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Paper Citation Record · LEDGER

Probabilistic Residual Learning for Online Recommendations

As of 19 August 2026, this Paper Citation Record lists 100 of 155 outbound references and 0 inbound Pith citation observations for arXiv:2607.20863.

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

pith.paper-citation-record.v1
2607.20863 v1

Coverage vector

measured 100 of 155 reference resolution

Typed states for the displayed outbound observations.

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measured 100 of 100 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

100 of 155 outbound references displayed

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External citation measurements

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Outbound references

Observation 7102421f-02ff-416b-bcda-f798ed878af0 · outbound

This paper cites an unresolved cited work.

Probabilistic Residual Learning for Online Recommendations Unresolved cited work

Reference 1

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Observation 89ae2444-6192-429c-8a3a-14b8135f5ada · outbound

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Probabilistic Residual Learning for Online Recommendations Unresolved cited work

Reference 2

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Probabilistic Residual Learning for Online Recommendations Unresolved cited work

Reference 4

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Probabilistic Residual Learning for Online Recommendations Publication quality tables in

Reference 5

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Probabilistic Residual Learning for Online Recommendations Unresolved cited work

Reference 6

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Reference 8

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Probabilistic Residual Learning for Online Recommendations Unresolved cited work

Reference 9

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Probabilistic Residual Learning for Online Recommendations TKDE , year=

Reference 10

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Probabilistic Residual Learning for Online Recommendations CVPR , pages=

Reference 11

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Probabilistic Residual Learning for Online Recommendations KDD , pages=

Reference 12

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Observation e9acca34-90a1-45f0-ad93-e4bb543edf90 · outbound

This paper cites ECCV , volume =.

Probabilistic Residual Learning for Online Recommendations ECCV , volume =

Reference 13

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Observation fb20ea81-3552-4e16-aaa9-347cab3bcad5 · outbound

This paper cites Joty and Muhammad Imran , editor =.

Probabilistic Residual Learning for Online Recommendations Joty and Muhammad Imran , editor =

Reference 14

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Probabilistic Residual Learning for Online Recommendations SIU , pages=

Reference 15

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Probabilistic Residual Learning for Online Recommendations Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

Reference 16

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Probabilistic Residual Learning for Online Recommendations GluonTS: Probabilistic Time Series Models in Python

Reference 17

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Probabilistic Residual Learning for Online Recommendations ICML , year=

Reference 18

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Probabilistic Residual Learning for Online Recommendations RecSys , pages=

Reference 19

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Observation 69414ae8-c0bb-44c4-bcb7-8a11a0969b00 · outbound

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Probabilistic Residual Learning for Online Recommendations NIPS , pages=

Reference 20

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Probabilistic Residual Learning for Online Recommendations Convolutional

Reference 21

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Probabilistic Residual Learning for Online Recommendations KDD , pages=

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Probabilistic Residual Learning for Online Recommendations NIPS , pages=

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Probabilistic Residual Learning for Online Recommendations TDKE , volume=

Reference 24

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Probabilistic Residual Learning for Online Recommendations CSUR , volume=

Reference 25

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Probabilistic Residual Learning for Online Recommendations AAAI StarAI Workshop , year=

Reference 26

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Probabilistic Residual Learning for Online Recommendations Frontiers in genetics , volume=

Reference 27

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Probabilistic Residual Learning for Online Recommendations ICML , year =

Reference 28

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Probabilistic Residual Learning for Online Recommendations DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction

Reference 29

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Probabilistic Residual Learning for Online Recommendations 2017 , school=

Reference 30

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Probabilistic Residual Learning for Online Recommendations 2022 , booktitle=

Reference 31

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Probabilistic Residual Learning for Online Recommendations Nature medicine , year=

Reference 32

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Probabilistic Residual Learning for Online Recommendations Nature medicine , volume=

Reference 33

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Observation 56558e95-4bc4-449b-84f5-59c76bf7a520 · outbound

