SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning | Paper Citation Record

Observation 9e68bc43-b433-43d6-b937-0563dfcf9ddc · outbound

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

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Katyusha: The first direct acceleration of stochastic gradient methods.Journal of Machine Learning Research, 18(221):1–51, 2018

Reference 1

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This paper cites Natasha 2: Faster non-convex optimization than sgd.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Natasha 2: Faster non-convex optimization than sgd

Reference 2

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This paper cites Optimal black-box reductions between optimization objectives.Advances in Neural Information Processing Systems, 29, 2016.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Optimal black-box reductions between optimization objectives.Advances in Neural Information Processing Systems, 29, 2016

Reference 3

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This paper cites Variance reduction for faster non-convex optimization.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Variance reduction for faster non-convex optimization

Reference 4

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This paper cites A fast iterative shrinkage-thresholding algorithm for linear inverse problems.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning A fast iterative shrinkage-thresholding algorithm for linear inverse problems

Reference 5

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This paper cites On the convergence of block coordinate descent type methods.SIAM Journal on Optimization, 23(4):2037–2060, 2013.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning On the convergence of block coordinate descent type methods.SIAM Journal on Optimization, 23(4):2037–2060, 2013

Reference 6

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This paper cites A multi-batch l-bfgs method for machine learning.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning A multi-batch l-bfgs method for machine learning

Reference 7

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This paper cites Convergence rate analysis of a stochas- tic trust-region method via supermartingales.INFORMS Journal on Optimization, 1(2):92–119, 2019.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Convergence rate analysis of a stochas- tic trust-region method via supermartingales.INFORMS Journal on Optimization, 1(2):92–119, 2019

Reference 8

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This paper cites Exact and inexact subsampled Newton methods for optimization.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Exact and inexact subsampled Newton methods for optimization

Reference 9

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This paper cites A progressive batching l-bfgs method for machine learning.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning A progressive batching l-bfgs method for machine learning

Reference 10

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This paper cites On the use of stochastic Hessian information in optimization methods for machine learning.SIAM Journal on Optimization, 21(3):977–995, 2011.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning On the use of stochastic Hessian information in optimization methods for machine learning.SIAM Journal on Optimization, 21(3):977–995, 2011

Reference 11

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This paper cites SAN: stochastic average Newton algo- rithm for minimizing finite sums.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning SAN: stochastic average Newton algo- rithm for minimizing finite sums

Reference 12

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This paper cites SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives.Advances in Neural Information Processing Systems, 27, 2014.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives.Advances in Neural Information Processing Systems, 27, 2014

Reference 13

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This paper cites Stochastic variance-reduced newton: Accelerating finite-sum minimization with large batches.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Stochastic variance-reduced newton: Accelerating finite-sum minimization with large batches

Reference 14

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This paper cites Minimizing Quasi-Self-Concordant Functions by Gradient Regularization of Newton Method.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Minimizing Quasi-Self-Concordant Functions by Gradient Regularization of Newton Method

Reference 15

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This paper cites Convergence rates of sub-sampled Newton methods.Advances in Neural Information Processing Systems, 28, 2015.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Convergence rates of sub-sampled Newton methods.Advances in Neural Information Processing Systems, 28, 2015

Reference 16

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This paper cites Variable selection via nonconcave penalized likelihood and its oracle properties.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Variable selection via nonconcave penalized likelihood and its oracle properties

Reference 17

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This paper cites CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks

Reference 18

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Observation 822eb63e-a289-4d32-aa62-18d8d8647387 · outbound

This paper cites PROMISE: Preconditioned stochastic optimization methods by incorporating scalable curvature estimates.Journal of Machine Learning Research, 25(346):1–57, 2024.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning PROMISE: Preconditioned stochastic optimization methods by incorporating scalable curvature estimates.Journal of Machine Learning Research, 25(346):1–57, 2024

Reference 19

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This paper cites Sketchysgd: reliable stochastic opti- mizationviarandomizedcurvatureestimates.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Sketchysgd: reliable stochastic opti- mizationviarandomizedcurvatureestimates

Reference 20

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This paper cites Randomized nyström preconditioning.SIAM Journal on Matrix Analysis and Applications, 44(2):718–752, 2023.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Randomized nyström preconditioning.SIAM Journal on Matrix Analysis and Applications, 44(2):718–752, 2023

Reference 21

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This paper cites Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients

Reference 22

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This paper cites RSN: Randomized Subspace Newton.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning RSN: Randomized Subspace Newton

Reference 23

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Observation ede529d4-2645-4d74-a85d-4841c5b6987b · outbound

This paper cites SGD: General analysis and improved rates.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning SGD: General analysis and improved rates

Reference 24

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This paper cites Datamodels: Predicting predictions from training data.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Datamodels: Predicting predictions from training data

Reference 25

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Observation 4be55c71-9cf4-415c-8913-988f25c2c4e6 · outbound

This paper cites Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization.Advances in Neural Information Processing Systems, 29, 2016.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization.Advances in Neural Information Processing Systems, 29, 2016

