Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution
The authors consider models with #latent #permutations and propose control variates for the #PlackettLuce distribution. In particular, the control variates allow them to optimize #blackBox functions over permutations using stochastic gradient descent. To illustrate the approach, they consider a variety of causal structure learning tasks for continuous and discrete data.
They show that the method outperforms competitive relaxation-based optimization methods and is also applicable to non-differentiable score functions.
paper: https://arxiv.org/abs/1911.10036
tweet: https://twitter.com/bayesgroup/status/1199023536653950976?s=20
The authors consider models with #latent #permutations and propose control variates for the #PlackettLuce distribution. In particular, the control variates allow them to optimize #blackBox functions over permutations using stochastic gradient descent. To illustrate the approach, they consider a variety of causal structure learning tasks for continuous and discrete data.
They show that the method outperforms competitive relaxation-based optimization methods and is also applicable to non-differentiable score functions.
paper: https://arxiv.org/abs/1911.10036
tweet: https://twitter.com/bayesgroup/status/1199023536653950976?s=20
