Decision Process (MDP) setting where the reward function is not given, but demonstrations from experts are available. Although the goal of imitation learning is to learn a policy that produces behaviors nearly as good as the experts' for a desired task, assumptions of consistent optimality for demonstrated behaviors are often violated in practice.

The multiple-try Metropolis (MTM) algorithm is an extension of the Metropolis-Hastings (MH) algorithm by selecting the proposed state among multiple trials according to some weight function. Although MTM has gained great popularity owing to its faster empirical convergence and mixing than the standard MH algorithm, its theoretical mixing property is rarely studied in the literature due to its complex proposal scheme.

The trend in normalizing flow literature has been to devise deeper, more complex transformations to achieve greater flexibility.

We want to thank the reviewers greatly for the time and effort put into these reviews. R4), and that the "conceptual difference to previous research is a big strength." "wider" approach to building normalizing flow-based models is more than just a way to improve performance, noting Our work uncovers challenges that are unique to boosting on normalizing flows. Only analytically invertible flows can be boosted for variational inference (Section 5.1, and Figure 2) In regards to R1 and R3's critique on further differentiating our work with boosted density estimation [Rosset-Segal, '02] and generative models [Grover-Ermon, '18]: We show that the change-of-variables formula can be recursively We felt that proofs of boosting's expressiveness to be outside R2 writes "there are two bottlenecks in NF expressivity--the base distribution We appreciate reviewers taking the time to check for correctness. We stand by Eq. (10), (c 1) (c 1)

In this paper, we tackle the Bayesian estimation of point process intensity as a function of covariates. We propose a novel augmentation of permanental process called augmented permanental process, a doubly-stochastic point process that uses a Gaussian process on covariate space to describe the Bayesian a pri-ori uncertainty present in the square root of intensity, and derive a fast Bayesian estimation algorithm that scales linearly with data size without relying on either domain discretization or Markov Chain Monte Carlo computation. The proposed algorithm is based on a non-trivial finding that the representer theorem, one of the most desirable mathematical property for machine learning problems, holds for the augmented permanental process, which provides us with many significant computational advantages. We evaluate our algorithm on synthetic and real-world data, and show that it outperforms state-of-the-art methods in terms of predictive accuracy while being substantially faster than a conventional Bayesian method.

By studying the generative sampling path, here we show that current solvers throw the sample path off the data manifold, and hence the error accumulates. To address this, we propose an additional correction term inspired by the manifold constraint, which can be used synergistically with the previous solvers to make the iterations close to the manifold. The proposed manifold constraint is straightforward to implement within a few lines of code, yet boosts the performance by a surprisingly large margin.