Establishes maximal concentration bounds for stochastic approximation under heavy-tailed Markovian noise, with tails ranging from sub-Gaussian to heavier than Weibull depending on step sizes and contractivity properties, plus a truncation argument for unbounded noise.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Meta-prompt optimization enables LLM agents to discover stable, generalizable tacit collusion strategies in market simulations that outperform hand-crafted prompt baselines.
Using common random numbers in rollout simulations provably reduces variance in relative utility estimates when a rollout policy is invoked beyond some depth.
citing papers explorer
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Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise
Establishes maximal concentration bounds for stochastic approximation under heavy-tailed Markovian noise, with tails ranging from sub-Gaussian to heavier than Weibull depending on step sizes and contractivity properties, plus a truncation argument for unbounded noise.
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Prompt Optimization Enables Stable Algorithmic Collusion in LLM Agents
Meta-prompt optimization enables LLM agents to discover stable, generalizable tacit collusion strategies in market simulations that outperform hand-crafted prompt baselines.
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Using Common Random Numbers for Simulation-based Planning with Rollouts
Using common random numbers in rollout simulations provably reduces variance in relative utility estimates when a rollout policy is invoked beyond some depth.