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Paper Citation Record · LEDGER

Forgetting-Factor Regret for Online Zero-Sum Games

As of 18 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2607.07078.

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

pith.paper-citation-record.v1
2607.07078 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-09T20:37:29.523862Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

44 of 44 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6cb62b7f-90e0-46ac-a4cd-850eaa730a66 · outbound

This paper cites Equilibrium points in n-person games,.

Forgetting-Factor Regret for Online Zero-Sum Games Equilibrium points in n-person games,

Reference 1

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3f844282-770b-4d8b-b551-39f4421c1877 · outbound

This paper cites Theory of games and economic behavior: 60th anniversary commemorative edition,.

Forgetting-Factor Regret for Online Zero-Sum Games Theory of games and economic behavior: 60th anniversary commemorative edition,

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation f67a9e75-248f-4719-96a4-7c9119f74093 · outbound

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

Forgetting-Factor Regret for Online Zero-Sum Games Online convex programming and generalized infinitesimal gradient ascent,

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation a4179af7-7fbc-4dc4-a218-0a8948c71137 · outbound

This paper cites Logarithmic regret algorithms for online convex optimization,.

Forgetting-Factor Regret for Online Zero-Sum Games Logarithmic regret algorithms for online convex optimization,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 4e71fe74-9a52-46ef-9587-7f05dd128102 · outbound

This paper cites Zur theorie der gesellschaftsspiele,.

Forgetting-Factor Regret for Online Zero-Sum Games Zur theorie der gesellschaftsspiele,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 6a88e904-7b38-4337-9756-9b487f7fa469 · outbound

This paper cites V on Neumann and O.

Forgetting-Factor Regret for Online Zero-Sum Games V on Neumann and O

Reference 6

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 7c76b658-2bf6-401e-98d9-8cb22350e3f7 · outbound

This paper cites Ben-Tal, L.

Forgetting-Factor Regret for Online Zero-Sum Games Ben-Tal, L

Reference 7

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation caac9eae-41b4-4f9a-9f3a-215c0d064314 · outbound

This paper cites Game theory, online prediction and boosting,.

Forgetting-Factor Regret for Online Zero-Sum Games Game theory, online prediction and boosting,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 554cdfc2-fcdc-4928-aff2-c6de170675d5 · outbound

This paper cites Generative adversarial nets,.

Forgetting-Factor Regret for Online Zero-Sum Games Generative adversarial nets,

Reference 9

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 45353e25-cd73-44a8-8d65-aec5e375fe4d · outbound

This paper cites Heads-up limit hold’em poker is solved,.

Forgetting-Factor Regret for Online Zero-Sum Games Heads-up limit hold’em poker is solved,

Reference 10

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 9b9ec9da-d8e3-4c9a-9ea3-416e46bfcce6 · outbound

This paper cites A generalization of brouwer’s fixed point theorem,.

Forgetting-Factor Regret for Online Zero-Sum Games A generalization of brouwer’s fixed point theorem,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 02488b54-d02a-4f5a-adcb-d9cc9f2fb378 · outbound

This paper cites Subgradient methods for saddle-point problems,.

Forgetting-Factor Regret for Online Zero-Sum Games Subgradient methods for saddle-point problems,

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 119a6cf2-60e0-42d1-a9e3-42a06bbb4f46 · outbound

This paper cites The limit points of (optimistic) gradient descent in min-max optimization,.

Forgetting-Factor Regret for Online Zero-Sum Games The limit points of (optimistic) gradient descent in min-max optimization,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation c4bb8dcc-dcf3-4ec0-b65c-1d84897d54fc · outbound

This paper cites Competitive gradient descent,.

Forgetting-Factor Regret for Online Zero-Sum Games Competitive gradient descent,

Reference 14

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation df1c5f2c-2ea6-46de-9222-a1727849fed7 · outbound

This paper cites Local saddle point optimization: A curvature exploitation approach,.

