Learning Graphical Models
48f7d3043bc03e6c48a6f0ebc0f258a8-AuthorFeedback.pdf
We thank all reviewers for thoughtful feedback! We reply separately to each reviewer. Reviewer #1: We would like to point out some of the paper's main contributions, not fully recognized in the review. Another example is our algorithm for sampling DAGs conditionally on a root-partition (Sections 3.4 Accordingly, our main innovations are algorithmic. We would like to correct that our algorithm for sampling DAGs is not "classical" (cf.
214cfbe603b7f9f9bc005d5f53f7a1d3-Supplemental.pdf
We focus on the case of i.i.d.
Residual Flows for Invertible Generative Modeling
Tian Qi Chen, Jens Behrmann, David K. Duvenaud, Joern-Henrik Jacobsen
We give a tractable unbiased estimate of the log density using a "Russian roulette" estimator, and reduce the memory required
From Boltzmann Machines to Neural Networks and Back Again
Graphical models are powerful tools for modeling high-dimensional data, but learning graphical models in the presence of latent variables is well-known to be difficult. In this work we give new results for learning Restricted Boltzmann Machines, probably the most well-studied class of latent variable models.
Large Scale Markov Decision Processes with Changing Rewards
The algorithm's computational complexity is polynomial in
Response to the paper " Large Scale Markov Decision Processes with Changing Rewards "
We will include a more detailed discussion to motivate our methodology.