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Probabilistic Residual Learning for Online Recommendations Revisiting k-means: New Algorithms via Bayesian Nonparametrics

Reference 34

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Probabilistic Residual Learning for Online Recommendations Deep Learning Recommendation Model for Personalization and Recommendation Systems

Reference 35

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Probabilistic Residual Learning for Online Recommendations Acm transactions on interactive intelligent systems (tiis) , volume=

Reference 36

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Probabilistic Residual Learning for Online Recommendations Proceedings of the 26th international conference on world wide web , pages=

Reference 37

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Probabilistic Residual Learning for Online Recommendations Computer Speech & Language , volume=

Reference 38

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Probabilistic Residual Learning for Online Recommendations Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval , pages=

Reference 39

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Probabilistic Residual Learning for Online Recommendations Relational stacked denoising autoencoder for tag recommendation , booktitle =

Reference 40

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Probabilistic Residual Learning for Online Recommendations TKDE , volume=

Reference 41

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source=arxiv_source observed=2026-08-01T09:14:46.601007Z digest=sha256:57c55421f2e791ac81a5f07b8c3ad068508e3c2d7207e0c56fa1ae5621fd92d0

Observation 15bac448-7686-4d7f-a491-6882a7392020 · outbound

This paper cites Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining , pages=.

Probabilistic Residual Learning for Online Recommendations Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining , pages=

Reference 42

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source=arxiv_source observed=2026-08-01T09:14:46.687385Z digest=sha256:bfbcbeb6324ca3557f3e2f9e86d7f5028c6c92a233dc897264215775bd61e99c

Observation 504896d8-5e47-4de2-9f08-d03d427580f2 · outbound

This paper cites Proceedings of the 30th ACM International Conference on Information & Knowledge Management , pages=.

Probabilistic Residual Learning for Online Recommendations Proceedings of the 30th ACM International Conference on Information & Knowledge Management , pages=

Reference 43

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source=arxiv_source observed=2026-08-01T09:14:46.785321Z digest=sha256:3daf61aec1afb7bc36eded01f083c5e707568616ac9f4d3ac712a85bb65f1b91

Observation eb23c5af-cae9-430d-b299-307e39fc1c42 · outbound

This paper cites IJCAI , pages =.

Probabilistic Residual Learning for Online Recommendations IJCAI , pages =

Reference 44

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source=arxiv_source observed=2026-08-01T09:14:46.856838Z digest=sha256:20c40bc4088a1731c78e4d1f6a4da42a1faa24c012183d8f3363412cab8a99c7

Observation 85f796c3-9a83-40d5-97c5-fdee37609970 · outbound

This paper cites Bell and Chris Volinsky , title =.

Probabilistic Residual Learning for Online Recommendations Bell and Chris Volinsky , title =

Reference 45

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source=arxiv_source observed=2026-08-01T09:14:46.919341Z digest=sha256:1c067455056c7c9a1b3b028a97c20aa6ed19ed11b964dc3298481dc61866e486

Observation 53d50aea-174b-4717-a5ea-5b9a1cba1cd7 · outbound

This paper cites Session-based Recommendations with Recurrent Neural Networks , booktitle =.

Probabilistic Residual Learning for Online Recommendations Session-based Recommendations with Recurrent Neural Networks , booktitle =

Reference 46

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source=arxiv_source observed=2026-08-01T09:14:46.975655Z digest=sha256:37e28aa11f38ee3af531e255e623af1fd14c4b1c7b9e2b805c9b1c6cc5cdb49a

Observation 73babdf7-5f30-4aec-af42-f33bf7776e5b · outbound

This paper cites KDD , year =.

Probabilistic Residual Learning for Online Recommendations KDD , year =

Reference 47

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source=arxiv_source observed=2026-08-01T09:14:47.079258Z digest=sha256:8004ec01e9edbd6e2741eca0f4700c79ca4b0f6e23c8f35686d5fefc2055489c

Observation 04f1f0a4-0b88-41dc-b43a-3ba31e087360 · outbound

This paper cites Zico Kolter and Vladlen Koltun , title =.