Forgetting-Factor Regret for Online Zero-Sum Games Local saddle point optimization: A curvature exploitation approach,

Reference 15

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 99152c8e-f71c-474e-968d-d14a4c1b232c · outbound

This paper cites A primal-dual algorithm with line search for general convex-concave saddle point problems,.

Forgetting-Factor Regret for Online Zero-Sum Games A primal-dual algorithm with line search for general convex-concave saddle point problems,

Reference 16

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation f3232c15-4ceb-4b21-8c68-f70e6fd4f4e3 · outbound

This paper cites On linear convergence of iterative methods for the variational inequality problem,.

Forgetting-Factor Regret for Online Zero-Sum Games On linear convergence of iterative methods for the variational inequality problem,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3460ec2d-4089-42a4-aed8-89ea1b8ee2dd · outbound

This paper cites Interaction matters: a note on non-asymptotic local convergence of generative adversarial networks,.

Forgetting-Factor Regret for Online Zero-Sum Games Interaction matters: a note on non-asymptotic local convergence of generative adversarial networks,

Reference 18

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation db1d6323-18da-47f7-8fe3-f6d42892f484 · outbound

This paper cites A unified analysis of extra-gradient and optimistic gradient methods for saddle point problems: proximal point approach,.

Forgetting-Factor Regret for Online Zero-Sum Games A unified analysis of extra-gradient and optimistic gradient methods for saddle point problems: proximal point approach,

Reference 19

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 92b0970a-a160-47cc-b26e-02d8d547ec49 · outbound

This paper cites Convergence rate of o(1/k) for optimistic gradient and extragradient methods in smooth convex-concave saddle point problems,.

Forgetting-Factor Regret for Online Zero-Sum Games Convergence rate of o(1/k) for optimistic gradient and extragradient methods in smooth convex-concave saddle point problems,

Reference 20

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation ab44dc07-bff2-4c4c-a29a-96a62039513a · outbound

This paper cites Accelerated algorithms for smooth convex-concave minimax problems with o (1/kˆ 2) rate on squared gradient norm,.

Forgetting-Factor Regret for Online Zero-Sum Games Accelerated algorithms for smooth convex-concave minimax problems with o (1/kˆ 2) rate on squared gradient norm,

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3b8fae68-9948-495e-ab8f-8c15c1e2b9ab · outbound

This paper cites Linear last-iterate convergence in constrained saddle-point optimization,.

Forgetting-Factor Regret for Online Zero-Sum Games Linear last-iterate convergence in constrained saddle-point optimization,

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 6ef59f55-0d21-40eb-ac2e-ce44251dc86a · outbound

This paper cites Min-max optimization without gradients: Convergence and applications to black-box evasion and poisoning attacks,.

Forgetting-Factor Regret for Online Zero-Sum Games Min-max optimization without gradients: Convergence and applications to black-box evasion and poisoning attacks,

Reference 23

Resolution
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raw_fallback, observed 2026-07-09T20:46:34.064433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 9c162b39-6b66-4d15-9f1a-931d22ae2cba · outbound

This paper cites Zeroth-order algorithms for nonconvex-strongly-concave minimax problems with improved complexities,.

Forgetting-Factor Regret for Online Zero-Sum Games Zeroth-order algorithms for nonconvex-strongly-concave minimax problems with improved complexities,

Reference 24

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raw_fallback, observed 2026-07-09T20:46:34.057334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 10a2682d-b075-4b62-bfaf-f8539d442be2 · outbound

This paper cites Frank-wolfe algorithms for saddle point problems,.

Forgetting-Factor Regret for Online Zero-Sum Games Frank-wolfe algorithms for saddle point problems,

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3cbb6594-f34e-4c2d-a4a8-4548c4a68dab · outbound

This paper cites On frank-wolfe and equilibrium computation,.

Forgetting-Factor Regret for Online Zero-Sum Games On frank-wolfe and equilibrium computation,

Reference 26

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raw_fallback, observed 2026-07-09T20:46:34.071008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 9982d2ee-ee96-486a-b60f-669dcb732b49 · outbound

This paper cites Faster rates for convex-concave games,.

