Learning Where to Sample in Structured Prediction

Tianlin Shi, Jacob Steinhardt, Percy Liang
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, PMLR 38:875-884, 2015.

Abstract

In structured prediction, most inference algorithms allocate a homogeneous amount of computation to all parts of the output, which can be wasteful when different parts vary widely in terms of difficulty. In this paper, we propose a heterogeneous approach that dynamically allocates computation to the different parts. Given a pre-trained model, we tune its inference algorithm (a sampler) to increase test-time throughput. The inference algorithm is parametrized by a meta-model and trained via reinforcement learning, where actions correspond to sampling candidate parts of the output, and rewards are log-likelihood improvements. The meta-model is based on a set of domain-general meta-features capturing the progress of the sampler. We test our approach on five datasets and show that it attains the same accuracy as Gibbs sampling but is 2 to 5 times faster.

Cite this Paper

BibTeX
@InProceedings{pmlr-v38-shi15, title = {{Learning Where to Sample in Structured Prediction}}, author = {Shi, Tianlin and Steinhardt, Jacob and Liang, Percy}, booktitle = {Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics}, pages = {875--884}, year = {2015}, editor = {Lebanon, Guy and Vishwanathan, S. V. N.}, volume = {38}, series = {Proceedings of Machine Learning Research}, address = {San Diego, California, USA}, month = {09--12 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v38/shi15.pdf}, url = {https://proceedings.mlr.press/v38/shi15.html}, abstract = {In structured prediction, most inference algorithms allocate a homogeneous amount of computation to all parts of the output, which can be wasteful when different parts vary widely in terms of difficulty. In this paper, we propose a heterogeneous approach that dynamically allocates computation to the different parts. Given a pre-trained model, we tune its inference algorithm (a sampler) to increase test-time throughput. The inference algorithm is parametrized by a meta-model and trained via reinforcement learning, where actions correspond to sampling candidate parts of the output, and rewards are log-likelihood improvements. The meta-model is based on a set of domain-general meta-features capturing the progress of the sampler. We test our approach on five datasets and show that it attains the same accuracy as Gibbs sampling but is 2 to 5 times faster.} }
Endnote
%0 Conference Paper %T Learning Where to Sample in Structured Prediction %A Tianlin Shi %A Jacob Steinhardt %A Percy Liang %B Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2015 %E Guy Lebanon %E S. V. N. Vishwanathan %F pmlr-v38-shi15 %I PMLR %P 875--884 %U https://proceedings.mlr.press/v38/shi15.html %V 38 %X In structured prediction, most inference algorithms allocate a homogeneous amount of computation to all parts of the output, which can be wasteful when different parts vary widely in terms of difficulty. In this paper, we propose a heterogeneous approach that dynamically allocates computation to the different parts. Given a pre-trained model, we tune its inference algorithm (a sampler) to increase test-time throughput. The inference algorithm is parametrized by a meta-model and trained via reinforcement learning, where actions correspond to sampling candidate parts of the output, and rewards are log-likelihood improvements. The meta-model is based on a set of domain-general meta-features capturing the progress of the sampler. We test our approach on five datasets and show that it attains the same accuracy as Gibbs sampling but is 2 to 5 times faster.
RIS
TY - CPAPER TI - Learning Where to Sample in Structured Prediction AU - Tianlin Shi AU - Jacob Steinhardt AU - Percy Liang BT - Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics DA - 2015/02/21 ED - Guy Lebanon ED - S. V. N. Vishwanathan ID - pmlr-v38-shi15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 38 SP - 875 EP - 884 L1 - http://proceedings.mlr.press/v38/shi15.pdf UR - https://proceedings.mlr.press/v38/shi15.html AB - In structured prediction, most inference algorithms allocate a homogeneous amount of computation to all parts of the output, which can be wasteful when different parts vary widely in terms of difficulty. In this paper, we propose a heterogeneous approach that dynamically allocates computation to the different parts. Given a pre-trained model, we tune its inference algorithm (a sampler) to increase test-time throughput. The inference algorithm is parametrized by a meta-model and trained via reinforcement learning, where actions correspond to sampling candidate parts of the output, and rewards are log-likelihood improvements. The meta-model is based on a set of domain-general meta-features capturing the progress of the sampler. We test our approach on five datasets and show that it attains the same accuracy as Gibbs sampling but is 2 to 5 times faster. ER -
APA
Shi, T., Steinhardt, J. & Liang, P.. (2015). Learning Where to Sample in Structured Prediction. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 38:875-884 Available from https://proceedings.mlr.press/v38/shi15.html.

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