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  1. Home
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  3. RIE-Greedy: Regularization-Induced Exploration for Contextua
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RIE-Greedy: Regularization-Induced Exploration for Contextual Bandits

Stale15d ago
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0.0/10

Compared to this week’s papers

Stale evidence

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: stale

Source paper: RIE-Greedy: Regularization-Induced Exploration for Contextual Bandits

PDF: https://arxiv.org/pdf/2603.11276v1

Source count: 0

Coverage: 33%

Last proof check: 2026-03-19T18:48:05.835Z

Paper Conversation

Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.

Paper Mode

RIE-Greedy: Regularization-Induced Exploration for Contextual Bandits

Overall score: 7/10
Lineage: 6c2bf6ef94ad…
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Canonical Paper Receipt

Last verification: 2026-03-19T18:48:05.835Z

Freshness: stale

Proof: unverified

Repo: missing

References: 0

Sources: 0

Coverage: 33%

Missingness
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Unknowns
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  • Paper mode pins trust state to the canonical paper kernel.
  • Workspace mode blends saved sources, prior evidence queries, and linked papers.

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Dimensions overall score 7.0

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Keep exploring

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Stability and Robustness via Regularization: Bandit Inference via Regularized Stochastic Mirror Descent
Score 4.0down
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Kernel Single-Index Bandits: Estimation, Inference, and Learning
Score 2.0down
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In-Context Learning for Pure Exploration in Continuous Spaces
Score 5.0down
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A Reduction Algorithm for Markovian Contextual Linear Bandits
Score 2.0down
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Decoupling Exploration and Policy Optimization: Uncertainty Guided Tree Search for Hard Exploration
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Optimism Stabilizes Thompson Sampling for Adaptive Inference
Score 2.0down
Prior Work
A Practical Algorithm for Feature-Rich, Non-Stationary Bandit Problems
Score 7.0stable

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