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Title: On importance-weighted autoencoders

Paper content:
The importance weighted autoencoder (IWAE) (Burda et al., 2016) is a popular variational-inference method which achieves a tighter evidence bound (and hence a lower bias) than standard variational autoencoders by optimising a multi-sample objective, i.e. an objective that is expressible as an integral over $K > 1$ Monte Carlo samples.
Unfortunately, IWAE crucially relies on the availability of reparametrisations and even if these exist, the multi-sample objective leads to inference-network gradients which break down as $K$ is increased (Rainforth et al., 2018).
This breakdown can only be circumvented by removing high-variance score-function terms, either by heuristically ignoring them (which yields the 'sticking-the-landing' IWAE (IWAE-STL) gradient from Roeder et al. (2017)) or through an identity from Tucker et al. (2019) (which yields the 'doubly-reparametrised' IWAE (IWAE-DREG) gradient).
In this work, we argue that directly optimising the proposal distribution in importance sampling as in the reweighted wake-sleep (RWS) algorithm from Bornschein & Bengio (2015) is preferable to optimising IWAE-type multi-sample objectives.
To formalise this argument, we introduce an adaptive-importance sampling framework termed adaptive importance sampling for learning (AISLE) which slightly generalises the RWS algorithm.
We then show that AISLE admits IWAE-STL and IWAE-DREG (i.e. the IWAE-gradients which avoid breakdown) as special cases.
Let x be some observation and let z be some latent variable taking values in some space Z. These are modeled via the generative model p θ (z, x) = p θ (z)p θ (x|z) which gives rise to the marginal likelihood p θ (x) = Z p θ (z, x) dz of the model parameters θ.
In this work, we analyse algorithms for variational inference, i.e. algorithms which aim to
1. learn the generative model, i.e. find a value θ which is approximately equal to the maximum-likelihood estimate (MLE) θ ml := arg max θ p θ (x);
2. construct a tractable variational approximation q φ,x (z) of p θ (z|x) = p θ (z, x)/p θ (x), i.e. find the value φ such that q φ ,x (z) is as close as possible to p θ (z|x) in some suitable sense.
A few comments about this setting are in order.
Firstly, as is common in the literature, we restrict our presentation to a single latent representation-observation pair (z, x) to avoid notational clutter -the extension to multiple independent observations is straightforward.
Secondly, we assume that no parameters are shared between the generative model p θ (z, x) and the variational approximation q φ,x (z).
This is common in neural-network applications but could be relaxed.
Thirdly, our setting is general enough to cover amortised inference.
For this reason, we often refer to φ as the parameters of an inference network.
Two main classes of stochastic gradient-ascent algorithms for optimising ψ := (θ, φ) which employ K ≥ 1 Monte Carlo samples ('particles') to reduce errors have been proposed.
(Roeder et al., 2017) heuristically drops the problematic score-function terms from the IWAE φ-gradient.
This induces bias for the IWAE objective.
-IWAE-DREG.
The 'doubly-reparametrised' IWAE (IWAE-DREG) φ-gradient (Tucker et al., 2019) unbiasedly removes the problematic score-function terms from the IWAE φ-gradient using a formal identity.
We have shown that the adaptive-importance sampling paradigm of the reweighted wake-sleep (RWS) (Bornschein & Bengio, 2015) is preferable to the multi-sample objective paradigm of importance weighted autoencoders (IWAEs) (Burda et al., 2016) because the former achieves all the goals of the latter whilst avoiding its drawbacks.
A On the rôle of the self-normalisation bias within RWS/AISLE

assistant: text: We show that most variants of importance-weighted autoencoders can be derived in a more principled manner as special cases of adaptive importance-sampling approaches like the reweighted-wake sleep algorithm.