GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds | Paper Citation Record

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GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds write newline

Reference 1

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This paper cites (2008), Optimization algorithms on matrix manifolds\/ , Princeton University Press.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2008), Optimization algorithms on matrix manifolds\/ , Princeton University Press

Reference 2

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This paper cites L., et al.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds L., et al

Reference 3

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This paper cites (2021), Predicting with proxies: Transfer learning in high dimension, Management Science\/ , 67, 2964--2984.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2021), Predicting with proxies: Transfer learning in high dimension, Management Science\/ , 67, 2964--2984

Reference 4

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This paper cites (2000), A model of inductive bias learning, Journal of artificial intelligence research\/ , 12, 149--198.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2000), A model of inductive bias learning, Journal of artificial intelligence research\/ , 12, 149--198

Reference 5

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GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Unresolved cited work

Reference 6

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This paper cites Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer

Reference 7

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Reference 8

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This paper cites (2021), Exploiting shared representations for personalized federated learning, in International conference on machine learning\/ , PMLR.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2021), Exploiting shared representations for personalized federated learning, in International conference on machine learning\/ , PMLR

Reference 9

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This paper cites (2008), Learning from Multiple Sources.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2008), Learning from Multiple Sources

Reference 10

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This paper cites (2020), The advantage of conditional meta-learning for biased regularization and fine tuning, Advances in Neural Information Processing Systems\/ , 33, 964--974.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2020), The advantage of conditional meta-learning for biased regularization and fine tuning, Advances in Neural Information Processing Systems\/ , 33, 964--974

Reference 11

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This paper cites (2022), Learning tensor representations for meta-learning, in International Conference on Artificial Intelligence and Statistics\/ , PMLR.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2022), Learning tensor representations for meta-learning, in International Conference on Artificial Intelligence and Statistics\/ , PMLR

Reference 12

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This paper cites (2014), Decaf: A deep convolutional activation feature for generic visual recognition, in International conference on machine learning\/ , PMLR.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2014), Decaf: A deep convolutional activation feature for generic visual recognition, in International conference on machine learning\/ , PMLR

Reference 13

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This paper cites Few-Shot Learning via Learning the Representation, Provably.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Few-Shot Learning via Learning the Representation, Provably

Reference 14

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This paper cites and Wang, K.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds and Wang, K

Reference 15

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This paper cites C., Feldman, V., Hu, L., and Talwar, K.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds C., Feldman, V., Hu, L., and Talwar, K

Reference 16

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This paper cites (2019), Scaling and benchmarking self-supervised visual representation learning, in Proceedings of the ieee/cvf International Conference on computer vision\/.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2019), Scaling and benchmarking self-supervised visual representation learning, in Proceedings of the ieee/cvf International Conference on computer vision\/

Reference 17

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This paper cites Robust angle-based transfer learning in high dimensions.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Robust angle-based transfer learning in high dimensions

Reference 18

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This paper cites H., and Duan, R.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds H., and Duan, R

Reference 19

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This paper cites Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning

Reference 20

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This paper cites (2022), Multi-task manifold learning for small sample size datasets, Neurocomputing\/ , 473, 138--157.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2022), Multi-task manifold learning for small sample size datasets, Neurocomputing\/ , 473, 138--157

Reference 21

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This paper cites (2010), A dirty model for multi-task learning, Advances in neural information processing systems\/ , 23.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2010), A dirty model for multi-task learning, Advances in neural information processing systems\/ , 23

Reference 22

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GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Unresolved cited work

Reference 23

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GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Unresolved cited work

Reference 24

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This paper cites (2020), On the sample complexity of adversarial multi-source pac learning, in International Conference on Machine Learning\/ , PMLR.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2020), On the sample complexity of adversarial multi-source pac learning, in International Conference on Machine Learning\/ , PMLR

Reference 25

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This paper cites and Orabona, F.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds and Orabona, F

Reference 26

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GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Unresolved cited work

Reference 27

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This paper cites (2023), Targeting underrepresented populations in precision medicine: A federated transfer learning approach, The Annals of Applied Statistics\/ , 17, 2970--2992.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2023), Targeting underrepresented populations in precision medicine: A federated transfer learning approach, The Annals of Applied Statistics\/ , 17, 2970--2992

Reference 28

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This paper cites T., and Li, H.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds T., and Li, H

Reference 29

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This paper cites On Hypothesis Transfer Learning of Functional Linear Models.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds On Hypothesis Transfer Learning of Functional Linear Models

