%0 Journal Article %1 sun2019informationgeometric %A Sun, Ke %A Nielsen, Frank %D 2019 %K information probability theory %T Information-Geometric Set Embeddings (IGSE): From Sets to Probability Distributions %U http://arxiv.org/abs/1911.12463 %X This letter introduces an abstract learning problem called the ``set embedding'': The objective is to map sets into probability distributions so as to lose less information. We relate set union and intersection operations with corresponding interpolations of probability distributions. We also demonstrate a preliminary solution with experimental results on toy set embedding examples.

@article{sun2019informationgeometric, abstract = {This letter introduces an abstract learning problem called the ``set embedding'': The objective is to map sets into probability distributions so as to lose less information. We relate set union and intersection operations with corresponding interpolations of probability distributions. We also demonstrate a preliminary solution with experimental results on toy set embedding examples.}, added-at = {2019-12-11T14:16:35.000+0100}, author = {Sun, Ke and Nielsen, Frank}, biburl = {https://www.bibsonomy.org/bibtex/2e6fadd7d56524de45d35d7ea1fa757ed/kirk86}, description = {[1911.12463] Information-Geometric Set Embeddings (IGSE): From Sets to Probability Distributions}, interhash = {bdd170de1ae79506b7eecb19f7e4889c}, intrahash = {e6fadd7d56524de45d35d7ea1fa757ed}, keywords = {information probability theory}, note = {cite arxiv:1911.12463Comment: To be presented at Sets & Partitions (NeurIPS 2019 workshop)}, timestamp = {2019-12-11T14:16:35.000+0100}, title = {Information-Geometric Set Embeddings (IGSE): From Sets to Probability Distributions}, url = {http://arxiv.org/abs/1911.12463}, year = 2019 }