Forgetting-Factor Regret for Online Zero-Sum Games Faster rates for convex-concave games,

Reference 27

Resolution
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raw_fallback, observed 2026-07-09T20:46:34.062570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation b6edf189-28ee-45c2-a7eb-e5192e5a12af · outbound

This paper cites Efficient regret minimization in non-convex games,.

Forgetting-Factor Regret for Online Zero-Sum Games Efficient regret minimization in non-convex games,

Reference 28

Resolution
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raw_fallback, observed 2026-07-09T20:46:34.087300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation b802acb2-4f09-4a8e-9993-e350cc8fa3ac · outbound

This paper cites On gradient descent ascent for nonconvex-concave minimax problems,.

Forgetting-Factor Regret for Online Zero-Sum Games On gradient descent ascent for nonconvex-concave minimax problems,

Reference 29

Resolution
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raw_fallback, observed 2026-07-09T20:46:34.090531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation b56242d5-81ce-4e5a-a8fa-b529bfaacce9 · outbound

This paper cites Online learning and online convex optimization,.

Forgetting-Factor Regret for Online Zero-Sum Games Online learning and online convex optimization,

Reference 30

Resolution
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raw_fallback, observed 2026-07-09T20:46:34.041063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation d121dc16-dfb5-4844-aa44-4eaf628a7d5d · outbound

This paper cites Online convex optimization in the bandit setting: gradient descent without a gradient,.

Forgetting-Factor Regret for Online Zero-Sum Games Online convex optimization in the bandit setting: gradient descent without a gradient,

Reference 31

Resolution
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raw_fallback, observed 2026-07-09T20:46:34.037651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-09T20:37:29.523862Z digest=sha256:b922c7084340235d713eaf7eaf2a14207aa0c378ee1afd89f0897b2c4ddab605

Observation 419ac9ac-5ab2-4101-9efb-368482e5cbe6 · outbound

This paper cites Introduction to online convex optimization,.

Forgetting-Factor Regret for Online Zero-Sum Games Introduction to online convex optimization,

Reference 33

Resolution
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raw_fallback, observed 2026-07-09T20:46:34.047542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-09T20:37:29.523862Z digest=sha256:8bbe2ab88718a36488626af33e8976e6467655644f1cd06185990d40ad11e0c4

Observation 6b658fd8-a194-4b1f-bbb0-1f5fc8ea9c32 · outbound

This paper cites Dynamic regret of strongly adaptive methods,.

Forgetting-Factor Regret for Online Zero-Sum Games Dynamic regret of strongly adaptive methods,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T20:46:34.088962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-09T20:37:29.523862Z digest=sha256:38c1d3d118c6d0a45a66f521b16f361dfafb69e80db5eb491bdd610668b2b78b

Observation cc215563-d0e8-43ad-b963-d826e53b0cd8 · outbound

This paper cites An online optimization approach for multi-agent tracking of dynamic parameters in the presence of adversarial noise,.

Forgetting-Factor Regret for Online Zero-Sum Games An online optimization approach for multi-agent tracking of dynamic parameters in the presence of adversarial noise,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T20:46:34.092299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-09T20:37:29.523862Z digest=sha256:5d9a0a4b993015696937d646f549614baa24a7f8e22e14d81d868b0da5d32f6b

Observation 8cc41aac-0ffb-41c1-9515-a96b88297f2e · outbound

This paper cites The online saddle point problem and online convex optimization with knapsacks,.

Forgetting-Factor Regret for Online Zero-Sum Games The online saddle point problem and online convex optimization with knapsacks,

Reference 36

Resolution
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raw_fallback, observed 2026-07-09T20:46:34.083546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-09T20:37:29.523862Z digest=sha256:8ca4c30775b0ac1de2e18a28613e263f10dd0e966cb313c7fcf66cc0ca9122a6

Observation 4c6f469a-be8b-4c57-8c6a-4105f186735d · outbound

This paper cites An online saddle point optimization algorithm with regularization,.

