AISTATS 2024poster0 citations
Pessimistic Off-Policy Multi-Objective Optimization
Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain, Branislav Kveton, Ge Liu
Abstract
Multi-objective optimization is a class of optimization problems with multiple conflicting objectives. We study offline optimization of multi-objective policies from data collected by a previously deployed policy. We propose a pessimistic estimator for policy values that can be easily plugged into existing formulas for hypervolume computation and optimized. The estimator is based on inverse propensity scores (IPS), and improves upon a naive IPS estimator in both theory and experiments. Our analysis is general, and applies beyond our IPS estimators and methods for optimizing them.
BibTeX
@InProceedings{pmlr-v238-alizadeh24a,
title = {Pessimistic Off-Policy Multi-Objective Optimization},
author = {Alizadeh, Shima and Bhargava, Aniruddha and Gopalswamy, Karthick and Jain, Lalit and Kveton, Branislav and Liu, Ge},
booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
pages = {2980--2988},
year = {2024},
editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen},
volume = {238},
series = {Proceedings of Machine Learning Research},
month = {02--04 May},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v238/alizadeh24a/alizadeh24a.pdf},
url = {https://proceedings.mlr.press/v238/alizadeh24a.html},
abstract = {Multi-objective optimization is a class of optimization problems with multiple conflicting objectives. We study offline optimization of multi-objective policies from data collected by a previously deployed policy. We propose a pessimistic estimator for policy values that can be easily plugged into existing formulas for hypervolume computation and optimized. The estimator is based on inverse propensity scores (IPS), and improves upon a naive IPS estimator in both theory and experiments. Our analysis is general, and applies beyond our IPS estimators and methods for optimizing them.}
}