6 papers
How much Data do We Need? Sequential Data Collection for Stochastic Programming
Xin Li, Juergen Branke, Xuan Vinh Doan
Data-driven optimization often requires collecting data to estimate uncertain model parameters before solving the underlying decision problem. In practice, however, data acquisitio…
Annealed Entropic Allocation for Ranking and Selection
Xin Fei, Juergen Branke
We propose annealed entropic allocation, an adaptive sampling policy based on an annealed, weighted soft-min formulation of static budget allocation. We replace the maximin large-d…
Bayesian Optimization with Preference Exploration using a Monotonic Neural Network Ensemble
Hanyang Wang, Juergen Branke, Matthias Poloczek
Many real-world black-box optimization problems have multiple conflicting objectives. Rather than attempting to approximate the entire set of Pareto-optimal solutions, interactive…
Learning in Repeated Multi-Objective Stackelberg Games with Payoff Manipulation
Phurinut Srisawad, Juergen Branke, Long Tran-Thanh
We study payoff manipulation in repeated multi-objective Stackelberg games, where a leader may strategically influence a follower's deterministic best response, e.g., by offering a…
Respecting the limit:Bayesian optimization with a bound on the optimal value
Hanyang Wang, Juergen Branke, Matthias Poloczek
In many real-world optimization problems, we have prior information about what objective function values are achievable. In this paper, we study the scenario that we have either ex…
Bayesian Optimization of Bilevel Problems
Omer Ekmekcioglu, Nursen Aydin, Juergen Branke
Bilevel optimization, a hierarchical mathematical framework where one optimization problem is nested within another, has emerged as a powerful tool for modeling complex decision-ma…