Forgetting-Factor Regret for Online Zero-Sum Games An online saddle point optimization algorithm with regularization,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T20:46:34.085435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-09T20:37:29.523862Z digest=sha256:5e2a50957fb34ce3a4b17bca43d7a7bf68606ba1cac3128392359b32ec502e36

Observation a529f80b-6b50-44f7-91dc-e78b84b467c7 · outbound

This paper cites Competing Against Equilibria in Zero-Sum Games with Evolving Payoffs.

Forgetting-Factor Regret for Online Zero-Sum Games Competing Against Equilibria in Zero-Sum Games with Evolving Payoffs

Reference 38

Resolution
verified exact
local_arxiv, observed 2026-07-09T20:46:33.691148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-09T20:37:29.523862Z digest=sha256:e85df5709b846bdef2ce2ed837e3aa7a4cc5999bb974bc11fe9fa519385819bd

Observation f46aeb6f-5868-4a84-9993-2b69eb34babb · outbound

This paper cites Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization.

Forgetting-Factor Regret for Online Zero-Sum Games Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization

Reference 39

Resolution
verified exact
local_arxiv, observed 2026-07-09T20:46:33.693661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-09T20:37:29.523862Z digest=sha256:54e83f17a76a2606316d62315a79814d416aced930a81e9c059953acf0688605

Observation 0057f699-bf83-408c-b188-7e5fbe96d577 · outbound

This paper cites No-regret learning in time-varying zero-sum games,.

Forgetting-Factor Regret for Online Zero-Sum Games No-regret learning in time-varying zero-sum games,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T20:46:34.071389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-09T20:37:29.523862Z digest=sha256:e953a0ba9d810f03370dda9a1d518239b41cc6e79f68b8a10f941dba94611a7a

Observation 0739d860-98fe-41ee-9a72-e7e73c51ff97 · outbound

This paper cites Online Saddle Point Problem and Online Convex-Concave Optimization.

Forgetting-Factor Regret for Online Zero-Sum Games Online Saddle Point Problem and Online Convex-Concave Optimization

Reference 41

Resolution
verified exact
local_arxiv, observed 2026-07-09T20:46:33.688834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-09T20:37:29.523862Z digest=sha256:3b2c310b3008ae30dd1971360c20c84cd14be332458d243a88db3a03ee096b03

Observation 2137e4ff-aada-4c57-b0d8-b200817d1936 · outbound

This paper cites Forgetting-factor regrets for online convex optimization,.

Forgetting-Factor Regret for Online Zero-Sum Games Forgetting-factor regrets for online convex optimization,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T20:46:34.054098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-09T20:37:29.523862Z digest=sha256:c6385cc5bdb14e7da95f999876edc7a979fb9faba355fb64a06f0b313009a5ff

Observation c8c0d506-450b-4ce4-a98f-47b172dc6732 · outbound

This paper cites Projection-free online learning,.

Forgetting-Factor Regret for Online Zero-Sum Games Projection-free online learning,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T20:46:34.055807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-09T20:37:29.523862Z digest=sha256:8707f6df7033c0e036a79bf2538f3ed26773e84e151e7bb8129c85728c442228

Observation a05819ed-a137-4f38-b255-26b423a1c4e3 · outbound

This paper cites Distributed convergence to Nash equilibria in two-network zero-sum games,.

Forgetting-Factor Regret for Online Zero-Sum Games Distributed convergence to Nash equilibria in two-network zero-sum games,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T20:46:34.057497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-09T20:37:29.523862Z digest=sha256:7ba94b7ee38444b07c0a6a576c501e9da8ff50df90cdd9aa770f85e8ff5d0e5c

Observation 521efc67-e852-44ad-9b06-5cca440fbdee · outbound

This paper cites Distributed stochastic subgradient projection algorithms for convex optimization,.

Forgetting-Factor Regret for Online Zero-Sum Games Distributed stochastic subgradient projection algorithms for convex optimization,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T20:46:34.059139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-09T20:37:29.523862Z digest=sha256:45bb562fc27c98cfb49d7603b86b8dea254ad61685e2d57ecedff45c846ff81c

Pith citing papers

No inbound Pith citation observations are available.