Rudi, A;
Ciliberto, C;
Marconi, GM;
Rosasco, L;
(2018)
Manifold Structured Prediction.
In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.)
Advances In Neural Information Processing Systems 31 (Nips 2018).
Neural Information Processing Systems (NIPS): Montreal, Canada.
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Abstract
Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure. While classical approaches consider finite, albeit potentially huge, output spaces, in this paper we discuss how structured prediction can be extended to a continuous scenario. Specifically, we study a structured prediction approach to manifold valued regression. We characterize a class of problems for which the considered approach is statistically consistent and study how geometric optimization can be used to compute the corresponding estimator. Promising experimental results on both simulated and real data complete our study
| Type: | Proceedings paper |
|---|---|
| Title: | Manifold Structured Prediction |
| Event: | 32nd Conference on Neural Information Processing Systems (NIPS) |
| Location: | Montreal, CANADA |
| Dates: | 02 December 2018 - 08 December 2018 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://papers.nips.cc/paper/2018/hash/f6185f0ef02... |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10118772 |
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