Paper Citation Record · LEDGER

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss

As of 7 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2507.12584.

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

pith.paper-citation-record.v1
2507.12584 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:58:15.003876Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

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

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d6b45aa9-fcf2-40e6-a4c5-687fdac23c69 · outbound

This paper cites write newline.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss write newline

Reference 1

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Observation 8ac4b9eb-f547-445c-a686-6c336a62d068 · outbound

This paper cites Open problem: First-order regret bounds for contextual bandits.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Open problem: First-order regret bounds for contextual bandits

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:20.908235Z

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Observation 44d1696d-9fc9-454f-ac14-c8af1c789d50 · outbound

This paper cites On least squares and linear combination of observations.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss On least squares and linear combination of observations

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:20.744739Z

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Observation 5bb59c8c-a226-4e0b-ade6-1b7b8a40e367 · outbound

This paper cites Make the minority great again: First-order regret bound for contextual bandits.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Make the minority great again: First-order regret bound for contextual bandits

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:20.588547Z

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Observation a673e881-add3-4224-ba6a-02ab55318a30 · outbound

This paper cites Hannan Consistency in On-Line Learning in Case of Unbounded Losses Under Partial Monitoring.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Hannan Consistency in On-Line Learning in Case of Unbounded Losses Under Partial Monitoring

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:20.446726Z

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Observation 573e357c-2e0c-4b90-9495-e722bdfa3387 · outbound

This paper cites Improved second-order bounds for prediction with expert advice.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Improved second-order bounds for prediction with expert advice

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:20.274637Z

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Observation 408cc02d-0814-425e-b394-98854ed655d4 · outbound

This paper cites an unresolved cited work.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Unresolved cited work

Reference 7

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Observation 5addd4bc-b6a6-4934-b039-5291de2a49a0 · outbound

This paper cites and Schapire, R.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss and Schapire, R

Reference 8

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This paper cites Regression with input-dependent noise: A gaussian process treatment.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Regression with input-dependent noise: A gaussian process treatment

Reference 9

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Observation d356267a-4789-4c6f-b446-2bf864f9a916 · outbound

This paper cites and Kale, S.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss and Kale, S

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:19.631815Z

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This paper cites and Kale, S.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss and Kale, S

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:19.449786Z

Source-reported events for the cited work

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Observation 8716a548-3069-4efa-9f50-0bbfcbfe8194 · outbound

This paper cites Tight first- and second-order regret bounds for adversarial linear bandits.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Tight first- and second-order regret bounds for adversarial linear bandits

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:19.254426Z

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Observation 4a4ac101-cb1b-4633-a513-21184bc430e7 · outbound

This paper cites How does variance shape the regret in contextual bandits? In Advances in Neural Information Processing Systems (NeurIPS), 2024.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss How does variance shape the regret in contextual bandits? In Advances in Neural Information Processing Systems (NeurIPS), 2024

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:19.089076Z

Source-reported events for the cited work

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This paper cites and Kim, J.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss and Kim, J

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:18.958056Z

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Observation 8d6c2487-086c-4a36-8170-f9b54a892480 · outbound

This paper cites Most likely heteroscedastic gaussian process regression.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Most likely heteroscedastic gaussian process regression

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:18.788728Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 737c2b2d-41c4-4951-968a-0c86fe1b381f · outbound

This paper cites Improved regret analysis for variance-adaptive linear bandits and horizon-free linear mixture mdps.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Improved regret analysis for variance-adaptive linear bandits and horizon-free linear mixture mdps

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:18.630198Z

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Observation ba85f3d8-25d2-4335-8637-a2ddfa42601d · outbound

This paper cites and Krause, A.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss and Krause, A

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:18.455161Z

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Observation c3e6c07d-13d6-4f40-87b4-39a062daa240 · outbound

This paper cites and Szepesv \' a ri, C.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss and Szepesv \' a ri, C

Reference 18

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This paper cites URL https://cs.bme.hu/ gergo/files/tutorial.pdf.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss URL https://cs.bme.hu/ gergo/files/tutorial.pdf

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:18.125826Z

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Observation a962f1f5-4e98-4eff-b378-1ac2f0ea3701 · outbound

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Second-Order Bounds for [0,1]-Valued Regression via Betting Loss and Jun, K.-S

Reference 20

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Observation 49c0307a-f628-4efe-8d27-0e02d98dc102 · outbound

This paper cites Second order bounds for contextual bandits with function approximation.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Second order bounds for contextual bandits with function approximation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:17.980557Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 057ee9e2-cfe5-4a40-b12c-a6b0161f6872 · outbound

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Second-Order Bounds for [0,1]-Valued Regression via Betting Loss and Van Roy , B

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:17.749105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fca10de9-cb95-42a0-a63c-d75f7bf0b320 · outbound

This paper cites Incomplete information and internal regret in prediction of individual sequences.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Incomplete information and internal regret in prediction of individual sequences

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:17.549612Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Unresolved cited work

Reference 24

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This paper cites J., Chen, Y., Simchowitz, M., Du, S., and Jamieson, K.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss J., Chen, Y., Simchowitz, M., Du, S., and Jamieson, K

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:17.204586Z

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Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Unresolved cited work

Reference 26

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Observation b341f6e1-4fa6-497a-be83-16dc84400285 · outbound

This paper cites More benefits of being distributional: Second-order bounds for reinforcement learning.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss More benefits of being distributional: Second-order bounds for reinforcement learning

Reference 27

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Observation ab21d6ac-d056-44f4-9ca7-01b06844b234 · outbound

This paper cites HelpSteer2: Open-source dataset for training top-performing reward models.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss HelpSteer2: Open-source dataset for training top-performing reward models

Reference 28

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Second-Order Bounds for [0,1]-Valued Regression via Betting Loss and Ramdas, A

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:16.709522Z

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Observation 1185acdf-5864-45e4-b630-4b27e4f18070 · outbound

This paper cites Experimental designs for heteroskedastic variance.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Experimental designs for heteroskedastic variance

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:16.443564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0a20adb5-e280-45f1-8a31-9af32ee47f90 · outbound

This paper cites Optimal comparator adaptive online learning with switching cost.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Optimal comparator adaptive online learning with switching cost

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:16.254369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 915d529b-798d-46eb-a5e3-c0b87a5be811 · outbound

This paper cites Variance-dependent regret bounds for linear bandits and reinforcement learning: Adaptivity and computational efficiency.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Variance-dependent regret bounds for linear bandits and reinforcement learning: Adaptivity and computational efficiency

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:16.054504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 97df13c8-cd46-4c6e-aaba-665b520879b1 · outbound

This paper cites Optimal online generalized linear regression with stochastic noise and its application to heteroscedastic bandits.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Optimal online generalized linear regression with stochastic noise and its application to heteroscedastic bandits

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:15.942160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a0ea72bb-7e2f-4d8a-a39a-66d409057b4c · outbound

This paper cites Adaptive experimentation when you can't experiment.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Adaptive experimentation when you can't experiment

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:15.797102Z

Source-reported events for the cited work

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Observation 0fbaba2c-b388-4047-a952-5d18f0b1800f · outbound

This paper cites and Gu, Q.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss and Gu, Q

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:15.597275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 154942ca-8625-4a17-b910-82dc6742aa43 · outbound

This paper cites Nearly minimax optimal reinforcement learning for linear mixture markov decision processes.

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Nearly minimax optimal reinforcement learning for linear mixture markov decision processes

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:15.428947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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