Fetching the paper…
Reading the bibliography…
Machine learning pipelines often rely on optimization procedures to make discrete decisions (e.g., sorting, picking closest neighbors, or shortest paths).
Learning with Fenchel-Young losses
Blondel, M., Martins, A. F., and Niculae, V. (2019) · 1901
Earlier work this paper cites.
Differentiation of blackbox combinatorial solvers
Vlastelica, M., Paulus, A., Musil, V., Martius, G., and Rolínek, M. (2019) · 1912
Earlier work this paper cites.
Label ranking by learning pairwise preferences
Hüllermeier, E., Fürnkranz, J., Cheng, W., and Brinker, K. (2008) · 1916
Earlier work this paper cites.
Ranking via Sinkhorn propagation
Adams, R. P. and Zemel, R. S. (2011) · 1925
Earlier work this paper cites.
Statistical Theory of Extreme Values and some Practical Applications: A Series of Lectures
Gumbel, E. J. (1954) · 1954
Earlier work this paper cites.
Approximation to bayes risk in repeated plays
Hannan, J. (1957) · 1957
Earlier work this paper cites.
Individual choice behavior
Luce, R. D. (1959) · 1959
Earlier work this paper cites.
Conditional logit analysis of qualitative choice behavior
McFadden, D. (1973) · 1973
Earlier work this paper cites.
The complexity of computing the permanent
Valiant, L. G. (1979) · 1979
Earlier work this paper cites.
A logit model of brand choice calibrated on scanner data
Guadagni, P. M. and Little, J. D. (1983) · 1983
Earlier work this paper cites.
Asymptotic Statistics
van der Vaart, A. W. (2000) · 2000
Earlier work this paper cites.
Efficient algorithms for online decision
Kalai, A. and Vempala, S. (2003) · 2003
Earlier work this paper cites.
Combinatorial Optimization: Polyhedra and Efficiency
Schrijver, A. (2003) · 2003
Earlier work this paper cites.
Learning Structured Prediction Models: A Large Margin Approach
Taskar, B. (2004) · 2004
Earlier work this paper cites.
Graphical models, exponential families, and variational inference
Wainwright, M. J. and Jordan, M. I. (2008) · 2008
Cited alongside, same era.
Decision tree and instance-based learning for label ranking
Cheng, W., Hühn, J., and Hüllermeier, E. (2009) · 2009
Cited alongside, same era.
Variational analysis
Rockafellar, R. T. and Wets, R. J.-B. (2009) · 2009
Cited alongside, same era.
Perturb-and-MAP random fields: Using discrete optimization to learn and sample from energy models
Papandreou, G. and Yuille, A. L. (2011) · 2011
Cited alongside, same era.
Optimization with sparsity-inducing penalties
Bach, F., Jenatton, R., Mairal, J., and Obozinski, G. (2012) · 2012
Cited alongside, same era.
The convex geometry of linear inverse problems
Chandrasekaran, V., Recht, B., Parrilo, P. A., and Willsky, A. S. (2012) · 2012
Cited alongside, same era.
Wasserstein barycentric coordinates: histogram regression using optimal transport
Bonneel, N., Peyré, G., and Cuturi, M. (2016) · 2016
Later among the works it cites.
Perturbations, Optimization, and Statistics
Hazan, T., Papandreou, G., and Tarlow, D. (2016) · 2016
Later among the works it cites.
Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B. (2016) · 2016
Later among the works it cites.
The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W. (2016) · 2016
Later among the works it cites.
Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits
Neu, G. and Bartók, G. (2016) · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
On the partition function and random maximum a-posteriori perturbations
Hazan, T. and Jaakkola, T. (2012) · 2012
Cited alongside, same era.
Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M. (2013) · 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) · 2013
Cited alongside, same era.
Online linear optimization via smoothing
Abernethy, J., Lee, C., Sinha, A., and Tewari, A. (2014) · 2014
Cited alongside, same era.
Learning with maximum a-posteriori perturbation models
Gane, A., Hazan, T., and Jaakkola, T. (2014) · 2014
Cited alongside, same era.
On measure concentration of random maximum a-posteriori perturbations
Orabona, F., Hazan, T., Sarwate, A. D., and Jaakkola, T. S. (2014) · 2014
Cited alongside, same era.
Parameter learning for log-supermodular distributions
Shpakova, T. and Bach, F. (2016) · 2016
Later among the works it cites.
Lost relatives of the Gumbel trick
Balog, M., Tripuraneni, N., Ghahramani, Z., and Weller, A. (2017) · 2017
Later among the works it cites.
Differentiable learning of submodular models
Djolonga, J. and Krause, A. (2017) · 2017
Later among the works it cites.
Differentiable dynamic programming for structured prediction and attention
Mensch, A. and Blondel, M. (2018) · 2018
Later among the works it cites.
Sparsemap: Differentiable sparse structured inference
Niculae, V., Martins, A. F., Blondel, M., and Cardie, C. (2018) · 2018
Later among the works it cites.
Differentiable convex optimization layers
Agrawal, A., Amos, B., Barratt, S., Boyd, S., Diamond, S., and Kolter, J. Z. (2019) · 2019
Later among the works it cites.
Structured prediction with projection oracles
Blondel, M. (2019) · 2019
Later among the works it cites.
Direct optimization through argmax for discrete variational auto-encoder
Lorberbom, G., Gane, A., Jaakkola, T., and Hazan, T. (2019) · 2019
Later among the works it cites.
Computational Optimal Transport
Peyré, G. and Cuturi, M. (2019) · 2019
Later among the works it cites.