no code implementations • 14 May 2024 • Minbiao Han, Fengxue Zhang, Yuxin Chen
This paper investigates the challenge of learning in black-box games, where the underlying utility function is unknown to any of the agents.
no code implementations • 12 Oct 2023 • Fengxue Zhang, Zejie Zhu, Yuxin Chen
Optimizing objectives under constraints, where both the objectives and constraints are black box functions, is a common scenario in real-world applications such as scientific experimental design, design of medical therapies, and industrial process optimization.
no code implementations • 25 Jul 2023 • Fengxue Zhang, Jialin Song, James Bowden, Alexander Ladd, Yisong Yue, Thomas A. Desautels, Yuxin Chen
Our approach is easy to tune, and is able to focus on local region of the optimization space that can be tackled by existing BO methods.
no code implementations • 16 Mar 2022 • Fengxue Zhang, Brian Nord, Yuxin Chen
We show that even with proper network design, such learned representation often leads to collision in the latent space: two points with significantly different observations collide in the learned latent space, leading to degraded optimization performance.
no code implementations • 1 Jan 2021 • Fengxue Zhang, Yair Altas, Louise Fan, Kaustubh Vinchure, Brian Nord, Yuxin Chen
To address this issue, we propose Collision-Free Latent Space Optimization (CoFLO), which employs a novel regularizer to reduce the collision in the learned latent space and encourage the mapping from the latent space to objective value to be Lipschitz continuous.