A Thompson sampling algorithm jointly infers unknown network interference and learns optimal individual-level treatments, with sublinear regret for additive spillover models and an explore-then-commit variant for general neighborhood interference.
n= 20n= 40 Median regret (IQR) Known-A TS 57 [40, 67] 56 [50, 59] Gibbs-TS 351 [302, 425] 1, 441 [1
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Adaptive Policy Learning Under Unknown Network Interference
A Thompson sampling algorithm jointly infers unknown network interference and learns optimal individual-level treatments, with sublinear regret for additive spillover models and an explore-then-commit variant for general neighborhood interference.