Optimal transport for conditional domain matching and label shift

Domain Adaptation under Target and Conditional Shift

Let X denote the feature and Y the target. We consider domain adaptation under three possible scenarios: (1) the marginal PY changes, while the conditional PX|Y stays the same (target shift), (2) the marginal PY is fixed, while the conditional PX|Y changes with certain constraints (conditional shift), and (3) the marginal PY changes, and the conditional PX|Y changes with constraints (generalize...

Optimal Shift-Or String Matching Algorithm for Multiple Patterns

In this paper, we develop a new algorithm for handling multiple patterns, which is based on average optimal shift-or algorithm. We have assumed that the pattern representation fits into a single computer word and length of each pattern is equal. We have adopted the concept of classes of characters for handling multiple patterns. We compare the performance of the proposed algorithm with the stan...

Label Distribution Learning by Optimal Transport

Label distribution learning (LDL) is a novel learning paradigm to deal with some real-world applications, especially when we care more about the relative importance of different labels in description of an instance. Although some approaches have been proposed to learn the label distribution, they could not explicitly learn and leverage the label correlation, which plays an importance role in LD...

Diffeomorphic Density Matching by Optimal Information Transport

a benchmarking approach to optimal asset allocation for insurers and pension funds

uncertainty in the financial market will be driven by underlying brownian motions, while the assets are assumed to be general stochastic processes adapted to the filtration of the brownian motions. the goal of this study is to calculate the accumulated wealth in order to optimize the expected terminal value using a suitable utility function. this thesis introduced the lim-wong’s benchmark fun...