However, one major contribution is that Table 1 can be extended with more columns (e.g. one more

In the first experiment, we compare the performance of the aforementioned four alternatives. As we generated instances to satisfy the full-rank condition, i.e., Assumption E.1, or if random perturbations are applied to the underlying LMAB model [8]). (recall Figure 3). Multi-Armed Bandits problem that has been extensively studied in literature ( e.g., see [ When the time-horizon is sufficiently long but finite e.g., if Regime switching bandits LMAB may be also seen as a special type of adversarial or non-stationary bandits ( e.g., [ The standard objective in non-stationary bandits is to find the best stationary policy in hindsight with unlimited possible contexts. We focus on significantly more general cases where there is no obvious way of clustering observations, e.g., when Note that this could still be in H A regime with large number of actions A .

Can we use spiking neural networks (SNN) as generative models of multi-neuronal recordings, while taking into account that most neurons are unobserved?

A DAG is a directed graph with no directed paths starting and ending at the same node.

To overcome these issues, we present Truncated Sequential Neural Posterior Estimation (TSNPE).

Bayesian inference in the space of functions has been an important topic for Bayesian modeling in the past. In this paper, we propose a new solution to this problem called Functional V ariational Inference (FVI). In FVI, we minimize a divergence in function space between the variational distribution and the posterior process.