no code implementations • 23 Feb 2024 • Sungwoo Park, Junyeop Kwon, Byeongnoh Kim, Suhyun Chae, Jeeyong Lee, Dabeen Lee
We provide experimental results that demonstrate the numerical superiority of our algorithms over the existing method and other black-box optimistic optimization methods.
no code implementations • 7 Dec 2023 • Yeongjong Kim, Dabeen Lee
This paper considers stochastic-constrained stochastic optimization where the stochastic constraint is to satisfy that the expectation of a random function is below a certain threshold.
no code implementations • 18 May 2023 • Duksang Lee, William Overman, Dabeen Lee
For the observe-then-decide regime, we prove that the expected regret against the dynamic clairvoyant optimal policy is bounded by $\tilde O(\rho^{-1}{H^{3/2}}S\sqrt{AT})$ where $\rho\in(0, 1)$ is the budget parameter, $H$ is the length of the horizon, $S$ and $A$ are the numbers of states and actions, and $T$ is the number of episodes.
no code implementations • 2 May 2023 • Duksang Lee, Nam Ho-Nguyen, Dabeen Lee
This paper develops projection-free algorithms for online convex optimization with stochastic constraints.
no code implementations • 26 Jan 2023 • Yeongjong Kim, Dabeen Lee
We propose a variant of the drift-plus-penalty algorithm that guarantees $O(\sqrt{T})$ expected regret and zero constraint violation, after a fixed number of iterations, which improves the vanilla drift-plus-penalty method with $O(\sqrt{T})$ constraint violation.
1 code implementation • NeurIPS 2021 • Dabeen Lee, Milan Vojnovic
Our numerical results demonstrate the efficacy of our algorithms and show that our regret analysis is nearly tight.
1 code implementation • 30 Dec 2020 • Dabeen Lee, Milan Vojnovic, Se-Young Yun
Motivated by recent developments in designing algorithms based on individual item scores for solving utility maximization problems, we study the framework of using test scores, defined as a statistic of observed individual item performance data, for solving the budgeted stochastic utility maximization problem.