We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets.

In this paper, we introduce the pseudo-extended MCMC method as a simple approach for improving the mixing of the MCMC sampler for multi-modal posterior distributions.

All of the reviewers' minor comments and corrections have been added to Below, we address the reviewers' main questions. The paper focuses on HMC sampling. Unfortunately, HMC can't be applied in the discrete setting due to discontinuous How do you recommend setting π and g to best estimate β? Therefore, it's quite straightforward to implement pseudo-extended HMC within Stan by As a minor comment in line 58, it would be good to state that delta is an arbitrary differentiable function. This is a good point and we've corrected this in the paper. The experiments in 4.1 and 4.2 use the RMSE error of the target variables which is quite unusual.

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However, it is not clear how such representations might be learned and computed in partially observed, noisy environments. Here, we introduce a neurally plausible model using distributional successor features, which builds on the distributed distributional code for the representation and computation of uncertainty, and which allows for efficient value function computation in partially observed environments via the successor representation.

The exploration bonus is an effective approach to manage the exploration-exploitation trade-off in Markov Decision Processes (MDPs).

We propose to learn deep undirected graphical models (i.e., MRFs) with a non-ELBO objective for which we can calculate exact gradients.

Therefore, in this paper, we study the occurring challenges for co-generation with GANs. To address those challenges we develop an annealed importance sampling based Hamiltonian Monte Carlo co-generation algorithm.

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