Probabilistic Residual Learning for Online Recommendations Zico Kolter and Vladlen Koltun , title =

Reference 48

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source=arxiv_source observed=2026-08-01T09:14:47.139729Z digest=sha256:7998461158ada43923d5567ca296ea16c164d4eaa5766dd5bad8d58cef6872d1

Observation 9bcebe06-79b2-4eb2-8221-5f7fcd0f4684 · outbound

This paper cites Proceedings of the VLDB Endowment , volume=.

Probabilistic Residual Learning for Online Recommendations Proceedings of the VLDB Endowment , volume=

Reference 49

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source=arxiv_source observed=2026-08-01T09:14:47.193280Z digest=sha256:bd5b379d58b133a51d1a7fb1a50ab39d0141bc05b97100d93c0d8dc6d09864b7

Observation 7e37c1ab-a5a4-4015-98b2-3267c896195b · outbound

This paper cites NIPS , pages=.

Probabilistic Residual Learning for Online Recommendations NIPS , pages=

Reference 50

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source=arxiv_source observed=2026-08-01T09:14:47.279982Z digest=sha256:0b9f0e69f808daabf12c0f51b12843aee3ba8711d5e42f163d70cc956286c920

Observation 326f6817-0ca6-4b3a-a1d0-280954a3464a · outbound

This paper cites Maddix and Jan Gasthaus and Dean Foster and Tim Januschowski , title =.

Probabilistic Residual Learning for Online Recommendations Maddix and Jan Gasthaus and Dean Foster and Tim Januschowski , title =

Reference 51

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source=arxiv_source observed=2026-08-01T09:14:47.397722Z digest=sha256:0b4950c50dd4b8b0ab38bdf511a42889626be1539a612efc5cd0770fbb926335

Observation 6e4925bc-ad09-4d98-88fc-3394cb9f321f · outbound

This paper cites CoRR , volume =.

Probabilistic Residual Learning for Online Recommendations CoRR , volume =

Reference 52

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source=arxiv_source observed=2026-08-01T09:14:47.450196Z digest=sha256:8bccc41489546105d4e75515099b48a65f9cd8ae5e4e9889df4c4c80fadb6332

Observation 311310d3-9768-418f-879b-f2f2fe04e1c8 · outbound

This paper cites IEEE transactions on power systems , volume=.

Probabilistic Residual Learning for Online Recommendations IEEE transactions on power systems , volume=

Reference 53

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source=arxiv_source observed=2026-08-01T09:14:47.507554Z digest=sha256:4b1b3608039f2933f36d479d134568ef75ccb313c87e26842009ff45fae6253b

Observation 735294d2-9bbe-43a8-853a-d6db643527e6 · outbound

This paper cites international conference on machine learning , pages=.

Probabilistic Residual Learning for Online Recommendations international conference on machine learning , pages=

Reference 54

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source=arxiv_source observed=2026-08-01T09:14:47.597345Z digest=sha256:a725c3e4f3fcb1aba2235e469450b548bc3658768322a19537b4eeb690bf0525

Observation f135df45-65c9-4bae-adb4-e4d3d3e4bec4 · outbound

This paper cites Advances in neural information processing systems , pages=.

Probabilistic Residual Learning for Online Recommendations Advances in neural information processing systems , pages=

Reference 55

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source=arxiv_source observed=2026-08-01T09:14:47.685190Z digest=sha256:22f04ac6838d06f16626b8bb99881864e9a60a096d41895756c64345afa82a5e

Observation 435bc91e-193d-4aec-b6dc-fc9955c12ee8 · outbound

This paper cites Natural-Parameter Networks:.

