Variational Learning on Aggregate Outputswith Gaussian Processes

Variational Learning on Aggregate Outputs
with Gaussian Processes

Ho Chung Leon Law
University of Oxford
&Dino Sejdinovic11footnotemark: 1
University of Oxford
&Ewan Cameron
University of Oxford
&Tim CD Lucas22footnotemark: 2
University of Oxford
&Seth Flaxman
Imperial College London
&Katherine Battle22footnotemark: 2
University Of Oxford
&Kenji Fukumizu
Institute of Statistical Mathematics
Department of Statistics, Oxford, UK. <,>Big Data Institute, Oxford, UK. <,,>Department of Mathematics and Data Science Institute, London, UK. <>Tachikawa, Japan. <>

While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of disease, only have access to outputs at a much coarser level than that of the inputs. Aggregation of outputs makes generalization to new inputs much more difficult. We consider an approach to this problem based on variational learning with a model of output aggregation and Gaussian processes, where aggregation leads to intractability of the standard evidence lower bounds. We propose new bounds and tractable approximations, leading to improved prediction accuracy and scalability to large datasets, while explicitly taking uncertainty into account. We develop a framework which extends to several types of likelihoods, including the Poisson model for aggregated count data. We apply our framework to a challenging and important problem, the fine-scale spatial modelling of malaria incidence, with over million observations.


Variational Learning on Aggregate Outputs
with Gaussian Processes

  Ho Chung Leon Lawthanks: Department of Statistics, Oxford, UK. <,> University of Oxford Dino Sejdinovic11footnotemark: 1 University of Oxford Ewan Cameronthanks: Big Data Institute, Oxford, UK. <,,> University of Oxford Tim CD Lucas22footnotemark: 2 University of Oxford Seth Flaxmanthanks: Department of Mathematics and Data Science Institute, London, UK. <> Imperial College London Katherine Battle22footnotemark: 2 University Of Oxford Kenji Fukumizuthanks: Tachikawa, Japan. <> Institute of Statistical Mathematics


noticebox[b]Preprint. \end@float

1 Introduction

A typical supervised learning setup assumes existence of a set of input-output examples from which a functional relationship or a conditional probabilistic model of outputs given inputs can be learned. A prototypical use-case is the situation where obtaining outputs for new, previously unseen, inputs is costly, i.e., labelling is expensive and requires human intervention, but measurements of inputs are cheap and automated. Similarly, in many applications, due to a much greater cost in acquiring labels, they are only available at a much coarser resolution than the level at which the inputs are available and at which we wish to make predictions. This is the problem of weakly supervised learning on aggregate outputs [14, 20], which has been studied in the literature in a variety of forms, with classification and regression notably being developed separately and without any unified treatment which can allow more flexible observation models. In this contribution, we consider a framework of observation models of aggregated outputs given bagged inputs, which reside in exponential families. While we develop a more general treatment, the main focus in the paper is on the Poisson likelihood for count data, which is motivated by the applications in spatial statistics. In particular, we consider the important problem of fine-scale mapping of diseases. High resolution maps of infectious disease risk can offer a powerful tool for developing National Strategic Plans, allowing accurate stratification of intervention types to areas of greatest impact [5]. In low resource settings these maps must be constructed through probabilistic models linking the limited observational data to a suite of spatial covariates (often from remote-sensing images) describing social, economic, and environmental factors thought to influence exposure to the relevant infectious pathways. In this paper, we apply our method to the incidence of clinical malaria cases. Point incidence data of malaria is typically available at a high temporal frequency (weekly or monthly), but lacks spatial precision, being aggregated by administrative district or by health facility catchment. The challenge for risk modelling is to produce fine-scale predictions from these coarse incidence data, leveraging the remote-sensing covariates and appropriate regularity conditions to ensure a well-behaved problem.

Methodologically, the Poisson distribution is a popular choice for modelling count data. In the mapping setting, the intensity of the Poisson distribution is modelled as a function of spatial and other covariates. We use Gaussian processes (GPs) as a flexible model for the intensity. GPs are a widely used approach in spatial modelling but also one of the pillars of Bayesian machine learning, enabling predictive models which explicitly quantify their uncertainty. Recently, we have seen many advances in variational GP posterior approximations, allowing them to couple with more complex observation likelihoods (e.g. binary or Poisson data [21, 17]) as well as a number of effective scalable GP approaches [24, 30, 8, 9], extending the applicability of GPs to dataset sizes previously deemed prohibitive.

