Method.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments ADADELTA: An Adaptive Learning Rate Method

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:30:55.258913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 66dfd07b-49b1-4658-bc05-0ec6daaeeada · outbound

This paper cites Adaptive Gradient Methods with Dynamic Bound of Learning Rate.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Adaptive Gradient Methods with Dynamic Bound of Learning Rate

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:30:55.296354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c3dcb82d-d508-4ffa-9e82-f2c6c351319e · outbound

This paper cites Adaptive methods for nonconvex optimization.Advances in Neural Information Processing Systems, 31.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Adaptive methods for nonconvex optimization.Advances in Neural Information Processing Systems, 31

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.577834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7826a395-3308-4516-a259-4cc5fdea46b5 · outbound

This paper cites Decoupled Weight Decay Regularization.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Decoupled Weight Decay Regularization

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-05-11T02:30:55.214541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 14c74eb3-f1ea-4cc8-90e2-2895e3ce8cc0 · outbound

This paper cites Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:30:55.180775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 025968da-dced-4ca0-88eb-ff300d9e001c · outbound

This paper cites Some methods of speeding up the convergence of iteration methods.USSR Computational Mathematics and Mathematical Physics, 4(5):1–17.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Some methods of speeding up the convergence of iteration methods.USSR Computational Mathematics and Mathematical Physics, 4(5):1–17

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.492367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation dfaf6b51-5f16-48bb-b28a-b1cd80ecc867 · outbound

This paper cites Adafactor: Adaptive learning rates with sublinear memory cost.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Adafactor: Adaptive learning rates with sublinear memory cost

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.496133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cd5d2ed4-d338-44ea-8569-32ca4901db2c · outbound

This paper cites Momentum-based variance reduction in non-convex SGD.Advances in Neural Information Processing Systems, 32.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Momentum-based variance reduction in non-convex SGD.Advances in Neural Information Processing Systems, 32

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.477557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 742c66b8-55ad-4f86-bba3-8873cbd0e392 · outbound

This paper cites A stochastic approximation method.The Annals of Mathematical Statistics, pages 400–407.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments A stochastic approximation method.The Annals of Mathematical Statistics, pages 400–407

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.466185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d12b8580-94eb-4b44-b658-fa5cc8472367 · outbound

This paper cites Optimization methods for large-scale machine learning.SIAM Review, 60(2):223–311.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Optimization methods for large-scale machine learning.SIAM Review, 60(2):223–311

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.485756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 034754b1-ea0f-4e92-9b8a-b76a08bc8d82 · outbound

This paper cites Adaptive subgradient methods for online learning and stochastic optimization.Journal of Machine Learning Research, 12(7).

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Adaptive subgradient methods for online learning and stochastic optimization.Journal of Machine Learning Research, 12(7)

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.329385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fb14205e-3267-428c-9675-8e3013046b16 · outbound

This paper cites Adabelief optimizer: Adapting stepsizes by the belief in observed gradients.Advances in Neural Information Processing Systems, 33:18795–18806.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Adabelief optimizer: Adapting stepsizes by the belief in observed gradients.Advances in Neural Information Processing Systems, 33:18795–18806

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.388003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 70c0ab31-7c05-4b0d-b3a2-345a46cab388 · outbound

This paper cites On the Convergence of Adam and Beyond.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments On the Convergence of Adam and Beyond

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:30:55.169793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d830107b-1b7a-471d-b3bc-546bdaa54f28 · outbound

This paper cites Adagrad stepsizes: Sharp convergence over nonconvex landscapes.Journal of Machine Learning Research, 21(219):1–30.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Adagrad stepsizes: Sharp convergence over nonconvex landscapes.Journal of Machine Learning Research, 21(219):1–30

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.553039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a4ac988b-9b32-4f78-9962-2ed1db7841e1 · outbound

This paper cites On the importance of initial- ization and momentum in deep learning.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments On the importance of initial- ization and momentum in deep learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.556689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 49c717a9-fb29-49d1-ba6d-3a41250750b2 · outbound

This paper cites Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks

Reference 50

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T02:30:55.196260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f388ce18-9955-4885-bcb5-736ffb4f3695 · outbound

This paper cites Learning-rate-free learning by D-Adaptation.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Learning-rate-free learning by D-Adaptation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.542728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 93304abd-007b-4d33-9a31-093431ec5892 · outbound

This paper cites Learning in stackelberg games with non-myopic agents.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Learning in stackelberg games with non-myopic agents

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.546172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 15924eac-c0e9-44fd-921e-b58c9908ae24 · outbound

This paper cites Repeated Contracting with Multiple Non-Myopic Agents: Policy Regret and Limited Liability.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Repeated Contracting with Multiple Non-Myopic Agents: Policy Regret and Limited Liability

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:30:55.220573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0ae7409a-017d-42ef-9b93-60f3646cc909 · outbound

