Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization | Paper Citation Record

Observation 9ca48e5d-e259-44c3-9b4f-c9b9d05da8b7 · outbound

This paper cites Moving Anchor Extragradient Methods For Smooth Structured Minimax Problems.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Moving Anchor Extragradient Methods For Smooth Structured Minimax Problems

Reference 1

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Observation 4f2f753d-4999-4148-94e1-8777a8d14147 · outbound

This paper cites Katyusha: The first direct acceleration of stochastic gradient methods.Journal of Machine Learning Research, 18(221):1–51, 2018.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Katyusha: The first direct acceleration of stochastic gradient methods.Journal of Machine Learning Research, 18(221):1–51, 2018

Reference 2

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Observation 19a5d371-edb7-4c91-85f7-21883b0b0b31 · outbound

This paper cites Bauschke and Patrick L.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Bauschke and Patrick L

Reference 3

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Observation 0968775c-7506-4dd4-a8cf-8023e8dc68ca · outbound

This paper cites Stochastic gradient descent-ascent: Unified theory and new efficient methods.International Conference on Artificial Intelligence and Statistics, 2023.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Stochastic gradient descent-ascent: Unified theory and new efficient methods.International Conference on Artificial Intelligence and Statistics, 2023

Reference 4

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Observation c010fa8d-08fc-43ec-879b-0d7f4328aef3 · outbound

This paper cites Bot ¸ and Enis Chenchene.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Bot ¸ and Enis Chenchene

Reference 5

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Observation b9dee892-0d6f-43d2-aafd-af0c48620dcf · outbound

This paper cites Fast Optimistic Gradient De- scent Ascent (OGDA) method in continuous and discrete time.Foundations of Computational Mathematics, 2023.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Fast Optimistic Gradient De- scent Ascent (OGDA) method in continuous and discrete time.Foundations of Computational Mathematics, 2023

Reference 6

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Observation 1075a698-88e3-4b0c-8264-0428b1809114 · outbound

This paper cites Stochastic fixed-point iterations for nonexpansive maps: Convergence and error bounds.SIAM Journal on Control and Optimization, 62(1):191–219, 2024.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Stochastic fixed-point iterations for nonexpansive maps: Convergence and error bounds.SIAM Journal on Control and Optimization, 62(1):191–219, 2024

Reference 7

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Observation dcdff90e-0306-4569-acd3-20baafe255e7 · outbound

This paper cites Stochastic Halpern iteration in normed spaces and appli- cations to reinforcement learning.Mathematical Programming, 2026.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Stochastic Halpern iteration in normed spaces and appli- cations to reinforcement learning.Mathematical Programming, 2026

Reference 8

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Observation 0dfd8b07-87ce-46a7-8c0a-f5ab11d6fb1f · outbound

This paper cites Stochastic halpern iteration with variance reduction for stochastic monotone inclusions.Neural Information Processing Systems, 2022.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Stochastic halpern iteration with variance reduction for stochastic monotone inclusions.Neural Information Processing Systems, 2022

Reference 9

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Observation 07cd6030-aef7-4138-bfc9-d0ee991d5383 · outbound

This paper cites Variance reduced halpern iteration for finite-sum monotone inclusions.International Conference on Learning Representations, 2024.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Variance reduced halpern iteration for finite-sum monotone inclusions.International Conference on Learning Representations, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:57.400866Z

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Observation 05ede653-4c07-4d4f-b534-bf5962988ffd · outbound

This paper cites Accelerated single-call methods for constrained min-max optimiza- tion.International Conference on Learning Representations, 2023.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Accelerated single-call methods for constrained min-max optimiza- tion.International Conference on Learning Representations, 2023

Reference 11

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Observation 6657cf94-628b-4a60-afdf-b87bae7509a4 · outbound

This paper cites Near-optimal algorithms for making the gradient small in stochastic minimax optimization.Journal of Machine Learning Research, 25(387):1–44, 2024.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Near-optimal algorithms for making the gradient small in stochastic minimax optimization.Journal of Machine Learning Research, 25(387):1–44, 2024

