Modern applications of machine learning (ML) deal with increasingly heterogeneous datasets comprised of data collected from overlapping latent subpopulations.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper presents a rational model of economic choice, where values are replaced by inferences about relative rank in a context. The model is shown to provide a good fit to pricing data and risky-choice experiments.Overall, I found the paper interesting, but somewhat too narrow and inaccessible for a NIPS audience. There was lots of insufficiently explained economic jargon (e.g. Veblen and Giffen goods), which are key for understanding the contribution.

Discovery of causal relations from observational data is essential for many disciplines of science and real-world applications.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper introduces a GP-Vol model to flexibly capture the time-dependent changes in variance, and develops a new online algorithm for fully Bayesian inference under the model. The paper is clearly written, the developed inference method seems technically sound, and the presented results look promising. My opinion on the model itself, using a non-parametric approach such as using the GP prior on the transition function (as in the paper), seems, though, a bit an obvious way of extending the prior work developed in the finance area. So, I wouldn't put too high grade on the paper in terms of its originality.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper presents a new Bayesian nonparametric model for mixed-membership modeling of grouped data. The model is based on a Beta-Negative Binomial Process (BNBP). Whereas this stochastic process was exploited by previous work, as mentioned in the paper, there are several aspects that set this work apart. First, this work obtains an EPPF (exchangeable partition probability function), and derives the prediction rules thereon.

So, most of the learning happens under model misspeci-fication.

As shown in several recent papers [Liu et al., 2017, Liang, 2018, Singh et al., 2018, Uppal Namely, the estimators used in past work rely on the unrealistic assumption that the practitioner knows the Besov space in which the true density lies. The rest of this paper is organized as follows.