This paper cites Pareto-optimal algo- rithms for learning in games.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Pareto-optimal algo- rithms for learning in games

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.581108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3116398b-5011-47a8-9701-1b4b98ad9e7d · outbound

This paper cites Levine, and Wolfgang Pesendorfer.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Levine, and Wolfgang Pesendorfer

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.539007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1d39c2d9-27aa-45d4-bc91-035d04d81707 · outbound

This paper cites Learning to Manipulate a Commitment Optimizer.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Learning to Manipulate a Commitment Optimizer

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:30:55.208928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e4b7fb6d-89ec-4ca7-a115-6e694009ae44 · outbound

This paper cites Nesterov , title =.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Nesterov , title =

Reference 57

Resolution: metadata mismatch; doi, observed 2026-05-11T02:30:52.223444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation be9a6b36-3c8b-4fef-8093-54ccf00e0023 · outbound

This paper cites Fast Convergence of Stochastic Gradient Descent under a Strong Growth Condition.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Fast Convergence of Stochastic Gradient Descent under a Strong Growth Condition

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:30:52.216056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 778f229a-1bb5-4d56-9d8a-257ddc10c2af · outbound

This paper cites Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.523716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2522d422-7640-4c87-b844-701e51b4c577 · outbound

This paper cites MNIST handwritten digit database.ATT Labs [Online], 2.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments MNIST handwritten digit database.ATT Labs [Online], 2

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.535099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c23ccbdc-1f5a-4145-bcbb-b45aa2cea314 · outbound

This paper cites Deep residual learning for im- age recognition.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Deep residual learning for im- age recognition

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.527325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b89170c2-b3fb-4b14-b877-be385283115c · outbound

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

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Learning multiple layers of features from tiny images

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.549681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 998da0a3-9e04-4c6f-818d-62e5858adbf9 · outbound

This paper cites Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.368531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7412b901-ade6-4858-9e74-050a9fb0f815 · outbound

This paper cites FLTrust: Byzantine-robust fed- erated learning via trust bootstrapping.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments FLTrust: Byzantine-robust fed- erated learning via trust bootstrapping

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.354812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7823c584-f0a3-49b3-ada1-c9599a9444f5 · outbound

This paper cites FLAME: Taming backdoors in federated learning.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments FLAME: Taming backdoors in federated learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.519067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 376fb915-76a5-4bb1-9459-23a690792e66 · outbound

This paper cites Fed- DMC: Efficient and robust federated learning via detecting malicious clients.IEEE Transactions on Dependable and Secure Computing, 21(6):5259–5274.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Fed- DMC: Efficient and robust federated learning via detecting malicious clients.IEEE Transactions on Dependable and Secure Computing, 21(6):5259–5274

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.391482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 124577c2-beab-4c6a-935e-468f96065188 · outbound

This paper cites FedID: Enhancing federated learning security through dynamic identification.IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(10):8907–8922.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments FedID: Enhancing federated learning security through dynamic identification.IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(10):8907–8922

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.531213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d2c25729-6224-4967-94df-ad68972384a5 · outbound

This paper cites RepuNet: A Reputation System for Mitigating Malicious Clients in DFL.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments RepuNet: A Reputation System for Mitigating Malicious Clients in DFL

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:30:55.250480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 80a24867-e85d-4ae7-a49f-a5a2c3dda340 · outbound

This paper cites FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:45:53.344936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5afac621-82a6-4cf9-b882-ab06328f9a7b · outbound

This paper cites Bitcoin: A peer-to-peer electronic cash system.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Bitcoin: A peer-to-peer electronic cash system

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.481334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation daa7fca7-f996-40d9-bf88-bbcfbcbbdf47 · outbound

This paper cites Ethereum white paper.GitHub repository, 1:22–23.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Ethereum white paper.GitHub repository, 1:22–23

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.563548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4c76ad08-4e46-4269-b38e-700d9b52d9d1 · outbound

This paper cites Proofs, arguments, and zero-knowledge.Foundations and Trends® in Privacy and Security, 4(2–4):117–660.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Proofs, arguments, and zero-knowledge.Foundations and Trends® in Privacy and Security, 4(2–4):117–660

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.380923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b8a45652-a0ad-4200-8450-d56f1cb1e802 · outbound

This paper cites ZEN: An optimizing compiler for verifiable, zero-knowledge neural network inferences.Cryptology ePrint Archive.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments ZEN: An optimizing compiler for verifiable, zero-knowledge neural network inferences.Cryptology ePrint Archive

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.512018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bcd4691a-d18a-4693-a18e-559e592256b8 · outbound

This paper cites ZkCNN: Zero-knowledge proofs for convolutional neural network predictions and accuracy.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments ZkCNN: Zero-knowledge proofs for convolutional neural network predictions and accuracy

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.508656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3ed617b3-b9da-47d8-b407-9c965495fc77 · outbound