Reference 26

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This paper cites Accelerating stochastic gradient descent using predictive variance reduction.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Accelerating stochastic gradient descent using predictive variance reduction

Reference 27

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This paper cites Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients

Reference 28

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This paper cites Sub-sampled cubic regularization for non-convex optimization.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Sub-sampled cubic regularization for non-convex optimization

Reference 29

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This paper cites Do subsampled newton methods work for high-dimensional data? In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 4723–4730, 2020.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Do subsampled newton methods work for high-dimensional data? In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 4723–4730, 2020

Reference 30

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This paper cites On faster convergence of cyclic block coordinate descent-type methods for strongly convex minimization, 2017.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning On faster convergence of cyclic block coordinate descent-type methods for strongly convex minimization, 2017

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This paper cites Activityidentificationandlocallinearconvergenceofforward– backward-type methods.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Activityidentificationandlocallinearconvergenceofforward– backward-type methods

Reference 32

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This paper cites Local convergence properties of douglas–rachford and al- ternating direction method of multipliers.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Local convergence properties of douglas–rachford and al- ternating direction method of multipliers

Reference 33

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This paper cites Catalyst acceleration for first-order convex optimization: from theory to practice.Journal of Machine Learning Research, 18(212):1–54, 2018.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Catalyst acceleration for first-order convex optimization: from theory to practice.Journal of Machine Learning Research, 18(212):1–54, 2018

Reference 34

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This paper cites Controlburn: Featureselectionbysparseforests.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Controlburn: Featureselectionbysparseforests

Reference 35

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This paper cites On the complexity analysis of randomized block-coordinate descent methods.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning On the complexity analysis of randomized block-coordinate descent methods

Reference 36

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Observation 4c72f6c6-f585-4445-8a1f-a027d88d71ea · outbound

This paper cites Fast and furious convergence: Stochastic second order methods under interpolation.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Fast and furious convergence: Stochastic second order methods under interpolation

Reference 37

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Observation d0846b56-c4b7-498d-b61e-89d119a91f5f · outbound

This paper cites A linearly-convergent stochastic L-BFGS algorithm.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning A linearly-convergent stochastic L-BFGS algorithm

Reference 38

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Observation 22e3fe70-4d12-4f0f-98b5-e4003f092352 · outbound

This paper cites Gradientmethodsforminimizingcompositefunctions.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Gradientmethodsforminimizingcompositefunctions

Reference 39

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This paper cites Springer, 2018.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Springer, 2018

Reference 40

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This paper cites Catalyst for gradient-based nonconvex optimization.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Catalyst for gradient-based nonconvex optimization

Reference 41

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Observation 88a5d890-cd2a-4cdf-91dd-9df99123ceff · outbound

This paper cites Neural networks are convex regularizers: Exact polynomial-time convex optimization formulations for two-layer networks.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Neural networks are convex regularizers: Exact polynomial-time convex optimization formulations for two-layer networks

Reference 42

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Observation e1faad9a-185a-405a-94a1-4588e97e2b2d · outbound

This paper cites Newton sketch: A near linear-time optimization algorithm with linear-quadratic convergence.SIAM Journal on Optimization, 27(1):205–245, 2017.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Newton sketch: A near linear-time optimization algorithm with linear-quadratic convergence.SIAM Journal on Optimization, 27(1):205–245, 2017

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.800541Z

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Observation 51e4e687-5d3e-4e42-a322-fc59cb419350 · outbound

This paper cites Local convergence properties of saga/prox-svrg and acceleration.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Local convergence properties of saga/prox-svrg and acceleration

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.786940Z

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Observation 9b28f786-59b8-4c7f-be47-32f69906d738 · outbound

This paper cites Challenges in Training PINNs: A Loss Landscape Perspective.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Challenges in Training PINNs: A Loss Landscape Perspective

Reference 45

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Observation 1358ea6c-7ea6-49c7-8c7f-f4dcfe89a032 · outbound

This paper cites Stochastic variance reduction for nonconvex optimization.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Stochastic variance reduction for nonconvex optimization

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.772703Z

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Observation 51fb3acd-5891-4c99-886c-03018d6f9d5b · outbound

This paper cites Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function.Mathematical Programming, 144(1):1–38, 2014.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function.Mathematical Programming, 144(1):1–38, 2014

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.758247Z

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Observation bee0719c-9b9c-4bd5-856c-b3572903ea0a · outbound

This paper cites Newton-mr: Inexact Newton method with minimum residual sub-problem solver.EURO Journal on Computational Optimization, 10:100035, 2022.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Newton-mr: Inexact Newton method with minimum residual sub-problem solver.EURO Journal on Computational Optimization, 10:100035, 2022

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.744341Z

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 6a3f4a9c-42e1-4a0a-8ecb-7cbed142ca58 · outbound

This paper cites Sub-sampled Newton methods.Mathematical Program- ming, 174(1):293–326, 2019.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Sub-sampled Newton methods.Mathematical Program- ming, 174(1):293–326, 2019

