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Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits

ICML 2023 · Bandits and online learning › Contextual bandits and Thompson sampling · approach: Linear and generalized linear bandits · 0 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use PASCombUCB with anytime variance-safety checksCombinatorial semi-bandits with variance constraints · PASSCSB problem-dependentexpected cumulative regret: O(∑i∈E\S⋆ K ln T/Δi,S∩B,min + ∑i∈E ci K ln T/Δi,Sc∩B,min + 2μ⋆H(Δ(Λ)) + 2μ⋆(L+1))Helped
Use PASCombUCB with anytime variance-safety checksCombinatorial semi-bandits with variance constraints and T>L · PASSCSB problem-independentexpected cumulative regret: O(√(KLT ln T) + LK²/(Δv)² ln(1/δT))Helped
Require safety probability to approach one exponentially quickly in TAny PASSCSB algorithm · PASSCSB with ln(1/δT)=Ω(T^b)expected cumulative regret: Ω(T^b)Hurt
Use PASCombUCB with a variance threshold that makes all solutions safeSet 1 · Experiment 1, unconstrained caseregretNo clear effect
Apply a restrictive variance threshold requiring safety checksPASCombUCB on Set 1 · Experiment 1, σ̄²=0.6regret: T>4×10^5Mixed
Ignore the anytime safety constraint and use CombUCB1Combinatorial semi-bandits on Set 1 · Experiment 2, σ̄²=0.4safety violations: Ω(T)Hurt
Use PASCombUCB to enforce anytime safetyCombinatorial semi-bandits on Set 1 · Experiment 2, σ̄²=0.4safety violationsHelped
Vary the variance thresholdPASCombUCB on Set 2 · Experiment 3, T=2×10^6additional regret versus 1/(ΔvS)²: barσ²=0.14×1.2^k for k=0,1,…,9Mixed

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