Reference 12

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Observation a5b13463-1cb6-41b7-b5ad-d95ae82959a0 · outbound

This paper cites Training GANs with optimism.International Conference on Learning Representations, 2018.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Training GANs with optimism.International Conference on Learning Representations, 2018

Reference 13

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Observation bc69296a-bac9-406f-9d90-a8d6ba6df40d · outbound

This paper cites Variance reduction for root-finding problems.Mathematical Programming, 197(1): 375–410, 2023.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Variance reduction for root-finding problems.Mathematical Programming, 197(1): 375–410, 2023

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:57.339998Z

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Observation 9e453e6e-020f-46b2-b71f-9a0644e7ba28 · outbound

This paper cites First-order methods of smooth convex optimization with inexact oracle.Mathematical Programming, 146(1):37–75, 2014.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization First-order methods of smooth convex optimization with inexact oracle.Mathematical Programming, 146(1):37–75, 2014

Reference 15

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Observation 8f35f8b8-8485-45ce-9772-63c452f74990 · outbound

This paper cites Halpern iteration for near-optimal and parameter-free monotone inclusion and strong solutions to variational inequalities.Conference on Learning Theory, 2020.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Halpern iteration for near-optimal and parameter-free monotone inclusion and strong solutions to variational inequalities.Conference on Learning Theory, 2020

Reference 16

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Observation b733cfbb-7c63-4c5a-b088-914c58d2d5da · outbound

This paper cites Springer-Verlag, 2003.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Springer-Verlag, 2003

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:57.298896Z

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Observation 5d1eddbd-f8d9-49f0-b8fd-88e06ccf6a64 · outbound

This paper cites Accelerated gradient methods for nonconvex nonlinear and stochastic programming.Mathematical Programming, 156(1):59–99, 2016.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Accelerated gradient methods for nonconvex nonlinear and stochastic programming.Mathematical Programming, 156(1):59–99, 2016

Reference 18

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Observation c8efa343-914a-4ecb-a17f-fa76b07040bf · outbound

This paper cites Generative adversarial nets.Neural Information Processing Systems, 2014.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Generative adversarial nets.Neural Information Processing Systems, 2014

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:57.273524Z

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Observation 6de432b0-ef02-413c-8c90-750925592e3a · outbound

This paper cites Stochastic extragradient: General analysis and improved rates.International Conference on Artificial Intelligence and Statis- tics, 2022.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Stochastic extragradient: General analysis and improved rates.International Conference on Artificial Intelligence and Statis- tics, 2022

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:57.258072Z

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Observation 1070d2f6-f4a0-483a-b51b-e6130815421d · outbound

This paper cites Fixed points of nonexpanding maps.Bulletin of the American Mathematical Society, 73(6):957–961, 1967.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Fixed points of nonexpanding maps.Bulletin of the American Mathematical Society, 73(6):957–961, 1967

Reference 21

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Observation 4fe22a57-fa47-43da-93aa-8a62c2f50034 · outbound

This paper cites Accelerated proximal point method for maximally monotone operators.Mathe- matical Programming, 190(1–2):57–87, 2021.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Accelerated proximal point method for maximally monotone operators.Mathe- matical Programming, 190(1–2):57–87, 2021

Reference 22

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Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 23

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Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 24

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Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 25

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Observation 5829444e-f5de-4214-9f77-acc31bc9c702 · outbound

This paper cites Near-optimal sample complexity for MDPs via anchoring.Proceedings of the 42nd international conference on machine learning, 2025.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Near-optimal sample complexity for MDPs via anchoring.Proceedings of the 42nd international conference on machine learning, 2025

Reference 26

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Observation 0d113d78-1513-45a7-9639-0a3ce569dbb6 · outbound

This paper cites Fast extra gradient methods for smooth structured nonconvex– nonconcave minimax problems.Neural Information Processing Systems, 2021.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Fast extra gradient methods for smooth structured nonconvex– nonconcave minimax problems.Neural Information Processing Systems, 2021

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Observation 5122a7b6-5683-42a1-8f75-3374fe71fe08 · outbound

