Efficient Online Multiclass Prediction on Graphs via Surrogate Losses

Alexander Rakhlin, Karthik Sridharan
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:1403-1411, 2017.

Abstract

We develop computationally efficient algorithms for online multi-class prediction. Our construction is based on carefully-chosen data-dependent surrogate loss functions, and the new methods enjoy strong mistake bound guarantees. To illustrate the technique, we study the combinatorial problem of node classification and develop a prediction strategy that is linear-time per round. In contrast, the offline benchmark is NP-hard to compute in general. We demonstrate the empirical performance of the method on several datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v54-rakhlin17a, title = {{Efficient Online Multiclass Prediction on Graphs via Surrogate Losses}}, author = {Rakhlin, Alexander and Sridharan, Karthik}, booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics}, pages = {1403--1411}, year = {2017}, editor = {Singh, Aarti and Zhu, Jerry}, volume = {54}, series = {Proceedings of Machine Learning Research}, month = {20--22 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v54/rakhlin17a/rakhlin17a.pdf}, url = {https://proceedings.mlr.press/v54/rakhlin17a.html}, abstract = { We develop computationally efficient algorithms for online multi-class prediction. Our construction is based on carefully-chosen data-dependent surrogate loss functions, and the new methods enjoy strong mistake bound guarantees. To illustrate the technique, we study the combinatorial problem of node classification and develop a prediction strategy that is linear-time per round. In contrast, the offline benchmark is NP-hard to compute in general. We demonstrate the empirical performance of the method on several datasets. } }
Endnote
%0 Conference Paper %T Efficient Online Multiclass Prediction on Graphs via Surrogate Losses %A Alexander Rakhlin %A Karthik Sridharan %B Proceedings of the 20th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2017 %E Aarti Singh %E Jerry Zhu %F pmlr-v54-rakhlin17a %I PMLR %P 1403--1411 %U https://proceedings.mlr.press/v54/rakhlin17a.html %V 54 %X We develop computationally efficient algorithms for online multi-class prediction. Our construction is based on carefully-chosen data-dependent surrogate loss functions, and the new methods enjoy strong mistake bound guarantees. To illustrate the technique, we study the combinatorial problem of node classification and develop a prediction strategy that is linear-time per round. In contrast, the offline benchmark is NP-hard to compute in general. We demonstrate the empirical performance of the method on several datasets.
APA
Rakhlin, A. & Sridharan, K.. (2017). Efficient Online Multiclass Prediction on Graphs via Surrogate Losses. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 54:1403-1411 Available from https://proceedings.mlr.press/v54/rakhlin17a.html.

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