Probabilistic Residual Learning for Online Recommendations Natural-Parameter Networks:

Reference 56

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source=arxiv_source observed=2026-08-01T09:14:47.741651Z digest=sha256:cecd1dda66bd098f6f0c8a439606faaa1b4b91cc9659348388d101d61242bc70

Observation c26a12c9-b281-4e8a-b1ad-07c240c58132 · outbound

This paper cites Auto-Encoding Variational Bayes.

Probabilistic Residual Learning for Online Recommendations Auto-Encoding Variational Bayes

Reference 57

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source=arxiv_source observed=2026-08-01T09:14:47.793978Z digest=sha256:ac88d7a9867a9991af6a572b6b1601273c5d4ed6c6b4b4672a4ad153c2dd43b9

Observation de044a53-1773-49df-931e-989ffdcf9825 · outbound

This paper cites 2013 , publisher=.

Probabilistic Residual Learning for Online Recommendations 2013 , publisher=

Reference 58

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source=arxiv_source observed=2026-08-01T09:14:47.879308Z digest=sha256:77597a008fcdbde23319e97c764eb926c07935fbed2a8ccdc11e35c2bcffcc0e

Observation 8dc2ff36-1bac-4f3e-9ca5-189ca1ea431d · outbound

This paper cites Kingma and Jimmy Ba , title =.

Probabilistic Residual Learning for Online Recommendations Kingma and Jimmy Ba , title =

Reference 59

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source=arxiv_source observed=2026-08-01T09:14:47.967661Z digest=sha256:b04a772e1f978422efece3412dcc427ed625ef9d92367a539dc88d9b81e1d821

Observation 40dc1c29-0d7b-4ac9-ad08-fd89b0e8e600 · outbound

This paper cites an unresolved cited work.

Probabilistic Residual Learning for Online Recommendations Unresolved cited work

Reference 60

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source=arxiv_source observed=2026-08-01T09:14:48.074644Z digest=sha256:0d99b7e83b4fcd4666cd381cca61624379dac48cbae0da085fdf35ecfe0c065b

Observation 49fddea9-8e5c-4074-acfe-e7091a2ba77c · outbound

This paper cites Gordon , title =.

Probabilistic Residual Learning for Online Recommendations Gordon , title =

Reference 61

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source=arxiv_source observed=2026-08-01T09:14:48.162576Z digest=sha256:64e348da403ff7dc56adc008feb18faf9355ee66987131c52df9471b45c97228

Observation 23e93cec-4b85-4f5a-9e35-74c523b91d66 · outbound

This paper cites VLDB , volume=.

Probabilistic Residual Learning for Online Recommendations VLDB , volume=

Reference 62

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source=arxiv_source observed=2026-08-01T09:14:48.224245Z digest=sha256:4eac7cdfac6c69e9b34d1d7e8899107a711e99fab999b3f78ac62499ccf7f791

Observation 4a01a87b-b417-402a-a6bc-769593e0fbb7 · outbound

This paper cites AAAI , pages =.

Probabilistic Residual Learning for Online Recommendations AAAI , pages =

Reference 63

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source=arxiv_source observed=2026-08-01T09:14:48.280797Z digest=sha256:35e70e6ba18a4dcf7b4d86e7b09452a0bd38c67ded049e12f050d93d8b1f11b6

Observation 05e6fd5e-ebb5-4be4-930f-b108e03fae83 · outbound

This paper cites Adversarial Multiple Source Domain Adaptation , booktitle =.

Probabilistic Residual Learning for Online Recommendations Adversarial Multiple Source Domain Adaptation , booktitle =

Reference 64

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source=arxiv_source observed=2026-08-01T09:14:48.338503Z digest=sha256:5bc8c36a411dcea362cde45e912bdec32fdfde4bb57d3f6aac7afd4a279ae560

Observation 8fe4cb2a-1bbe-46f9-80a9-517af2642fd5 · outbound

This paper cites Castillo and Rama Chellappa , title =.