Reference 30

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This paper cites Taking Advantage of Sparsity in Multi-Task Learning.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Taking Advantage of Sparsity in Multi-Task Learning

Reference 31

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GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Unresolved cited work

Reference 32

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This paper cites (2016), The benefit of multitask representation learning, Journal of Machine Learning Research\/ , 17, 1--32.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2016), The benefit of multitask representation learning, Journal of Machine Learning Research\/ , 17, 1--32

Reference 33

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This paper cites (2019), Pytorch: An imperative style, high-performance deep learning library, Advances in neural information processing systems\/ , 32.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2019), Pytorch: An imperative style, high-performance deep learning library, Advances in neural information processing systems\/ , 32

Reference 34

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This paper cites (2018), Do Outliers Ruin Collaboration? in International Conference on Machine Learning\/ , PMLR.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2018), Do Outliers Ruin Collaboration? in International Conference on Machine Learning\/ , PMLR

Reference 35

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This paper cites Learning Discrete Distributions from Untrusted Batches.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Learning Discrete Distributions from Untrusted Batches

Reference 36

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Observation d12e86c4-aa07-44f7-b8b7-f4ec97083e52 · outbound

This paper cites (2019), Transfusion: Understanding transfer learning for medical imaging, Advances in neural information processing systems\/ , 32.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2019), Transfusion: Understanding transfer learning for medical imaging, Advances in neural information processing systems\/ , 32

Reference 37

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This paper cites (2022), Transfer learning via representation learning, in Federated and Transfer Learning\/ , Springer, 233--257.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2022), Transfer learning via representation learning, in Federated and Transfer Learning\/ , Springer, 233--257

Reference 38

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GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Unresolved cited work

Reference 39

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Observation b7a20eb2-e83a-40e8-8761-8f6afe2eeb41 · outbound

This paper cites K., Jain, P., Netrapalli, P., and Oh, S.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds K., Jain, P., Netrapalli, P., and Oh, S

Reference 40

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This paper cites and Feng, Y.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds and Feng, Y

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:25.446473Z

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Observation de950419-6017-4188-8ef9-951a68030e86 · outbound

This paper cites Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness

Reference 42

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Observation 613161b7-9be9-4128-962e-6ec4d8e4fd5d · outbound

This paper cites (2022), Unsupervised multi-task and transfer learning on gaussian mixture models, arXiv preprint arXiv:2209.15224\/.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2022), Unsupervised multi-task and transfer learning on gaussian mixture models, arXiv preprint arXiv:2209.15224\/

Reference 43

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This paper cites (2021), Provable meta-learning of linear representations, in International Conference on Machine Learning\/ , PMLR.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2021), Provable meta-learning of linear representations, in International Conference on Machine Learning\/ , PMLR

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:25.432715Z

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Observation 647c552f-57d3-4956-9213-aeacc8749754 · outbound

This paper cites (2020), On the theory of transfer learning: The importance of task diversity, Advances in neural information processing systems\/ , 33, 7852--7862.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2020), On the theory of transfer learning: The importance of task diversity, Advances in neural information processing systems\/ , 33, 7852--7862

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:25.374560Z

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GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Unresolved cited work

Reference 46

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This paper cites Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling

Reference 47

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This paper cites M., Wilson, T.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds M., Wilson, T

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:25.114484Z

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Observation bd3e5a34-36ad-428b-9c04-1a587678eddf · outbound

This paper cites Multitask Learning and Bandits via Robust Statistics.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Multitask Learning and Bandits via Robust Statistics

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-16T00:43:24.675930Z

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Observation 5c723c4d-c837-4265-9d8e-a034844b771f · outbound

This paper cites and Yang, Q.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds and Yang, Q

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:25.099597Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5b532b74-699c-45ee-afb4-448c2badf66d · outbound

This paper cites an unresolved cited work.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:43:25.084673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ccf76da2-d004-4646-88c6-ab434b496893 · outbound

This paper cites SOFARI: High-Dimensional Manifold-Based Inference.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds SOFARI: High-Dimensional Manifold-Based Inference

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-16T00:43:24.655932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c8c5203d-8ca9-43e2-b236-da0e549d6f0a · outbound

This paper cites Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-16T00:43:24.560639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dba8f54e-ee9f-4b06-91b5-f66d4adfdc21 · outbound

This paper cites (2024), Doubly robust augmented model accuracy transfer inference with high dimensional features, Journal of the American Statistical Association\/ , 1--26.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds (2024), Doubly robust augmented model accuracy transfer inference with high dimensional features, Journal of the American Statistical Association\/ , 1--26

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:24.989381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Pith citing papers