Fetching the paper…
Reading the bibliography…
The Gumbel trick is a method to sample from a discrete probability distribution, or to estimate its normalizing partition function.
An inequality satisfied by the Gamma function
Gurland, J · 1956
Earlier work this paper cites.
Analysis of survival data , volume 21
Cox, D. and Oakes, D · 1984
Earlier work this paper cites.
Graphical models
Lauritzen, S · 1998
Earlier work this paper cites.
Numerical optimization
Wright, S. and Nocedal, J · 1999
Earlier work this paper cites.
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., and Zabih, R · 2001
Earlier work this paper cites.
Statistical inference , volume 2
Casella, G. and Berger, R · 2002
Earlier work this paper cites.
Convergent tree-reweighted message passing for energy minimization
Kolmogorov, V · 2006
Earlier work this paper cites.
Graphical Models, Exponential Families, and Variational Inference
Wainwright, M. and Jordan, M · 2008
Earlier work this paper cites.
Global optimization for first order Markov random fields with submodular priors
Darbon, J · 2009
Earlier work this paper cites.
libDAI: A free and open source C++ library for discrete approximate inference in graphical models
Mooij, J · 2010
Cited alongside, same era.
Gaussian sampling by local perturbations
Papandreou, G. and Yuille, A · 2010
Cited alongside, same era.
Perturb-and-MAP random fields: Using discrete optimization to learn and sample from energy models
Papandreou, G. and Yuille, A · 2011
Cited alongside, same era.
On the partition function and random maximum a-posteriori perturbations
Hazan, T. and Jaakkola, T · 2012
Cited alongside, same era.
Randomized optimum models for structured prediction
Tarlow, D., Adams, R., and Zemel, R · 2012
Cited alongside, same era.
Marginal inference in MRFs using Frank-Wolfe
Belanger, D., Sheldon, D., and McCallum, A · 2013
Cited alongside, same era.
On measure concentration of random maximum a-posteriori perturbations
Orabona, F., Hazan, T., Sarwate, A., and Jaakkola, T · 2014
Later among the works it cites.
Barrier Frank-Wolfe for Marginal Inference
Krishnan, Rahul G, Lacoste-Julien, Simon, and Sontag, David · 2015
Later among the works it cites.
Scalable discrete sampling as a multi-armed bandit problem
Chen, Y. and Ghahramani, Z · 2016
Later among the works it cites.
High dimensional inference with random maximum a-posteriori perturbations
Hazan, T., Orabona, F., Sarwate, A., Maji, S., and Jaakkola, T · 2016
Later among the works it cites.
Exact sampling with integer linear programs and random perturbations
Kim, C., Sabharwal, A., and Ermon, S · 2016
Later among the works it cites.
A Poisson process model for Monte Carlo
Maddison, C · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Taming the curse of dimensionality: Discrete integration by hashing and optimization
Ermon, S., Sabharwal, A., and Selman, B · 2013
Cited alongside, same era.
On sampling from the Gibbs distribution with random maximum a-posteriori perturbations
Hazan, T., Maji, S., and Jaakkola, T · 2013
Cited alongside, same era.
Active boundary annotation using random MAP perturbations
Maji, S., Hazan, T., and Jaakkola, T · 2014
Cited alongside, same era.
Approximating the Bethe partition function
Weller, A. and Jebara, T
Cited in the paper.
Clamping variables and approximate inference
Weller, A. and Jebara, T
Cited in the paper.
Later among the works it cites.
Clamping improves TRW and mean field approximations
Weller, A. and Domke, J · 2016
Later among the works it cites.
Variable clamping for optimization-based inference
Zhao, J., Djolonga, J., Tschiatschek, S., and Krause, A · 2016
Later among the works it cites.
Local Perturb-and-MAP for Structured Prediction
Bertasius, G., Liu, Q., Torresani, L., and Shi, J · 2017
Closest in time.