Paper Citation Record · LEDGER

Improved Online Confidence Bounds for Multinomial Logistic Bandits

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 4 inbound Pith citation observations for arXiv:2502.10020.

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

pith.paper-citation-record.v1
2502.10020 v5

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:57:04.463584Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-05T10:20:16.326364Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-05T10:20:57.158448Z

Reference resolution

35 of 35 outbound references displayed

verified exact3
verified fuzzy11
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4d77ca67-19d0-48ce-8074-84a0de452634 · outbound

This paper cites Instance-wise minimax-optimal algorithms for logistic bandits.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Instance-wise minimax-optimal algorithms for logistic bandits

Reference 1

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Observation 8386362a-4e37-4f85-ae78-c312b0f2bf24 · outbound

This paper cites A tractable online learning algorithm for the multinomial logit contextual bandit.

Improved Online Confidence Bounds for Multinomial Logistic Bandits A tractable online learning algorithm for the multinomial logit contextual bandit

Reference 2

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Observation 98ac0450-8c64-4734-9a48-1ffd666d766c · outbound

This paper cites Thompson sampling for the mnl-bandit.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Thompson sampling for the mnl-bandit

Reference 3

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Observation 029518b4-661e-4cc4-beb5-e994341b99a1 · outbound

This paper cites Mnl-bandit: A dynamic learning approach to assortment selection.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Mnl-bandit: A dynamic learning approach to assortment selection

Reference 4

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Observation 71714843-40a4-4df9-897f-2b9ba0e877d5 · outbound

This paper cites and Orabona, F.

Improved Online Confidence Bounds for Multinomial Logistic Bandits and Orabona, F

Reference 5

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Observation c221067a-8140-476a-b020-4a9cc358103b · outbound

This paper cites Dynamic assortment optimization with changing contextual information.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Dynamic assortment optimization with changing contextual information

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:57:04.928699Z

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Observation a4a86fdf-f7d1-4fd8-b13c-76432e207683 · outbound

This paper cites Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-07T19:57:04.613603Z

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Observation 2ef9a6c0-dde0-4f3d-9dfa-1c1592e11769 · outbound

This paper cites M., Gallego, G., and Topaloglu, H.

Improved Online Confidence Bounds for Multinomial Logistic Bandits M., Gallego, G., and Topaloglu, H

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:57:04.912282Z

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Observation 395b0f73-9a7c-45c4-86e1-eeae296055fd · outbound

This paper cites On the performance of thompson sampling on logistic bandits.

Improved Online Confidence Bounds for Multinomial Logistic Bandits On the performance of thompson sampling on logistic bandits

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:57:04.896306Z

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Observation 33c44dad-05ae-441a-a5f4-e05eee9c740a · outbound

This paper cites Improved optimistic algorithms for logistic bandits.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Improved optimistic algorithms for logistic bandits

Reference 10

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Observation 354e32f4-8d52-47f0-8da2-b10453c109c5 · outbound

This paper cites Jointly efficient and optimal algorithms for logistic bandits.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Jointly efficient and optimal algorithms for logistic bandits

Reference 11

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Observation 562a6fc7-a9f1-4907-ae95-e98de2813233 · outbound

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

Improved Online Confidence Bounds for Multinomial Logistic Bandits J., Kale, S., Luo, H., Mohri, M., and Sridharan, K

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:57:04.859444Z

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Observation a57f2435-0449-459b-be8f-6ed5c9cfcdfb · outbound

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Improved Online Confidence Bounds for Multinomial Logistic Bandits Unresolved cited work

Reference 13

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Observation 886bccc9-1dbf-444a-b1c8-8ed5779ce9bf · outbound

This paper cites Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-07T19:57:04.588808Z

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Observation 9456c4c2-280c-4de9-be63-40ff74cafd58 · outbound

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

Improved Online Confidence Bounds for Multinomial Logistic Bandits Mixability made efficient: Fast online multiclass logistic regression

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:57:04.832839Z

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Observation daaca871-7c85-402a-b064-bd77003f04a1 · outbound

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Improved Online Confidence Bounds for Multinomial Logistic Bandits Unresolved cited work

Reference 16

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Observation e032c028-f5fd-4599-b6a4-2f50dadb6237 · outbound

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

Improved Online Confidence Bounds for Multinomial Logistic Bandits Improved regret analysis for variance-adaptive linear bandits and horizon-free linear mixture mdps

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:57:04.806395Z

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Observation 9357f74d-f942-4fa8-bfc8-5245d60d0d14 · outbound

This paper cites and Oh, M.-h.

