Paper Citation Record · LEDGER

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability

As of 19 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2506.10616.

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

pith.paper-citation-record.v1
2506.10616 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:38:28.905504Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T10:33:32.263320Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

verified exact1
verified fuzzy37
unresolved3
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5a97a4ac-781b-4a65-8971-53720ea5c8b3 · outbound

This paper cites write newline.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability write newline

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc93fa47-14b6-4003-845d-b4ba9483ddc9 · outbound

This paper cites M., Chernov, A.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability M., Chernov, A

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:39.774385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3fbb0016-dfef-4c15-83c3-a77d0b6e56f0 · outbound

This paper cites and Wang, Y.-X.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability and Wang, Y.-X

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:39.445476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 541ad762-aa37-42df-bb23-add149037a2c · outbound

This paper cites and Wang, Y.-X.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability and Wang, Y.-X

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:39.171617Z

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Observation b164c2d1-936b-4a0b-83b5-313b9502338b · outbound

This paper cites and Wang, Y.-X.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability and Wang, Y.-X

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:38.892332Z

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Observation f9dc0e34-4b4d-4121-8ef6-3dec21b5b462 · outbound

This paper cites Non-stationary contextual pricing with safety constraints.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Non-stationary contextual pricing with safety constraints

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:38.673253Z

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Observation c00cec0d-8dba-417f-920f-b31c226eacdd · outbound

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Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Unresolved cited work

Reference 7

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Source-reported events for the cited work

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Observation 901f6fa4-72dc-4d71-bf6b-3197977801d0 · outbound

This paper cites an unresolved cited work.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Unresolved cited work

Reference 8

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Source-reported events for the cited work

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Observation 28ac2170-f0fa-4aee-937a-b8b72072963a · outbound

This paper cites and Lugosi, G.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability and Lugosi, G

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:37.923624Z

Source-reported events for the cited work

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Observation 44ad8126-24eb-4b0a-8c24-58ee053d4fc6 · outbound

This paper cites Mirror Descent Meets Fixed Share (and feels no regret).

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Mirror Descent Meets Fixed Share (and feels no regret)

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:38:29.195433Z

Source-reported events for the cited work

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Observation 27331ec4-25d0-4cc9-871b-31911ca3a29b · outbound

This paper cites Mirror descent meets fixed share (and feels no regret).

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Mirror descent meets fixed share (and feels no regret)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:37.690974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 746cb21c-e129-4752-b811-ce22b09cfb5d · outbound

This paper cites I-divergence geometry of probability distributions and minimization problems.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability I-divergence geometry of probability distributions and minimization problems

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:37.479790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c9f0ebb9-6070-4b5a-96dc-8cd3889eddd1 · outbound

This paper cites and Matus, F.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability and Matus, F

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:37.240682Z

Source-reported events for the cited work

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Observation 33a66c31-9303-4cf7-b79c-473c6ad7d32d · outbound

This paper cites Parameter-free, dynamic, and strongly-adaptive online learning.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Parameter-free, dynamic, and strongly-adaptive online learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:36.972784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6703f382-8061-4a06-ae42-111c2c467594 · outbound

This paper cites J., Kale, S., Luo, H., Mohri, M., and Sridharan, K.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability J., Kale, S., Luo, H., Mohri, M., and Sridharan, K

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:36.656187Z

Source-reported events for the cited work

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Observation 95d2d4b5-be6a-478e-87ed-9529907a198a · outbound

This paper cites Introduction to O nline C onvex O ptimization.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Introduction to O nline C onvex O ptimization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:36.353154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a8d84607-bc2a-493d-8cbb-3466e75a59cc · outbound

This paper cites and Seshadhri, C.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability and Seshadhri, C

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:36.044804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1d1b9f0e-7502-4db5-b0a0-a532f973eee4 · outbound

This paper cites Logarithmic regret algorithms for online convex optimization.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Logarithmic regret algorithms for online convex optimization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:35.725970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7052a411-bf70-43d3-a69e-a745565d47a1 · outbound

This paper cites Information Theory for Continuous Systems.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Information Theory for Continuous Systems

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:35.396004Z

Source-reported events for the cited work

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Observation fd3de33c-0b69-45d0-9539-6dfa4315eae1 · outbound

This paper cites An optimal algorithm for bandit convex optimization with strongly-convex and smooth loss.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability An optimal algorithm for bandit convex optimization with strongly-convex and smooth loss