Contribution Our contributions can be summarised as follows. A general framework is developed for aggregated observation models using exponential families and Gaussian processes. This is novel, as previous work on aggregation or bag models focuses on specific types of output models such as binary classification. Tractable and scalable variational inference methods are proposed for several instances of the aggregated observation models, making use of novel lower bounds on the model evidence. In experiments, it is demonstrated that the proposed methods can scale to dataset sizes of more than million observations. We thoroughly investigate an application of the developed methodology to disease mapping from coarse measurements, where the observation model is Poisson, giving encouraging results. Uncertainty quantification, which is explicit in our models, is essential for this application.

Related Work The framework of learning from aggregate data was believed to have been first introduced in [20], which considers the two regimes of classification and regression. However, while the task of classification of individuals from aggregate data (also known as learning from label proportions) has been explored widely in the literature [23, 22, 13, 18, 35, 34, 14], there has been little literature on the analogous regression regime in the machine learning community. Perhaps the closest literature available is [13], who considers a general framework for learning from aggregate data, but also only considers the classification case for experiments. In this work, we will appropriately adjust the framework in [13] and take this to be our baseline. A related problem arises in the spatial statistics community under the name of ‘down-scaling’, ‘fine-scale modelling’ or ‘spatial disaggregation’ [11, 10], in the analysis of disease mapping, agricultural data, and species distribution modelling, with a variety of proposed methodologies (cf. [33] and references therein), including kriging [6]. However, to the best of our knowledge, approaches making use of recent advances in scalable variational inference for GPs are not considered.

Another closely related topic is multiple instance learning (MIL), concerned with classification with max-aggregation over labels in a bag, i.e. a bag is positively labeled if at least one individual is positive, and it is otherwise negatively labelled. While the task in MIL is typically to predict labels of new unobserved bags, [7] demonstrates that individual labels of a GP classifier can also be inferred in MIL setting with variational inference. Our work parallels that approach, considering bag observation models in exponential families and deriving new approximation bounds for some common generalized linear models. In deriving these bounds, we have taken an approach similar to [17], who considers the problem of Gaussian process-modulated Poisson process estimation using variational inference. However, our problem is made more complicated by the aggregation of labels. Other related research topics include distribution regression and set regression, as in [28, 15, 16] and [36]. In these regression problems, while the input data for learning is the same as the current setup, the goal is to learn a function at the bag level, rather than the individual level, the application of these methods in our setting, naively treating single individuals as “distributions”, may lead to suboptimal performance. An overview of some other approaches for classification using bags of instances is given in [4].

2 Bag observation model: aggregation in mean parameters

Suppose we have a statistical model for output , with parameter given by a function of input , i.e., . Although one can formulate in an arbitrary fashion, practitioners often only focus on interpretable simple models, hence we restrict our attention to arising from exponential families. We assume that is the mean parameter of the exponential family.

Assume that we have a fixed set of points such that is a bag of points with individuals, and we wish to estimate the regression value for each individual. However, instead of the typical setup where we have a paired sample of individuals and their outputs to use as a training set, we observe only aggregate outputs for each of the bags. Hence, our training data is of the form


and the goal is to estimate parameters corresponding to individuals. To relate the aggregate and the bag , we use the following bag observation model:


where is an optional fixed non-negative weight used to adjust the scales (see Section 3 for an example). Note that the aggregation in the bag observation model is on the mean parameters for individuals, not necessarily on the individual responses . This implies that each individual contributes to the mean bag response and that the observation model for bags belongs to the same parametric form as the one for individuals. For tractable and scalable estimation, we will use variational methods, as the aggregated observation model leads to intractable posteriors. We consider the Poisson, normal, and exponential distributions, but devote a special focus to the Poisson model in this paper, and refer readers to Appendix A for other cases and experimental results for the Normal model in Appendix H.2.

It is also worth noting that we place no restrictions on the collection of the individuals, with the bagging process possibly dependent on covariates or any unseen factors. The bags can also be of different sizes, with potentially the same individuals appearing in multiple bags. After we obtain our individual model , we can use it for prediction of in-bag individuals, as well as out-of-bag individuals.

