Paper:
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-Divergences
We present the first robust Bayesian Online Changepoint Detection algorithm through General Bayesian Inference (GBI) with β-divergences. The resulting inference procedure is doubly robust for both the parameter and the changepoint (CP) posterior, with linear time and constant space complexity. We provide a construction for exponential models and demonstrate it on the Bayesian Linear Regression model. In so doing, we make two additional contributions: Firstly, we make GBI scalable using Structural Variational approximations that are exact as β → 0. Secondly, we give a principled way of choosing the divergence parameter β by minimizing expected predictive loss on-line. Reducing False Discovery Rates of CPS from over 90% to 0% on real world data, this offers the state of the art.
increases as they move away from the posterior mean, mimicking the KLD. However, once they move far enough, their influence decreases again. This can be interpreted to mean that they are (increasingly) treated as outliers. As β increases, observations are registered as outliers closer to the posterior mean. Conversely, as β → 0, one recovers the KLD which cannot treat any observation as an outlier. In addressing misspecification and outliers this way, our approach builds on the principles of General Bayesian Inference (GBI) [see 6,21] and robust divergences [e.g. 4,15]. This paper presents three contributions in separate domains that are also illustrated in Figs. 1 and 3:
(1) Robust BOCPD: We construct the very first robust BOCPD inference. The procedure is applicable to a wide class of (multivariate) models and is demonstrated on Bayesian Linear Regression (BLR). Unlike standard BOCPD, it discerns outliers and CPS, see Fig. 1 B.
(2) Scalable GBI: Due to intractable posteriors, GBI has received little attention in machine learning so far. We remedy this with a Structural Variational approximation which preserves parameter dependence and is exact as β → 0, providing a near-perfect fit, see Fig. 3.
(3) Choosing β: While Fig. 1 A shows that β regulates the degree of robustness [see also 21,15], it is unclear how to set its magnitude. For the first time, we provide a principled way of initializing β. Further, we show how to refine it on-line by minimizing predictive losses.
The remainder of the paper is structured as follows: In Section 2, we summarize standard BOCPD and show how to extend it to robust inference using the β-D. We quantify the degree of robustness and show that inference under the β-D can be designed so that a single outlier never results in false declaration of a CP, which is impossible under the KLD. Section 3 motivates efficient Structural Variational Inference (SVI) with the β-D posterior. Within BOCPD, we propose to scale SVI using variance-reduced Stochastic Gradient Descent. Next, Section 4 expands on how β can be initialized before the algorithm is run and then optimized on-line during execution time. Lastly, Section 5 showcases the substantial gains in performance of robust BOCPD when compared to its standard version on real world data in terms of both predictive error and CP detection.

Review:
This paper builds on recent work in the area of “General Bayesian Inference” (GBI). GBI has been proposed to deal with the “M-open world”, where the class of models we choose does not capture the actual sampling distribution. A remarkable fact is that in this setting standard Bayesian updating can be seen as a method which learns a model by minimising the predictive KL-divergence from the model from which the data were sampled. The problem with the KL-divergence in the case where the model is mis-specified the penalties are much worse in the tails, meaning that the posteriors that are derived can be unsatisfactory for e.g. decision making. Hence, the use of “robust” divergence measures are proposed, which focus instead on the areas of greatest mass. $\beta$-divergences have been proposed to do exactly this. The $\beta$-divergence can be derived from the more general $\alpha,\beta$-divergence by setting $\alpha$ to 1, or from the Bregman divergence family. There are two nice properties of the $\beta$-divergence: posterior inference does not require estimating the data generating density; and it results in robust inference, since observations with low predicted probability under the model are automatically down weighted. This is not for free, and is at the cost of statistical efficiency.   The paper’s first contribution is to take GBI, and apply it to sequential Bayesian inference, where the prior at time t is the posterior from t - 1, to produce a general online inference method that is naturally robust to model misspecification. I feel that some of this could be made clearer. For example, M-open worlds are cited but not explained, and to the general reader it is probably not at all obvious why standard Bayesian inference corresponds to the KL between the fitted model and the data generating mechanism. I also think that the point that using $\beta$-divergence gives your robustness *irrespective of the model* is quite subtle. It is mentioned in the paper (e.g. at the end of 2.1) but I think this point could get missed. The technical contribution here is then to take a specific model: the Product Partition Model (aka Bayesian Changepoint Detection), and derive the inference procedure for the $\beta$-divergence. This essentially boils down to a regression model that is computed over the period since the last changepoint (the run length), with a corresponding model for the probability of the run continuing from one time step to the next. Note that the (extremely long and detailed) derivations are given in the appendix.  The second contribution is to improve the efficiency of GBI in this model using stochastic variance reduced gradient descent (SVRG). This is actually general to GBI, rather than specific to BOCPD, although it’s also a fairly trivial modification from SVRG with KL. There is an extra nuance where the gradient estimates are anchored every few steps, which presumably comes from an insight (failure?) whilst running vanilla SVRG. It would be interesting to know how much is lost (if anything) versus performing full optimisation of the variational parameters (as we know, sometimes SGD provides an additional regularisation effect).   The final technical contribution is of optimisation of $\beta$, including doing so in an online fashion. There are a lot of moving parts in this, even for the static version. It would be interesting in the examples to see how $\beta$ ends up getting set, and how it evolves through the time course of the experiments. Without this is somewhat difficult to estimate the utility of this method of selection, and whether the online updating is necessary or not.  The experiments themselves are two classical examples in CPD. Unsurprisingly the proposed method is more robust to outliers, so mission accomplished in this regard. However, I feel there are multiple ways that these could be improved. Firstly, both of these examples are 1-dimensional; whilst the methods are clearly applicable to higher dimensions, and it would be really interesting to see how they behave. Also, as described in the appendix, there are quite a few hyper parameters and preprocessing steps, and little intuition as to how these are reached. I fear that a lot of experimentation was performed to get to this stage, and that application to a new domain would remain a difficult task.  Specific comments:  Figure 1a is a bit unintuitive. It’s clear that the KL is focusing more on the tails of the distribution. However, the influence has a strange profile for the $\beta$-divergences - going up as you move away before falling away again. It’s hard to say what the practitioner should take from this: one might think to use as small a $\beta$ as possible. However when $\beta$->0 it reduces to the KL-Divergence (again, not obvious at all from the plot). I realise that section 4 discusses optimisation of $\beta$, but I think there is some intuition missing here In the definition of the product partition model, there is a $\Sigma_0$ which has no definition (it is multiplied by $\sigma^2$, which has a conjugate prior) In the algorithm, what is B vs b? What is Geom? Is the $\mathcal{I}$ sampled with or without replacement? In the for loop, it looks like the indexer i isn’t used, and clashes with the $i \in \mathcal{I}$ [22] looks like it has been published under the title "Principles of Bayesian Inference Using General Divergence Criteria" references missing in appendices (latex build)  Note: I didn’t check all of the appendix, but the parts that I did check were all correct.  Note: I have reviewed author's feedback, and glad to see that it addresses the concerns raised both by myself also by R2. I had a misconception of the data being used in the 2nd example. Accordingly I have raised my score from a 7 to an 8.