Discounted i.i.d. rewards force prophet inequality competitive ratios down to 1/2, as hard as the non-i.i.d. case, with matching upper and lower bounds via calibrated single-quantile thresholds.
arXiv preprint arXiv:2505.18828 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Introduces a parametric reservation-index policy with GMM estimation and UCB exploration for contextual LLM cascading under output-mediated feedback, claiming dimension-dependent square-root regret.
Characterizes the optimal asymptotic competitive ratio for parametric prophet inequalities and proposes an online confidence-based DP policy achieving it without offline samples.
citing papers explorer
-
I.i.d. Prophet Inequalities with Discounted Rewards: As Hard as the Non-i.i.d. Case
Discounted i.i.d. rewards force prophet inequality competitive ratios down to 1/2, as hard as the non-i.i.d. case, with matching upper and lower bounds via calibrated single-quantile thresholds.
-
Online Pandora's Box for Contextual LLM Cascading
Introduces a parametric reservation-index policy with GMM estimation and UCB exploration for contextual LLM cascading under output-mediated feedback, claiming dimension-dependent square-root regret.
-
Asymptotically Optimal Learning for Parametric Prophet Inequalities
Characterizes the optimal asymptotic competitive ratio for parametric prophet inequalities and proposes an online confidence-based DP policy achieving it without offline samples.