3 Poisson bag model: Modelling aggregate counts

The Poisson distribution is considered for count observations, and this paper discusses the Poisson regression with intensity multiplied by a ‘population’ , which is a constant assumed to be known for each individual (or ‘sub-bag’) in the bag. The population for a bag is given by . An observed bag count is assumed to follow

Note that, by introducing unobserved counts , the bag observation has the same distribution as since the Poisson distribution is closed under convolutions. If a bag and its individuals correspond to an area and its partition in geostatistical applications, as in the malaria example in Section 4.2, the population in the above bag model can be regarded as the population of an area or a sub-area. With this formulation, the goal is to estimate the basic intensity function from the aggregated observations (1). Assuming independence given , the negative log-likelihood (NLL) across bags is


where denotes an equality up to additive constant. During training, this term will pass information from the bag level observations to the individual basic intensity . It is noted that once we have trained an appropriate model for , we will be able to make individual level predictions, and also bag level predictions if desired. We will consider baselines with (3) using penalized likelihoods inspired by manifold regularization in semi-supervised learning [2] – presented in Appendix B. In the next section, we propose a model for based on GPs.

3.1 VBAgg-Poisson: Gaussian processes for aggregate counts

Suppose now we model as a Gaussian process (GP), then we have:


where and are some appropriate mean function and covariance kernel . (For implementation, we consider a constant mean function.) Since the intensity is always non-negative, in all models, we will need to use a transformation , where is a non-negative valued function. We will consider cases and . A discussion of various choices of this link function in the context of Poisson intensities modulated by GPs is given in [17]. Modelling with a GP allows us to propagate uncertainty on the predictions to , which is especially important in this weakly supervised problem setting, where we do not directly observe any individual output . Since the total number of individuals in our target application of disease mapping is typically in the millions (see Section 4.2), we will approximate the posterior over using variational inference, with details found in Appendix E.

For scalability of the GP method, as in previous literature [7, 17], we use a set of inducing points , which are given by the function evaluations of the Gaussian process at landmark points ; i.e., . The distribution is thus given by


The joint likelihood is given by:


where , with , denoting their respective evaluations of the mean function and being parameters of the mean and kernel functions of the GP. Proceeding similarly to [17], which discusses (non-bag) Poisson regression with GP, we obtain a lower bound of the marginal log-likelihood :


where is a variational distribution to be optimized. The general solution to the maximization over of the evidence lower bound above is given by the posterior of the inducing points , which is intractable. We introduce a restriction to the class of to approximate the posterior . Suppose that the variational distribution is Gaussian, . We then need to maximize the lower bound over the variational parameters and .

The resulting gives an approximation to the posterior which also leads to a Gaussian approximation to the posterior , which we finally then transform through to obtain the desired approximate posterior on each (which is either log-normal or non-central depending on the form of ). The approximate posterior on will then allow us to make predictions for individuals while, crucially, taking into account the uncertainties in (note that even the posterior predictive mean of will depend on the predictive variance in due to the nonlinearity ). We also want to emphasis the use of inducing variables is essential for scalability in our model: we cannot directly obtain approximations to the posterior of for all individuals, since this is often large in our problem setting (Section 4.2).

As the and are both Gaussian, the last term (KL-divergence) of (8) can be computed explicitly with exact form found in Appendix E.3. To consider the first two terms, let be the marginal normal distribution of , where follows the variational posterior . The distribution of is then , using (7) :


In the first term of (8), each summand is of the form


in which the second term is tractable for both of and . The integral of the first term, however with Gaussian is not tractable. To solve this, we take different approaches for and ; for the former, approximation by Taylor expansion is applied, while for the latter, further lower bound is taken.

First consider the case , and rewrite the first term of (8) as:

with and . By a Taylor series approximation for (similar to [29]) around , we obtain


with details are in Appendix E.4. An alternative approach which we use for the case is to take a further lower bound, which is applicable to a general class of (we provide further details for the analogous approach for in Appendix E.2). We use the following Lemma (proof found in Appendix E.1):

Lemma 1.

Let be a random vector with probability density with marginal densities , and let , . Then, for any non-negative valued function ,

Hence we obtain that


Using the above two approximation schemes, our objective (up to constant terms) can be formulated as: 1)




Given these objectives, we can now optimise these lower bounds with respect to variational parameters , parameters of the mean and kernel functions, using stochastic gradient descent (SGD) on bags. Additionally, we might also learn , locations for the landmark points. In this form, we can also see that the bound for has the added computational advantage of not requiring the full computation of the matrix , but only its diagonals, while for computation of involves full , which may be problematic for extremely large bag sizes.