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.730311Z

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

Observation 4b149ffe-ce9a-4b84-aed7-db6547ff494a · outbound

This paper cites Are we there yet? manifold identification of gradient-related proximal methods.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Are we there yet? manifold identification of gradient-related proximal methods

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.716546Z

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 461a9f56-d897-48d4-97df-346762fd0c1a · outbound

This paper cites Stochastic cubic regular- ization for fast nonconvex optimization.Advances in Neural Information Processing Systems, 31, 2018.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Stochastic cubic regular- ization for fast nonconvex optimization.Advances in Neural Information Processing Systems, 31, 2018

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.702816Z

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 4ea55321-1ea8-45d4-9230-6285a5f6301b · outbound

This paper cites Fixed-rank approximation of a positive- semidefinite matrix from streaming data.Advances in Neural Information Processing Systems, 30, 2017.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Fixed-rank approximation of a positive- semidefinite matrix from streaming data.Advances in Neural Information Processing Systems, 30, 2017

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.688095Z

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 ff7f042c-43b3-419a-a9b6-b0760c0fb73d · outbound

This paper cites Fixed-rank approximation of a positive- semidefinite matrix from streaming data.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Fixed-rank approximation of a positive- semidefinite matrix from streaming data

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.673685Z

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 3d11e5fc-25f6-46cd-8ea9-5351d33fb086 · outbound

This paper cites Utilizing second order information in minibatch stochastic variance reduced proximal iterations.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Utilizing second order information in minibatch stochastic variance reduced proximal iterations

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.659030Z

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 7ef791b8-4365-4611-a18a-cef1a3867b6c · outbound

This paper cites A proximal stochastic gradient method with progressive variance reduction.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning A proximal stochastic gradient method with progressive variance reduction

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.644556Z

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 d0fd77f3-eb6f-4161-8fd5-b16f2543c2d3 · outbound

This paper cites Newton-type methods for non-convex optimization under inexact Hessian information.Mathematical Programming, 184(1-2):35–70, 2020.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Newton-type methods for non-convex optimization under inexact Hessian information.Mathematical Programming, 184(1-2):35–70, 2020

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.630801Z

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 908e22a2-c2a3-4c6f-b15e-51dd3b81a939 · outbound

This paper cites Inexact nonconvex Newton-type methods.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Inexact nonconvex Newton-type methods

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.615588Z

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 4e49ec12-b3b7-4f03-8809-009a2e2a39ad · outbound

This paper cites Inexact newton-cg algo- rithms with complexity guarantees.IMA Journal of Numerical Analysis, 43(3):1855–1897, 2023.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Inexact newton-cg algo- rithms with complexity guarantees.IMA Journal of Numerical Analysis, 43(3):1855–1897, 2023

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.600862Z

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 74c18bc2-5817-4ba4-9fc6-10996659873d · outbound

This paper cites Approximate Newton methods.Journal of Machine Learning Research, 22(66):1–41, 2021.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Approximate Newton methods.Journal of Machine Learning Research, 22(66):1–41, 2021

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.586338Z

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 f44818ec-c562-46fd-93f4-c1474ae4eec4 · outbound

This paper cites Sketched Newton–Raphson.SIAM Journal on Opti- mization, 32(3):1555–1583, 2022.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Sketched Newton–Raphson.SIAM Journal on Opti- mization, 32(3):1555–1583, 2022

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.571743Z

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 72ce393e-48e7-497b-bfdc-7971da144e42 · outbound

This paper cites R 1 Sm S−1X s=0 mX k=1 ˆw(s) k ! − R(w⋆) # ≤ ∥w0 − w⋆∥2 P (0) 0 + (2η − η2) (R(w0) − R(w⋆)) . Rearranging, we find that E.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning R 1 Sm S−1X s=0 mX k=1 ˆw(s) k ! − R(w⋆) # ≤ ∥w0 − w⋆∥2 P (0) 0 + (2η − η2) (R(w0) − R(w⋆)) . Rearranging, we find that E

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:56:23.556937Z

Source-reported events for the cited work

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Observation f4810a62-46e3-42a4-a642-79798e660d90 · outbound

This paper cites an unresolved cited work.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-10T13:56:23.540583Z

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 61192d3f-d821-434e-aedd-8ce6ab668f65 · outbound

This paper cites an unresolved cited work.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-10T13:56:23.526213Z

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 e39a1135-66c4-49f6-9d93-b2bb1e85618e · outbound

This paper cites an unresolved cited work.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-10T13:56:23.511748Z

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 d130616d-86fd-4db8-b97c-d05ea19e80e9 · outbound

This paper cites an unresolved cited work.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Unresolved cited work

Reference 65

Resolution: parse uncertain; raw_fallback, observed 2026-08-10T13:56:23.496927Z

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 b41cb540-f407-4c85-be96-85ca7897557d · outbound

This paper cites an unresolved cited work.

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-10T13:56:23.482668Z

Source-reported events for the cited work

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