Paper Citation Record · LEDGER

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods

As of 10 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2508.08413.

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

pith.paper-citation-record.v1
2508.08413 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:34:30.560906Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

19 of 19 outbound references displayed

verified exact5
verified fuzzy1
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5655493b-3f9c-4aad-bb32-617292eccf8e · outbound

This paper cites What Doubling Tricks Can and Can't Do for Multi-Armed Bandits.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods What Doubling Tricks Can and Can't Do for Multi-Armed Bandits

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T21:34:28.978718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d39d22c6-38e4-4380-9d43-94dacf5e8240 · outbound

This paper cites Transfer Learning for Contextual Multi-armed Bandits.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Transfer Learning for Contextual Multi-armed Bandits

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-05T21:34:31.458090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 20778f5a-b127-40ca-b80e-33bebfcbb05b · outbound

This paper cites Leveraging (biased) information: Multi-armed bandits with offline data.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Leveraging (biased) information: Multi-armed bandits with offline data

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T21:34:29.235518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf45d9dc-deaf-4358-abb9-216f84744679 · outbound

This paper cites Online Meta-Learning in Adversarial Multi-Armed Bandits.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Online Meta-Learning in Adversarial Multi-Armed Bandits

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-05T21:34:30.919358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 304b8f12-fdd3-4811-a784-943619dfdac6 · outbound

This paper cites Balancing optimism and pessimism in offline-to-online learning.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Balancing optimism and pessimism in offline-to-online learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T21:34:29.944064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 646788d6-5594-40d9-a671-2ba2e2fb386a · outbound

This paper cites Leveraging Offline Data in Online Reinforcement Learning.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Leveraging Offline Data in Online Reinforcement Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T21:34:30.394721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c43d2d3a-9d8b-4a65-b27d-f08ea8f332e2 · outbound

This paper cites Best Arm Identification with Possibly Biased Offline Data.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Best Arm Identification with Possibly Biased Offline Data

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T21:34:30.560906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 740e932a-1f0b-4b67-9824-4e70b049ec78 · outbound

This paper cites Meta-Learning Adversarial Bandits.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Meta-Learning Adversarial Bandits

Reference 2002

Resolution: verified exact; local_arxiv, observed 2026-08-05T21:34:31.670509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2cdf827e-46dd-4e3d-abe5-0fefb13351d1 · outbound

This paper cites Leveraging Offline Data in Linear Latent Contextual Bandits.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Leveraging Offline Data in Linear Latent Contextual Bandits

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-05T21:34:29.314573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 999b1d41-92d8-4b78-9603-c90c16f0dd28 · outbound

This paper cites Online Bandit Learning with Offline Preference Data for Improved RLHF.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Online Bandit Learning with Offline Preference Data for Improved RLHF

Reference 2011

Resolution: unresolved; no resolver link, observed 2026-08-05T21:34:28.208406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71bd113b-88f0-4ca3-a984-ec12b73c2e29 · outbound

This paper cites Optimal Best-Arm Identification in Bandits with Access to Offline Data.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Optimal Best-Arm Identification in Bandits with Access to Offline Data

Reference 2012

Resolution: verified exact; local_arxiv, observed 2026-08-05T21:34:31.872381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 412ddb58-3fe5-4cfd-84a0-dfdd76f85eca · outbound

This paper cites Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning

Reference 2013

Resolution: verified exact; local_arxiv, observed 2026-08-05T21:34:31.049836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 54d71233-b41f-407d-9024-bd9fa7794d14 · outbound

This paper cites Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-05T21:34:30.243849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9dcd4b8-2629-4e3d-adee-59b9053a8190 · outbound

This paper cites On Frequentist Regret of Linear Thompson Sampling.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods On Frequentist Regret of Linear Thompson Sampling

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-05T21:34:29.277153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe62eb4f-92a2-4514-a7ef-158472a4406b · outbound

This paper cites Bandit Algorithms for Precision Medicine.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Bandit Algorithms for Precision Medicine

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-05T21:34:29.559992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ba8ead0-4718-4759-9bea-5837bfe5d9eb · outbound

This paper cites Some aspects of the sequential design of experiments.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Some aspects of the sequential design of experiments

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:34:32.069251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 19b8f177-1080-4532-812f-1a5351fb1d6d · outbound

This paper cites Efficient Online Reinforcement Learning with Offline Data.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Efficient Online Reinforcement Learning with Offline Data

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-05T21:34:28.644931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14dc6f08-35d7-4a9c-b466-d592d75e5a64 · outbound

This paper cites Artificial Replay: A Meta-Algorithm for Harnessing Historical Data in Bandits.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Artificial Replay: A Meta-Algorithm for Harnessing Historical Data in Bandits

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-05T21:34:28.800001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce2bea9f-586e-443c-ac30-d1b65fea1109 · outbound

This paper cites Bandits with Mean Bounds.

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods Bandits with Mean Bounds

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-05T21:34:30.062073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.