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

Graphical models are widely used in science to represent joint probability distributions with an underlying conditional dependence structure. The inverse problem of learning a discrete graphical model given i.i.d samples from its joint distribution can be solved with near-optimal sample complexity using a convex optimization method known as Generalized Regularized Interaction Screening Estimator (GRISE). But the computational cost of GRISE becomes prohibitive when the energy function of the true graphical model has higher order terms. We introduce NeurISE, a neural net based algorithm for graphical model learning, to tackle this limitation of GRISE. We use neural nets as function approximators in an Interaction Screening objective function.

Planning with a generative model is thinking before acting .

Solving OPE is often the starting point in many RL applications. To tackle the problem of OPE, the idea of importance sampling (IS) corrects the mismatch in the distributions under the behavior policy and target policy.

So far, BMDPs could only be solved in the case of finite state spaces with known dynamics. This work extends the state-of-the-art to continuous spaces environments and unknown dynamics. We show that the solution to a BMDP is a fixed point of a novel Budgeted Bellman Optimality operator. This observation allows us to introduce natural extensions of Deep Reinforcement Learning algorithms to address large-scale BMDPs.

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.