3 accepted papers
What policy should be employed in a Markov decision process with uncertain parameters? Robust optimization answer to this question is to use rectangular uncertainty sets, which independently reflect available knowledge about each state, and then obtains a decision policy that maximizes expected rewa…
In many learning settings, the source data available to train a regression model differs from the target data it encounters when making predictions due to input distribution shift. Appropriately dealing with this situation remains an important challenge. Existing methods attempt to “reweight” the so…
Predictive inverse optimal control is a powerful approach for estimating the control policy of an agent from observed control demonstrations. Its usefulness has been established in a number of large-scale sequential decision settings characterized by complete state observability. However, many real…