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Found 8 papers, 1 papers with code
Optimal Top-Two Method for Best Arm Identification and Fluid Analysis
We show that the proposed algorithm is optimal as $\delta \rightarrow 0$.
CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption
In this setting, accommodating potentially unbounded corruptions, we establish a problem-dependent lower bound on regret for a given family of arm distributions.
Optimal Best-Arm Identification in Bandits with Access to Offline Data
Learning paradigms based purely on offline data as well as those based solely on sequential online learning have been well-studied in the literature.
Agent based simulators for epidemic modelling: Simulating larger models using smaller ones
They provide the flexibility to accurately model a heterogeneous population with time and location varying, person-specific interactions as well as detailed governmental mobility restrictions.
Regret Minimization in Heavy-Tailed Bandits
We show that our index concentrates faster than the well known truncated or trimmed empirical mean estimators for the mean of heavy-tailed distributions.
Optimal Best-Arm Identification Methods for Tail-Risk Measures
Conditional value-at-risk (CVaR) and value-at-risk (VaR) are popular tail-risk measures in finance and insurance industries as well as in highly reliable, safety-critical uncertain environments where often the underlying probability distributions are heavy-tailed.
City-Scale Agent-Based Simulators for the Study of Non-Pharmaceutical Interventions in the Context of the COVID-19 Epidemic
We highlight the usefulness of city-scale agent-based simulators in studying various non-pharmaceutical interventions to manage an evolving pandemic.
Populations and Evolution Other Computer Science Physics and Society Quantitative Methods
Optimal $δ$-Correct Best-Arm Selection for Heavy-Tailed Distributions
We then propose a $\delta$-correct algorithm that matches the lower bound as $\delta$ reduces to zero under the mild restriction that a known bound on the expectation of $(1+\epsilon)^{th}$ moment of the underlying random variables exists, for $\epsilon > 0$.