Probabilistic Residual Learning for Online Recommendations Castillo and Rama Chellappa , title =

Reference 65

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source=arxiv_source observed=2026-08-01T09:14:48.424716Z digest=sha256:892e2210b2ba57b0bb07ad8f05fbb6b5aea7e4dd608c94971cf029cfe61b916e

Observation 1cf07320-a93f-483e-a072-632e79b2ea97 · outbound

This paper cites CVPR , pages =.

Probabilistic Residual Learning for Online Recommendations CVPR , pages =

Reference 66

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source=arxiv_source observed=2026-08-01T09:14:48.500545Z digest=sha256:9c3889856f2cebbae6ddaab94bbaf0ae9dbfa7f915e4e157a453cf8e262f5b5c

Observation 94459d9c-4279-48e3-947f-1e5faf026041 · outbound

This paper cites Jordan , title =.

Probabilistic Residual Learning for Online Recommendations Jordan , title =

Reference 67

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source=arxiv_source observed=2026-08-01T09:14:48.570548Z digest=sha256:d97f729810dc9ca6508900e05dbf7d7961cdf9dd287d2a55cf50f3426cc488f4

Observation c3e61ee0-d61d-47f5-86bd-b217602d4326 · outbound

This paper cites TKDE , volume=.

Probabilistic Residual Learning for Online Recommendations TKDE , volume=

Reference 68

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source=arxiv_source observed=2026-08-01T09:14:48.622278Z digest=sha256:c303739ba9ae4b6f7573bb347273266372093dc008d736faa368b0c52aa0dfc1

Observation 5b3c251c-7723-4cce-960a-e790024106c7 · outbound

This paper cites JAMA , volume=.

Probabilistic Residual Learning for Online Recommendations JAMA , volume=

Reference 69

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source=arxiv_source observed=2026-08-01T09:14:48.677827Z digest=sha256:95d34436cb0854b7fa80cdb8ab54d4a5bb3d3cb850625242457c718d63a72d6f

Observation 54a22bf1-e7f0-4b0c-8509-8861f4947e21 · outbound

This paper cites JAMA , volume=.

Probabilistic Residual Learning for Online Recommendations JAMA , volume=

Reference 70

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source=arxiv_source observed=2026-08-01T09:14:48.757084Z digest=sha256:97c5b484f329b6917f3131ce2cab0d7c35ccf996f57924e0100db03674f5a648

Observation 2d7449aa-f3f7-4173-baa9-412ee5df1511 · outbound

This paper cites Sleep , volume=.

Probabilistic Residual Learning for Online Recommendations Sleep , volume=

Reference 71

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source=arxiv_source observed=2026-08-01T09:14:48.814773Z digest=sha256:28b7ed01b568325e20fb6586f2c9a87a8e12c4eb1b7d5f8aad1c4d62855798bf

Observation f15d2607-7391-4b19-8ba8-6a4548524837 · outbound

This paper cites NIPS , pages=.

Probabilistic Residual Learning for Online Recommendations NIPS , pages=

Reference 72

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source=arxiv_source observed=2026-08-01T09:14:48.872147Z digest=sha256:0802c3cecf8c8058e53d15faf1d98b52b660a93537da91448975e038bb80ee5d

Observation 7c94f725-8df4-4e18-9f2b-1703e523bf37 · outbound

This paper cites 1994 , publisher=.

Probabilistic Residual Learning for Online Recommendations 1994 , publisher=

Reference 73

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source=arxiv_source observed=2026-08-01T09:14:48.927051Z digest=sha256:8d741e713b17f7fc991d3b4ef1bcd0af65a600cc70bde32b4e19001d4e6d2ae4

Observation a7d5cbea-e731-4578-9815-7c2d068ed5bf · outbound

This paper cites NIPS , pages=.