Improved Online Confidence Bounds for Multinomial Logistic Bandits and Oh, M.-h

Reference 18

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Observation 43b8d225-c795-465e-b2a8-95d9a7baee25 · outbound

This paper cites Combinatorial Reinforcement Learning with Preference Feedback.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Combinatorial Reinforcement Learning with Preference Feedback

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-07T19:57:04.564210Z

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Observation 18e02381-33bd-4e77-a525-fdd154b8bde5 · outbound

This paper cites Improved regret bounds of (multinomial) logistic bandits via regret-to-confidence-set conversion.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Improved regret bounds of (multinomial) logistic bandits via regret-to-confidence-set conversion

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:57:04.779086Z

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Observation 6e692353-883d-45f9-bdb6-35fc744601eb · outbound

This paper cites A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits.

Improved Online Confidence Bounds for Multinomial Logistic Bandits A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits

Reference 21

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Observation 6907349d-54b4-4b13-9520-78832b7496a8 · outbound

This paper cites Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation

Reference 22

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Observation 43fdf7f1-f54e-42ab-b232-3be17b1e7154 · outbound

This paper cites Modelling the choice of residential location.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Modelling the choice of residential location

Reference 23

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Improved Online Confidence Bounds for Multinomial Logistic Bandits and Iyengar, G

Reference 24

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This paper cites and Iyengar, G.

Improved Online Confidence Bounds for Multinomial Logistic Bandits and Iyengar, G

Reference 25

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Observation cacdfdb8-e4d8-476f-af53-984628a6ae22 · outbound

This paper cites Multinomial logit bandit with linear utility functions.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Multinomial logit bandit with linear utility functions

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:57:04.731088Z

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Observation 7cc01e79-fa64-4674-89d6-f96ff50575af · outbound

This paper cites Infinite-Horizon Reinforcement Learning with Multinomial Logistic Function Approximation.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Infinite-Horizon Reinforcement Learning with Multinomial Logistic Function Approximation

Reference 27

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Improved Online Confidence Bounds for Multinomial Logistic Bandits and Goyal, V

Reference 28

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Improved Online Confidence Bounds for Multinomial Logistic Bandits M., and Shmoys, D

Reference 29

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This paper cites and Zeevi, A.

Improved Online Confidence Bounds for Multinomial Logistic Bandits and Zeevi, A

Reference 30

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This paper cites Generalized linear bandits with limited adaptivity.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Generalized linear bandits with limited adaptivity

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:57:04.682395Z

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Observation 6e004508-af96-4b42-b2ea-9fc93e18cb04 · outbound

This paper cites Composite convex minimization involving self-concordant-like cost functions.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Composite convex minimization involving self-concordant-like cost functions

Reference 32

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This paper cites Etude critique de la notion de collectif, volume 3.

Improved Online Confidence Bounds for Multinomial Logistic Bandits Etude critique de la notion de collectif, volume 3

Reference 33

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Improved Online Confidence Bounds for Multinomial Logistic Bandits and Sugiyama, M

Reference 34

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Improved Online Confidence Bounds for Multinomial Logistic Bandits write newline

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Pith citing papers

Observation ea485244-967c-49dc-a9d6-746bf542b833 · inbound

Optimal Exploration of New Products under Assortment Decisions cites this paper.

Optimal Exploration of New Products under Assortment Decisions Improved Online Confidence Bounds for Multinomial Logistic Bandits

Reference 31

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Observation 1cb3e46c-1f32-45e8-a04f-0985611330cf · inbound

Optimal Exploration of New Products under Assortment Decisions cites this paper.

Optimal Exploration of New Products under Assortment Decisions Improved Online Confidence Bounds for Multinomial Logistic Bandits

Reference 31

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Optimal Online and Offline Algorithms for Contextual MNL with Applications to Assortment and Pricing cites this paper.

Optimal Online and Offline Algorithms for Contextual MNL with Applications to Assortment and Pricing Improved Online Confidence Bounds for Multinomial Logistic Bandits

Reference 33

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Observation cc609e57-f4fe-4023-9cd3-8e58f7a5e6ea · inbound

Minimax Optimal Variance-Aware Regret Bounds for Multinomial Logistic MDPs cites this paper.

Minimax Optimal Variance-Aware Regret Bounds for Multinomial Logistic MDPs Improved Online Confidence Bounds for Multinomial Logistic Bandits

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-20T04:58:05.031716Z

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