Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search

Stephen Mussmann, Daniel Levy, Stefano Ermon
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:371-380, 2017.

Abstract

Inference in log-linear models scales linearly in the size of output space in the worst-case. This is often a bottleneck in natural language processing and computer vision tasks when the output space is feasibly enumerable but very large. We propose a method to per-form inference in log-linear models with sub-linear amortized cost. Our idea hinges on using Gumbel random variable perturbations and a pre-computed Maximum Inner Product Search data structure to access the most-likely elements in sublinear amortized time. Our method yields provable runtime and accuracy guarantees. Further, we present empirical experiments on ImageNet and Word Embeddings showing significant speedups for sampling, inference, and learning in log-linear models.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-mussmann17a, title = {Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search}, author = {Mussmann, Stephen and Levy, Daniel and Ermon, Stefano}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {371--380}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/mussmann17a/mussmann17a.pdf}, url = {https://proceedings.mlr.press/r15/mussmann17a.html}, abstract = {Inference in log-linear models scales linearly in the size of output space in the worst-case. This is often a bottleneck in natural language processing and computer vision tasks when the output space is feasibly enumerable but very large. We propose a method to per-form inference in log-linear models with sub-linear amortized cost. Our idea hinges on using Gumbel random variable perturbations and a pre-computed Maximum Inner Product Search data structure to access the most-likely elements in sublinear amortized time. Our method yields provable runtime and accuracy guarantees. Further, we present empirical experiments on ImageNet and Word Embeddings showing significant speedups for sampling, inference, and learning in log-linear models.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search %A Stephen Mussmann %A Daniel Levy %A Stefano Ermon %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-mussmann17a %I PMLR %P 371--380 %U https://proceedings.mlr.press/r15/mussmann17a.html %V R15 %X Inference in log-linear models scales linearly in the size of output space in the worst-case. This is often a bottleneck in natural language processing and computer vision tasks when the output space is feasibly enumerable but very large. We propose a method to per-form inference in log-linear models with sub-linear amortized cost. Our idea hinges on using Gumbel random variable perturbations and a pre-computed Maximum Inner Product Search data structure to access the most-likely elements in sublinear amortized time. Our method yields provable runtime and accuracy guarantees. Further, we present empirical experiments on ImageNet and Word Embeddings showing significant speedups for sampling, inference, and learning in log-linear models. %Z Reissued by PMLR on 04 October 2026.
APA
Mussmann, S., Levy, D. & Ermon, S.. (2017). Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:371-380 Available from https://proceedings.mlr.press/r15/mussmann17a.html. Reissued by PMLR on 04 October 2026.

Related Material