This paper cites Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey

Reference 75

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:30:55.238804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 34ae6a3b-20dd-4654-910d-8bcba2c695f5 · outbound

This paper cites SecureML: A system for scalable privacy-preserving machine learning.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments SecureML: A system for scalable privacy-preserving machine learning

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.488975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cc024fcc-260f-495e-9b6f-d82f706e08a9 · outbound

This paper cites vCNN: Verifiable convolutional neu- ral network based on zk-SNARKs.IEEE Transactions on Dependable and Secure Computing.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments vCNN: Verifiable convolutional neu- ral network based on zk-SNARKs.IEEE Transactions on Dependable and Secure Computing

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.515583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c05ded73-0547-41bf-a813-d78a311c3c58 · outbound

This paper cites Mystique: Efficient conversions for Zero-Knowledge proofs with applications to machine learning.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Mystique: Efficient conversions for Zero-Knowledge proofs with applications to machine learning

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.469846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0604ab4b-e6ef-4e6f-afa4-42e11f6899a2 · outbound

This paper cites Interactive proofs for rounding arithmetic.IEEE Access, 10:122706–122725.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Interactive proofs for rounding arithmetic.IEEE Access, 10:122706–122725

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.473555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a317c2d4-2e06-487d-b7c6-4a74940f2779 · outbound

This paper cites Succinct zero-knowledge for floating point computations.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Succinct zero-knowledge for floating point computations

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.560418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f9174882-642f-4062-8fe7-591d71e232c6 · outbound

This paper cites Taking Proof-Based verified computation a few steps closer to practicality.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Taking Proof-Based verified computation a few steps closer to practicality

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.504632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 82ca4cb1-b77d-4b7a-b631-41500c2a4be7 · outbound

This paper cites Experimenting with zero-knowledge proofs of training.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Experimenting with zero-knowledge proofs of training

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:30:16.500356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3c24d4c7-e4ed-46c5-945c-891b902f64e5 · outbound

This paper cites SAKSHI: Decentralized AI Platforms.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments SAKSHI: Decentralized AI Platforms

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:30:55.149819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a41306c3-86e1-4ccd-9c94-46d9290df6c0 · outbound

This paper cites opML: Optimistic Machine Learning on Blockchain.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments opML: Optimistic Machine Learning on Blockchain

Reference 84

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:30:55.140122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 37f62c66-dcd2-4166-b39d-2791209e84ed · outbound

This paper cites Draft is Available.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Draft is Available

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.350542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fda84324-50c5-4f61-a1f5-7f3f7b75476a · outbound

This paper cites Frame codes for distributed coded computation.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Frame codes for distributed coded computation

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.384333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation eba7ff56-fa07-43b8-9ce0-f73cb8a9c01e · outbound

This paper cites Analog error-correcting codes.IEEE Transactions on Information Theory, 66(7): 4075–4088.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Analog error-correcting codes.IEEE Transactions on Information Theory, 66(7): 4075–4088

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.336795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2cfa998a-e534-49d9-9cc1-f01723e340c3 · outbound

This paper cites Codedsketch: A coding scheme for distributed computation of approximated matrix multiplication.IEEE Transactions on Information Theory, 67(6):4185–4196.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Codedsketch: A coding scheme for distributed computation of approximated matrix multiplication.IEEE Transactions on Information Theory, 67(6):4185–4196

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.341539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0f3d3e7e-2f1f-4ceb-a3fa-fe04ca28244e · outbound

This paper cites Berrut approximated coded comput- ing: Straggler resistance beyond polynomial computing.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1):111–122.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Berrut approximated coded comput- ing: Straggler resistance beyond polynomial computing.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1):111–122

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.359518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 40216cec-1ff7-4569-a4f9-96b95206f2e5 · outbound

This paper cites Polynomial codes: an optimal design for high-dimensional coded matrix multiplication.Advances in Neural Information Processing Systems, 30.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Polynomial codes: an optimal design for high-dimensional coded matrix multiplication.Advances in Neural Information Processing Systems, 30

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.377186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7eee97f3-1e23-4dee-9f55-e33697e13ae9 · outbound

This paper cites Lagrange coded computing: Optimal design for resiliency, security, and privacy.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Lagrange coded computing: Optimal design for resiliency, security, and privacy

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.372762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d68df628-2f0f-4cec-a29f-bcb6e18645c9 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.345956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a2c227c8-45f6-4a71-adc7-2b4dae4ae752 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.Advances in neural information processing systems, 32.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Pytorch: An imperative style, high-performance deep learning library.Advances in neural information processing systems, 32

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T12:35:14.395079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a2b06adb-6b44-4bb3-949e-16739fa1e5db · outbound

This paper cites an unresolved cited work.

\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Unresolved cited work

Reference 94

Resolution: unresolved; raw_fallback, observed 2026-05-14T12:35:14.363443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers