Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms

Chaosheng Dong, Bo Zeng
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2648-2657, 2020.

Abstract

We consider a new unsupervised learning task of inferring parameters of a multiobjective decision making model, based on a set of observed decisions from the human expert. This setting is important in applications (such as the task of portfolio management) where it may be difficult to obtain the human expert’s intrinsic decision making model. We formulate such a learning problem as an inverse multiobjective optimization problem (IMOP) and propose its first sophisticated model with statistical guarantees. Then, we reveal several fundamental connections between IMOP, K-means clustering, and manifold learning. Leveraging these critical insights and connections, we propose two algorithms to solve IMOP through manifold learning and clustering. Numerical results confirm the effectiveness of our model and the computational efficacy of algorithms.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-dong20f, title = {Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms}, author = {Dong, Chaosheng and Zeng, Bo}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {2648--2657}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/dong20f/dong20f.pdf}, url = {https://proceedings.mlr.press/v119/dong20f.html}, abstract = {We consider a new unsupervised learning task of inferring parameters of a multiobjective decision making model, based on a set of observed decisions from the human expert. This setting is important in applications (such as the task of portfolio management) where it may be difficult to obtain the human expert’s intrinsic decision making model. We formulate such a learning problem as an inverse multiobjective optimization problem (IMOP) and propose its first sophisticated model with statistical guarantees. Then, we reveal several fundamental connections between IMOP, K-means clustering, and manifold learning. Leveraging these critical insights and connections, we propose two algorithms to solve IMOP through manifold learning and clustering. Numerical results confirm the effectiveness of our model and the computational efficacy of algorithms.} }
Endnote
%0 Conference Paper %T Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms %A Chaosheng Dong %A Bo Zeng %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-dong20f %I PMLR %P 2648--2657 %U https://proceedings.mlr.press/v119/dong20f.html %V 119 %X We consider a new unsupervised learning task of inferring parameters of a multiobjective decision making model, based on a set of observed decisions from the human expert. This setting is important in applications (such as the task of portfolio management) where it may be difficult to obtain the human expert’s intrinsic decision making model. We formulate such a learning problem as an inverse multiobjective optimization problem (IMOP) and propose its first sophisticated model with statistical guarantees. Then, we reveal several fundamental connections between IMOP, K-means clustering, and manifold learning. Leveraging these critical insights and connections, we propose two algorithms to solve IMOP through manifold learning and clustering. Numerical results confirm the effectiveness of our model and the computational efficacy of algorithms.
APA
Dong, C. & Zeng, B.. (2020). Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:2648-2657 Available from https://proceedings.mlr.press/v119/dong20f.html.

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