Probabilistic Residual Learning for Online Recommendations NIPS , pages=

Reference 74

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source=arxiv_source observed=2026-08-01T09:14:48.987678Z digest=sha256:92585cdb25dbb644ac4d0a6a46dbb734a183f6d116f20004bf416ae0bbe92c2f

Observation e5e77651-9979-401f-bd96-e896e17763d1 · outbound

This paper cites Jaakkola and Matt T.

Probabilistic Residual Learning for Online Recommendations Jaakkola and Matt T

Reference 75

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source=arxiv_source observed=2026-08-01T09:14:49.070440Z digest=sha256:49fe84d50b56a89bb3b048ec5efc69723a9ccc3cdf7c1c70afc9be87eac7c92e

Observation 9413b0b4-a819-4b4b-adfc-f54f9d5be41c · outbound

This paper cites ICCV workshop on Transferring and Adapting Source Knowledge in Computer Vision (TASK-CV) , pages =.

Probabilistic Residual Learning for Online Recommendations ICCV workshop on Transferring and Adapting Source Knowledge in Computer Vision (TASK-CV) , pages =

Reference 76

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source=arxiv_source observed=2026-08-01T09:14:49.127895Z digest=sha256:44f5b8b7c0636fcd75510b449da48f0628278294636dfa01c33157d969496afa

Observation a4632dbf-39ff-426b-8b74-f34fc82f33cf · outbound

This paper cites Machine learning , volume=.

Probabilistic Residual Learning for Online Recommendations Machine learning , volume=

Reference 77

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source=arxiv_source observed=2026-08-01T09:14:49.198040Z digest=sha256:58e7e33e87728bc8f5b32374d76e193876ee8086439f49089f5bdf393e4c7dfd

Observation d3fe33c5-52e1-4fea-be3d-6c3fc8f772f0 · outbound

This paper cites TNN , volume=.

Probabilistic Residual Learning for Online Recommendations TNN , volume=

Reference 78

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source=arxiv_source observed=2026-08-01T09:14:49.271453Z digest=sha256:3541957f5b6142d79eb0db72ec3949745e693085078a6cbe05a3154752813029

Observation aa81fd12-f902-4f52-93ed-128a455a8022 · outbound

This paper cites Deep Domain Confusion: Maximizing for Domain Invariance.

Probabilistic Residual Learning for Online Recommendations Deep Domain Confusion: Maximizing for Domain Invariance

Reference 79

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source=arxiv_source observed=2026-08-01T09:14:49.333988Z digest=sha256:daff72ff1a74fb4d931bd2ae2a4809e4975ab5c53cd9b1a08202facadb2e6ff9

Observation 58db1a45-0799-4cff-86f9-f0a9ebadbf12 · outbound

This paper cites CVPR , pages=.

Probabilistic Residual Learning for Online Recommendations CVPR , pages=

Reference 80

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source=arxiv_source observed=2026-08-01T09:14:49.413173Z digest=sha256:8507583ea76244c50d4c70b8b4d218a8b76af2e9f87fbfa66d0ec58f1edf69c2

Observation a5eaa36d-dc72-43c5-bc28-bfb168f30cce · outbound

This paper cites Bridging Theory and Algorithm for Domain Adaptation.

Probabilistic Residual Learning for Online Recommendations Bridging Theory and Algorithm for Domain Adaptation

Reference 81

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source=arxiv_source observed=2026-08-01T09:14:49.463952Z digest=sha256:06400a5c5ff5f0d29c145e81183bf04ea21fc0da8b59a4a7e3e78b2928dd54a3

Observation a09a5e18-0f24-41b1-a2e9-d2a6527a4d0d · outbound

This paper cites Domain-Invariant Adversarial Learning for Unsupervised Domain Adaption.