This paper cites PAGE: A simple and optimal probabilistic gradient estimator for nonconvex optimization.International Conference on Machine Learning, 2021.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization PAGE: A simple and optimal probabilistic gradient estimator for nonconvex optimization.International Conference on Machine Learning, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:57.137076Z

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Observation b6b60c5f-5faa-4aa4-a916-f2bb52e6852e · outbound

This paper cites On the convergence rate of the Halpern-iteration.Optimization Letters, 15(2):405– 418, 2021.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization On the convergence rate of the Halpern-iteration.Optimization Letters, 15(2):405– 418, 2021

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Observation b9786d49-2796-4ff0-8dc1-ab35a39297e3 · outbound

This paper cites A universal catalyst for first-order optimization.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization A universal catalyst for first-order optimization

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:57.108169Z

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Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 31

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Observation aa174ebd-eca1-42e5-bc18-13ed823eaa34 · outbound

This paper cites Stochastic gradient descent-ascent and consensus optimization for smooth games: Convergence analysis under expected co-coercivity.Neural Information Processing Systems, 2021.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Stochastic gradient descent-ascent and consensus optimization for smooth games: Convergence analysis under expected co-coercivity.Neural Information Processing Systems, 2021

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Observation 21630d7f-f6f8-4f16-b76e-60986c01d4e1 · outbound

This paper cites To- wards deep learning models resistant to adversarial attacks.International Conference on Learning Representations, 2018.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization To- wards deep learning models resistant to adversarial attacks.International Conference on Learning Representations, 2018

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:57.057820Z

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Observation 69ac14be-22d9-4050-9e43-a23073f9bc4d · outbound

This paper cites Mean value methods in iteration.Proceedings of the American Mathematical Society, 4(3):506–510, 1953.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Mean value methods in iteration.Proceedings of the American Mathematical Society, 4(3):506–510, 1953

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:57.041576Z

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Observation 5037ba4e-3bdf-40c6-9af0-5b892468da50 · outbound

This paper cites Foo, Vijay Chandrasekhar, and Georgios Piliouras.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Foo, Vijay Chandrasekhar, and Georgios Piliouras

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Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 36

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Observation fd586ae2-44e8-4b8e-b384-f54aa3318bcd · outbound

This paper cites Cocoercivity, smoothness and bias in variance-reduced stochastic gradient methods.Numerical Algorithms, 91(2):749–772, 2022.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Cocoercivity, smoothness and bias in variance-reduced stochastic gradient methods.Numerical Algorithms, 91(2):749–772, 2022

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Observation 813f6fb5-018f-40be-b1e5-5281450bb682 · outbound

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Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

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Observation 712a8ae9-39c0-4da8-91f1-09a8265fec21 · outbound

This paper cites Nguyen, Jie Liu, Katya Scheinberg, and Martin Tak´ aˇ c.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Nguyen, Jie Liu, Katya Scheinberg, and Martin Tak´ aˇ c

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Observation 3c1d2767-aa5d-411b-908e-a45f00873e8d · outbound

This paper cites Exact optimal accelerated complexity for fixed-point iterations.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Exact optimal accelerated complexity for fixed-point iterations

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Observation 3ebdc605-9956-4ea2-a444-239ce510bd7e · outbound

This paper cites Asymptotic Regularity of a Generalised Stochastic Halpern Scheme.Journal of Optimization Theory and Applications, 210(1):3, 2026.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Asymptotic Regularity of a Generalised Stochastic Halpern Scheme.Journal of Optimization Theory and Applications, 210(1):3, 2026

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:56.938535Z

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Observation e887f767-9197-4054-b36d-90d0bd62bc79 · outbound

This paper cites Tyrrell Rockafellar.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Tyrrell Rockafellar

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This paper cites Cam- bridge University Press, 2022.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Cam- bridge University Press, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:56.912954Z

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Observation 537882a6-227c-4629-8378-c86820f1b1d7 · outbound

This paper cites ODE Analysis of Stochastic Gradient Methods with Optimism and Anchoring for Minimax Problems.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization ODE Analysis of Stochastic Gradient Methods with Optimism and Anchoring for Minimax Problems

