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Found 12 papers, 6 papers with code
Escaping Local Optima in Global Placement
Placement is crucial in the physical design, as it greatly affects power, performance, and area metrics.
Reinforced In-Context Black-Box Optimization
In this paper, we propose RIBBO, a method to reinforce-learn a BBO algorithm from offline data in an end-to-end fashion.
Quality-Diversity Algorithms Can Provably Be Helpful for Optimization
In this paper, we try to shed some light on the optimization ability of QD algorithms via rigorous running time analysis.
Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimation
Considering that the estimated PDF may have high estimation error when the true distribution is complicated, we further propose the second algorithm that optimizes the distributionally robust objective.
Diversity from Human Feedback
DivHF learns a behavior descriptor consistent with human preference by querying human feedback.
Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy
Machine learning has been adapted to help solve NP-hard combinatorial optimization problems.
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers
Concretely, to avoid the ego-system overfitting to a specific attacker, we maintain a set of attackers, which is optimized to guarantee the attackers high attacking quality and behavior diversity.
Multi-agent Dynamic Algorithm Configuration
MA-DAC formulates the dynamic configuration of a complex algorithm with multiple types of hyperparameters as a contextual multi-agent Markov decision process and solves it by a cooperative multi-agent RL (MARL) algorithm.
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization
Bayesian optimization (BO) is a class of popular methods for expensive black-box optimization, and has been widely applied to many scenarios.
Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution
Generating agents that can achieve zero-shot coordination (ZSC) with unseen partners is a new challenge in cooperative multi-agent reinforcement learning (MARL).
Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning
However, due to the inefficient selection mechanisms, these methods cannot fully guarantee both high quality and diversity.
Bayesian Optimization using Pseudo-Points
Bayesian optimization (BO) is a popular approach for expensive black-box optimization, with applications including parameter tuning, experimental design, robotics.