Probabilistic Residual Learning for Online Recommendations Domain-Invariant Adversarial Learning for Unsupervised Domain Adaption

Reference 82

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source=arxiv_source observed=2026-08-01T09:14:49.582292Z digest=sha256:5b9cbbe91fee77f70e070aeaf7c23e4e86fcdc1d6d00ec19058884a6b2343033

Observation 4a63249b-f368-4c44-ab25-8db5fe325e48 · outbound

This paper cites Domain Adaptation for Visual Applications: A Comprehensive Survey.

Probabilistic Residual Learning for Online Recommendations Domain Adaptation for Visual Applications: A Comprehensive Survey

Reference 83

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source=arxiv_source observed=2026-08-01T09:14:49.672153Z digest=sha256:9d833a63be4c44c41eabf6b6c9191879d82b35ecb0a655bb54c661abea7cfa6b

Observation 21de53b0-3cbe-438f-b7e3-769d606f14cc · outbound

This paper cites CVPR , pages=.

Probabilistic Residual Learning for Online Recommendations CVPR , pages=

Reference 84

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Observation 6b57b201-f459-48d1-a424-82760c204d05 · outbound

This paper cites JMLR , volume=.

Probabilistic Residual Learning for Online Recommendations JMLR , volume=

Reference 85

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Observation b526e42d-a69c-4cc9-be7c-d8047b3ddebf · outbound

This paper cites NIPS , pages=.

Probabilistic Residual Learning for Online Recommendations NIPS , pages=

Reference 86

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source=arxiv_source observed=2026-08-01T09:14:49.895857Z digest=sha256:13139d7cc0a7561d1230553906f92331839d740a09c6b516c874463874e299b2

Observation 700ea91b-9bb9-493e-bc9f-af77bf6dcab2 · outbound

This paper cites CVPR , pages=.

Probabilistic Residual Learning for Online Recommendations CVPR , pages=

Reference 87

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source=arxiv_source observed=2026-08-01T09:14:49.979970Z digest=sha256:db1d5e9eca0eafe2155f0cc1a4949b0811849db1fbda3e6be90604f0ee3f2145

Observation ac7c09ef-fa58-49e2-90c8-858bc64ba0a5 · outbound

This paper cites KDD , pages=.

Probabilistic Residual Learning for Online Recommendations KDD , pages=

Reference 88

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source=arxiv_source observed=2026-08-01T09:14:50.043649Z digest=sha256:0ae3b8cd8a9f70c2254a5c96735f978931f40f93cdc5740e91414bed3318d242

Observation e9d3bcaa-35c3-432b-8595-b756b5786df6 · outbound

This paper cites CVPR , pages=.

Probabilistic Residual Learning for Online Recommendations CVPR , pages=

Reference 89

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source=arxiv_source observed=2026-08-01T09:14:50.138912Z digest=sha256:48b316b0bab03b547b355814c50c96e7b59800688a81c4dc92c4a11e03b54709

Observation 41547fa0-4762-4e13-a368-e5634dd76589 · outbound

This paper cites an unresolved cited work.

Probabilistic Residual Learning for Online Recommendations Unresolved cited work

Reference 90

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source=arxiv_source observed=2026-08-01T09:14:50.301444Z digest=sha256:8b2fbbed6a6bd02de81c19b80a6b693ea587bbfad7551fcac37efc8b49a114b3

Observation 2ecad685-f744-4b8c-a754-d378f0b54ece · outbound

This paper cites Forward-Backward Splitting for Optimal Transport based Problems.

Probabilistic Residual Learning for Online Recommendations Forward-Backward Splitting for Optimal Transport based Problems

Reference 91

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source=arxiv_source observed=2026-08-01T09:14:50.436764Z digest=sha256:127568da35869656c536d5fb55878265d07c4131cd71dbfeae179e5665d42670

Observation d173cb5e-21d5-40da-a8c9-eaf25e552297 · outbound

This paper cites ICRA , pages=.

