first commit

Author: aoiang

LaMOO_workflow.png

@@ -0,0 +1,24 @@
+# MULTI-OBJECTIVE OPTIMIZATION BY LEARNING SPACE PARTITIONS (LaMOO)
+
+LaMOO is a a novel multi-objective optimizer that learns a model from observed samples to partition the search space and then focus on promising regions that
+are likely to contain a subset of the Pareto frontier. So that existing solvers like Bayesian Optimizations (BO) can focus on promising subregions to mitigate the over-exploring issue.
+
+<p align="center">
+<img src='./LaMOO_workflow.png' width="800">
+</p>
+
+Please reference the following publication when using this package. OpenReview <a href="https://openreview.net/pdf?id=FlwzVjfMryn">link</a>.
+
+
+```
+@inproceedings{
+zhao2022multiobjective,
+title={Multi-objective Optimization by Learning Space Partition},
+author={Yiyang Zhao and Linnan Wang and Kevin Yang and Tianjun Zhang and Tian Guo and Yuandong Tian},
+booktitle={International Conference on Learning Representations},
+year={2022},
+url={https://openreview.net/forum?id=FlwzVjfMryn}
+}
+```
+
+