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Observation c1cf30be-8c97-4371-abf5-48020c28c9cb · outbound

This paper cites A first order method for solving convex bilevel optimization problems.SIAM Journal on Optimization, 27(2):640–660, 2017.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization A first order method for solving convex bilevel optimization problems.SIAM Journal on Optimization, 27(2):640–660, 2017

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Resolution: unresolved; no resolver link, observed 2026-08-16T00:26:56.590801Z

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Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:26:56.888058Z

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Observation 0ff27ff5-8155-4111-bfd4-20de5915ca36 · outbound

This paper cites Formes bilineaires coercitives sur les ensembles convexes.Comptes Rendus Hebdomadaires Des Seances De L Academie Des Sciences, 258(18):4413, 1964.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Formes bilineaires coercitives sur les ensembles convexes.Comptes Rendus Hebdomadaires Des Seances De L Academie Des Sciences, 258(18):4413, 1964

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:56.874206Z

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Observation 45d43a3d-16a4-40ac-9fbc-3aafa5820003 · outbound

This paper cites Suh, Jisun Park, and Ernest K.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Suh, Jisun Park, and Ernest K

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:56.859060Z

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Observation b0ba7c0d-5d15-41b7-ab3f-8f2783d15d5f · outbound

This paper cites From Halpern’s fixed-point iterations to Nesterov’s accelerated interpretations for root-finding problems.Computational Optimization and Applications, 87(1):181–218, 2024.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization From Halpern’s fixed-point iterations to Nesterov’s accelerated interpretations for root-finding problems.Computational Optimization and Applications, 87(1):181–218, 2024

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Observation b882eafd-41f2-46cd-bb4d-ad0e13bc0908 · outbound

This paper cites Halpern-Type Accelerated and Splitting Algorithms For Monotone Inclusions.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Halpern-Type Accelerated and Splitting Algorithms For Monotone Inclusions

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T00:26:56.614725Z

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Observation e6cf40e1-0f8a-4504-9a8d-3fcb85c4d45e · outbound

This paper cites Approximation of fixed points of nonexpansive mappings.Archiv der Mathe- matik, 58(5):486–491, 1992.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Approximation of fixed points of nonexpansive mappings.Archiv der Mathe- matik, 58(5):486–491, 1992

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:56.833375Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 450a733d-1099-415c-befd-8beda267b948 · outbound

This paper cites A Theory of Composition and Duality of Extremal Optimal Fixed-Point Algorithms.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization A Theory of Composition and Duality of Extremal Optimal Fixed-Point Algorithms

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-16T00:26:56.624497Z

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Observation dda1ab64-2737-41a9-beaa-af95a431f9e1 · outbound

This paper cites an unresolved cited work.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-16T00:26:56.629267Z

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Observation 4c5d9ed4-54af-46e9-8a35-8e00f2a88173 · outbound

This paper cites an unresolved cited work.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T00:26:56.633854Z

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Observation e0c7fc16-1dec-4a02-a35a-e66eccb66e38 · outbound

This paper cites Suh, and Ernest K.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Suh, and Ernest K

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:56.798096Z

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Observation 3d0f78d9-6e79-4e9b-9904-65cb12ecb9c0 · outbound

This paper cites H-invariance theory: A complete charac- terization of minimax optimal fixed-point algorithms.Accepted for publication in Mathematical Programming, 2025.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization H-invariance theory: A complete charac- terization of minimax optimal fixed-point algorithms.Accepted for publication in Mathematical Programming, 2025

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:56.783479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 69cd4e41-be33-453f-ad95-62f74678a47a · outbound

This paper cites Multiplayer federated learning: Reaching equilibrium with less communication.Advances in Neural Information Processing Systems, 2026.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Multiplayer federated learning: Reaching equilibrium with less communication.Advances in Neural Information Processing Systems, 2026

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:26:56.768884Z

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Observation 804b15de-0304-417e-947a-4e29e94d109c · outbound

This paper cites an unresolved cited work.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:26:56.751765Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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