Probabilistic Residual Learning for Online Recommendations ICRA , pages=

Reference 92

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source=arxiv_source observed=2026-08-01T09:14:50.542946Z digest=sha256:ae5f27af585b90a046b087797e157f62dd8fc2fa5d40f30df8fc6e07cff7d7aa

Observation 2354cd28-717d-438a-91d2-979922a6d3c3 · outbound

This paper cites TKDE , volume=.

Probabilistic Residual Learning for Online Recommendations TKDE , volume=

Reference 93

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source=arxiv_source observed=2026-08-01T09:14:50.691730Z digest=sha256:fe79781461304ffe84a76160a16bc8c19f04870383503c08580dfc7677dc1727

Observation 64e7df2f-bff3-4091-ac47-138cd30acb55 · outbound

This paper cites Towards Continuous Domain adaptation for Healthcare.

Probabilistic Residual Learning for Online Recommendations Towards Continuous Domain adaptation for Healthcare

Reference 94

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source=arxiv_source observed=2026-08-01T09:14:50.770379Z digest=sha256:12c8e720302e7a71ee9fe20b1866e7ffe716a4e01da6f2fe8b20bedec542717f

Observation d926e914-bde6-4f90-bc7d-b31ef3263229 · outbound

This paper cites Geophysical Research Letters , number =.

Probabilistic Residual Learning for Online Recommendations Geophysical Research Letters , number =

Reference 95

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source=arxiv_source observed=2026-08-01T09:14:50.869555Z digest=sha256:d3640deb60b8fd64427d4965612690a1a2bbc34e30f57965e7da23590a2061e4

Observation 513c4c6d-14c4-47a6-b555-e867f58b113b · outbound

This paper cites Computers & Geosciences , keywords =.

Probabilistic Residual Learning for Online Recommendations Computers & Geosciences , keywords =

Reference 96

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source=arxiv_source observed=2026-08-01T09:14:50.999870Z digest=sha256:b22e3014d95c1af52e5be7d6414406ad838d1e3123b475732352f6b55efd57e6

Observation a2ec4abb-8e6b-4590-92b7-c1a0f9035ea5 · outbound

This paper cites Geophysical Research Letters , number =.

Probabilistic Residual Learning for Online Recommendations Geophysical Research Letters , number =

Reference 97

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source=arxiv_source observed=2026-08-01T09:14:51.170179Z digest=sha256:b53ee224b7f013cbca91b81861f9e16939d25ab0906855c61c0a7be3f7f4792a

Observation 01f3c3a7-68ed-4a01-a09c-d6c6e95bbaa6 · outbound

This paper cites 2018 IEEE international conference on data mining (ICDM) , pages=.

Probabilistic Residual Learning for Online Recommendations 2018 IEEE international conference on data mining (ICDM) , pages=

Reference 98

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source=arxiv_source observed=2026-08-01T09:14:51.299112Z digest=sha256:934b2e968455dff02e796954f1995730e76200325c3c66fa42b94d17c79b3ece

Observation 22294aff-b658-415b-a1ac-bb7667997e68 · outbound

This paper cites I (Still) Can't Believe It's Not Better! NeurIPS 2021 Workshop , year=.

Probabilistic Residual Learning for Online Recommendations I (Still) Can't Believe It's Not Better! NeurIPS 2021 Workshop , year=

Reference 99

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source=arxiv_source observed=2026-08-01T09:14:51.398788Z digest=sha256:8c425d42947e283fef99b99d6c1cc243c235d046923e7221295f3ed5fc1cc3da

Observation e7563004-f71f-4545-a724-e731c5e3259c · outbound

This paper cites Proceedings of the 17th ACM Conference on Recommender Systems , pages=.

Probabilistic Residual Learning for Online Recommendations Proceedings of the 17th ACM Conference on Recommender Systems , pages=

Reference 100

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source=arxiv_source observed=2026-08-01T09:14:51.481732Z digest=sha256:a114893124f065a5417ef47cf82298288702f2bf88f088e23d03d9dfb66c0b89

Pith citing papers

No inbound Pith citation observations are available.