Max-margin learning with the Bayes Factor. Krishnan, R. G., Khandelwal, A., Ranganath, R., & Sontag, D. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2018. ![Max-margin learning with the Bayes Factor [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex We propose a new way to answer probabilistic queries that span multiple datapoints. We formalize reasoning about the similarity of different datapoints as the evaluation of the Bayes Factor within a hierarchical deep generative model that enforces a separation between the latent variables used for representation learning and those used for reasoning. Under this model, we derive an intuitive estimator for the Bayes Factor that represents similarity as the amount of overlap in representation space shared by different points. The estimator we derive relies on a query-conditional latent reasoning network, that parameterizes a distribution over the latent space of the deep generative model. The latent reasoning network is trained to amortize the posterior-predictive distribution under a hierarchical model using supervised data and a max-margin learning algorithm. We explore how the model may be used to focus the data variations captured in the latent space of the deep generative model and how this may be used to build new algorithms for few-shot learning.
@inproceedings{KrishnanEtAl_uai18,
author = {Rahul G. Krishnan and Arjun Khandelwal and Rajesh Ranganath and David Sontag},
title = {Max-margin learning with the Bayes Factor},
booktitle = {Proceedings of the Conference on Uncertainty in Artificial Intelligence ({UAI})},
year = {2018},
keywords = {Machine learning, Unsupervised learning, Deep learning, Approximate inference in graphical models},
abstract = {We propose a new way to answer probabilistic queries that span multiple datapoints. We formalize reasoning about the similarity of different datapoints as the evaluation of the Bayes Factor within a hierarchical deep generative model that enforces a separation between the latent variables used for representation learning and those used for reasoning. Under this model, we derive an intuitive estimator for the Bayes Factor that represents similarity as the amount of overlap in representation space shared by different points. The estimator we derive relies on a query-conditional latent reasoning network, that parameterizes a distribution over the latent space of the deep generative model. The latent reasoning network is trained to amortize the posterior-predictive distribution under a hierarchical model using supervised data and a max-margin learning algorithm. We explore how the model may be used to focus the data variations captured in the latent space of the deep generative model and how this may be used to build new algorithms for few-shot learning.},
url_Paper = {http://people.csail.mit.edu/dsontag/papers/KrishnanEtAl_UAI18.pdf}
}
Downloads: 0
{"_id":"rDKC3haF8nm8fBDQY","bibbaseid":"krishnan-khandelwal-ranganath-sontag-maxmarginlearningwiththebayesfactor-2018","author_short":["Krishnan, R. G.","Khandelwal, A.","Ranganath, R.","Sontag, D."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Rahul","G."],"propositions":[],"lastnames":["Krishnan"],"suffixes":[]},{"firstnames":["Arjun"],"propositions":[],"lastnames":["Khandelwal"],"suffixes":[]},{"firstnames":["Rajesh"],"propositions":[],"lastnames":["Ranganath"],"suffixes":[]},{"firstnames":["David"],"propositions":[],"lastnames":["Sontag"],"suffixes":[]}],"title":"Max-margin learning with the Bayes Factor","booktitle":"Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI)","year":"2018","keywords":"Machine learning, Unsupervised learning, Deep learning, Approximate inference in graphical models","abstract":"We propose a new way to answer probabilistic queries that span multiple datapoints. We formalize reasoning about the similarity of different datapoints as the evaluation of the Bayes Factor within a hierarchical deep generative model that enforces a separation between the latent variables used for representation learning and those used for reasoning. Under this model, we derive an intuitive estimator for the Bayes Factor that represents similarity as the amount of overlap in representation space shared by different points. The estimator we derive relies on a query-conditional latent reasoning network, that parameterizes a distribution over the latent space of the deep generative model. The latent reasoning network is trained to amortize the posterior-predictive distribution under a hierarchical model using supervised data and a max-margin learning algorithm. We explore how the model may be used to focus the data variations captured in the latent space of the deep generative model and how this may be used to build new algorithms for few-shot learning.","url_paper":"http://people.csail.mit.edu/dsontag/papers/KrishnanEtAl_UAI18.pdf","bibtex":"@inproceedings{KrishnanEtAl_uai18,\n author = {Rahul G. Krishnan and Arjun Khandelwal and Rajesh Ranganath and David Sontag},\n title = {Max-margin learning with the Bayes Factor},\n booktitle = {Proceedings of the Conference on Uncertainty in Artificial Intelligence ({UAI})},\n year = {2018},\n keywords = {Machine learning, Unsupervised learning, Deep learning, Approximate inference in graphical models},\n abstract = {We propose a new way to answer probabilistic queries that span multiple datapoints. We formalize reasoning about the similarity of different datapoints as the evaluation of the Bayes Factor within a hierarchical deep generative model that enforces a separation between the latent variables used for representation learning and those used for reasoning. Under this model, we derive an intuitive estimator for the Bayes Factor that represents similarity as the amount of overlap in representation space shared by different points. The estimator we derive relies on a query-conditional latent reasoning network, that parameterizes a distribution over the latent space of the deep generative model. The latent reasoning network is trained to amortize the posterior-predictive distribution under a hierarchical model using supervised data and a max-margin learning algorithm. We explore how the model may be used to focus the data variations captured in the latent space of the deep generative model and how this may be used to build new algorithms for few-shot learning.},\n url_Paper = {http://people.csail.mit.edu/dsontag/papers/KrishnanEtAl_UAI18.pdf}\n}\n\n","author_short":["Krishnan, R. G.","Khandelwal, A.","Ranganath, R.","Sontag, D."],"key":"KrishnanEtAl_uai18","id":"KrishnanEtAl_uai18","bibbaseid":"krishnan-khandelwal-ranganath-sontag-maxmarginlearningwiththebayesfactor-2018","role":"author","urls":{" paper":"http://people.csail.mit.edu/dsontag/papers/KrishnanEtAl_UAI18.pdf"},"keyword":["Machine learning","Unsupervised learning","Deep learning","Approximate inference in graphical models"],"metadata":{"authorlinks":{}},"downloads":0,"html":""},"bibtype":"inproceedings","biburl":"http://people.csail.mit.edu/dsontag/papers/bibtex/david_sontag_papers_all.bib","dataSources":["g3ofqhxNQWsRWkCrp"],"keywords":["machine learning","unsupervised learning","deep learning","approximate inference in graphical models"],"search_terms":["max","margin","learning","bayes","factor","krishnan","khandelwal","ranganath","sontag"],"title":"Max-margin learning with the Bayes Factor","year":2018}