Search Results for author: Dabeen Lee

Found 7 papers, 2 papers with code

Parameter-Free Algorithms for Performative Regret Minimization under Decision-Dependent Distributions

no code implementations23 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.

Stochastic Optimization

Stochastic-Constrained Stochastic Optimization with Markovian Data

no code implementations7 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.

Fairness Stochastic Optimization

Online Resource Allocation in Episodic Markov Decision Processes

no code implementations18 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.

Decision Making

Projection-Free Online Convex Optimization with Stochastic Constraints

no code implementations2 May 2023 Duksang Lee, Nam Ho-Nguyen, Dabeen Lee

This paper develops projection-free algorithms for online convex optimization with stochastic constraints.

Stochastic Optimization

Online Convex Optimization with Stochastic Constraints: Zero Constraint Violation and Bandit Feedback

no code implementations26 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.

LEMMA

Scheduling Jobs with Stochastic Holding Costs

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.

Scheduling

Test Score Algorithms for Budgeted Stochastic Utility Maximization

1 code implementation30 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.

Cannot find the paper you are looking for? You can Submit a new open access paper.