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:35.026306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4531446e-cb52-4d6e-a763-a7d42210b04e · outbound

This paper cites and Cutkosky, A.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability and Cutkosky, A

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:34.791714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3513fe37-44aa-4ddf-9129-defa2ddd6d1b · outbound

This paper cites Mixability made efficient: Fast online multiclass logistic regression.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Mixability made efficient: Fast online multiclass logistic regression

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:34.522067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1c98d144-fc10-4cc6-aea4-016de5ccc54a · outbound

This paper cites Near-optimal dynamic regret for adversarial linear mixture mdps.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Near-optimal dynamic regret for adversarial linear mixture mdps

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:34.284578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 19ae5522-a99e-41a9-8b21-4493123e9e41 · outbound

This paper cites J., Hadiji, H., and van Erven, T.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability J., Hadiji, H., and van Erven, T

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:33.949069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d0d541fb-09b6-444b-bf97-f900e24ed630 · outbound

This paper cites Online learning via sequential complexities.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Online learning via sequential complexities

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:33.679024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1451fe17-54db-42f9-86df-cc42a21d625a · outbound

This paper cites and Ben-David, S.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability and Ben-David, S

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:33.421281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f55faf6b-35bb-4361-bd2a-87b1ab2ac24e · outbound

This paper cites The many faces of exponential weights in online learning.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability The many faces of exponential weights in online learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:33.151288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 91dfe9c6-318a-4774-9b0c-8f75a088ddd9 · outbound

This paper cites and Koolen, W.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability and Koolen, W

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:32.871210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dfe21616-087d-4d82-89b7-2f8b40ccb346 · outbound

This paper cites D., and Williamson, R.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability D., and Williamson, R

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:32.660139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3a724d75-06bc-4a36-bc73-c28428f2b0cd · outbound

This paper cites D., Mehta, N.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability D., Mehta, N

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:32.343613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a2a740a7-07b8-4a12-8998-9b44b41234e1 · outbound

This paper cites A game of prediction with expert advice.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability A game of prediction with expert advice

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:32.044831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6c5d5732-7cea-4809-a2cf-0c842fa7780d · outbound

This paper cites Competitive on-line statistics.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Competitive on-line statistics

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:31.764012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 750395ee-bcef-4e41-a3c4-5952c26097f2 · outbound

This paper cites and Zhdanov, F.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability and Zhdanov, F

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:31.546899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 98708636-c295-4c91-aad3-27017c105f6e · outbound

This paper cites and Luo, H.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability and Luo, H

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:31.287730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7038781c-e1db-4eeb-b7a6-fe739f3c230c · outbound

This paper cites Adaptive online learning in dynamic environments.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Adaptive online learning in dynamic environments

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:30.986461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c0980bdd-3510-4fdb-94d3-196be18ed6cc · outbound

This paper cites Adapting to continuous covariate shift via online density ratio estimation.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Adapting to continuous covariate shift via online density ratio estimation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:30.758442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 08e339e7-b236-41ca-89c8-7115a1c1b8b1 · outbound

This paper cites Unconstrained dynamic regret via sparse coding.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Unconstrained dynamic regret via sparse coding

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:30.559371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4f053094-f813-415c-9e48-c64c21114dc8 · outbound

This paper cites Dynamic regret of convex and smooth functions.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Dynamic regret of convex and smooth functions

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:30.335466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f4c24c05-f12f-4e8c-bf78-e1665a93a86a · outbound

This paper cites Efficient methods for non-stationary online learning.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Efficient methods for non-stationary online learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:30.058868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3fa9d2c7-6069-4b45-94b0-eadb6efd06c6 · outbound

This paper cites Adaptivity and non-stationarity: Problem-dependent dynamic regret for online convex optimization.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Adaptivity and non-stationarity: Problem-dependent dynamic regret for online convex optimization

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:29.836447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f118db62-ef2c-49e8-b05b-32a35264d124 · outbound

This paper cites Online convex programming and generalized infinitesimal gradient ascent.

Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability Online convex programming and generalized infinitesimal gradient ascent

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:29.550345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 5ecb1a17-8c15-484e-a5ff-5513462ee330 · inbound

Adaptive Bayesian Online Learning via Expert Aggregation cites this paper.

Adaptive Bayesian Online Learning via Expert Aggregation Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T10:33:32.263320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.