4 Experiments

We will now demonstrate various approaches: Variational Bayes with Gaussian Process (VBAgg), a MAP estimator of Bayesian Poisson regression with explicit feature maps (Nyström) and a neural network (NN) – the latter two employing manifold regularisation with RBF kernel (unless stated otherwise). For additional baselines, we consider a constant within bag model (constant), i.e. and also consider creating ‘individual’ covariates by aggregation of the covariates within a bag (bag-pixel). For details of all these approaches, see Appendix B. We also denote and as Exp and Sq respectively.

We implement our models in TensorFlow111Code will be available for use. and use SGD with Adam [12] to optimise their respective objectives, and we split the dataset into parts, namely train, early-stop, validation and test set. Here the early-stop set is used for early stopping for the Nyström, NN and bag-pixel models, while the VBAgg approach ignores this partition as it optimises the lower bound to the marginal likelihood. The validation set is used for parameter tuning of any regularisation scaling, as well as learning rate, layer size and multiple initialisations. Throughout, VBAgg and Nyström have access to the same set of landmarks for fair comparison. It is also important to highlight that we perform early stopping and tuning based on bag level performance on NLL only, as this is the only information available to us.

For the VBAgg model, there are two approaches to tuning, one approach is to choose parameters based on NLL on the validation bag sets, another approach is to select all parameters based on the training objective , the lower bound to the marginal likelihood. We denote the latter approach VBAgg-Obj and report its toy experimental results in Appendix H.1.1 for presentation purposes. In general, the results are relatively insensitive to this choice, especially when . To make predictions, we use the mean of our approximated posterior (provided by a log-normal and non-central distribution for Exp and Sq). As an additional evaluation, we report mean square error (MSE) and bag performance results in Appendix H.

Figure 1: Left: Random samples on the Swiss roll manifold. Middle, Right: Individual Average NLL on train set for varying number of training bags and increasing , over repetitions. Constant prediction within bag gives a NLL of . bag-pixel model gives NLL above for the varying number of bags experiment.

4.1 Poisson Model: Swiss Roll

We first demonstrate our method on the swiss roll dataset222The swiss roll manifold function (for sampling) can be found on the Python scikit-learn package., illustrated in Figure 1 (left). To make this an aggregate learning problem, we first construct bags with sizes drawn from a negative binomial distribution , where and represents the respective mean and standard deviation of . We then randomly select points from the swiss roll manifold to be the locations, giving us a set of colored locations in . Ordering these random locations by their -axis coordinate, we group them, filling up each bag in turn as we move along the -axis. The aim of this is to simulate that in real life the partitioning of locations into bags is often not independent of covariates. Taking the colour of each location as the underlying rate at that location, we simulate , and take our observed outputs to be , where . Our goal is then to predict the underlying individual rate parameter , given only bag-level observations . To make this problem even more challenging, we embed the data manifold into by rotating it with a random orthogonal matrix. For the choice of for VBAgg and Nyström, we use the RBF kernel, with the bandwidth parameter learnt. For landmark locations, we use the K-means++ algorithm, so that landmark points lie evenly across the data manifold.

Varying number of Bags:

To see the effect of increasing number of bags available for training, we fix and , and vary the number of bags for the training set from to with the same number of bags for early stopping and validation. Each experiment is repeated for runs, and results are shown in Figure 1 for individual NLL on the train set. Again we emphasise that the individual labels are not used in training. We see that all versions of VBAgg outperform all other models, in terms of MSE and NLL, with statistical significance confirmed by a signed rank permutation test (see Appendix H.1.1). We also observe that the bag-pixel model has poor performance, as a result of losing individual level covariate information in training by simply aggregating them.

Varying number of individuals per bag:  To study the effect of increasing bag sizes (with larger bag sizes, we expect "disaggregation" to be more difficult), we fix the number of training bags to be with early stopping and validation set to be bags, while varying the number of individuals per bag through and in the negative binomial distribution. To keep the relative scales between and the same, we take . The results are shown in Figure 1, focusing on the best performing methods in the previous experiment. Here, we observe that VBAgg models again perform better than the Nyström and NN models with statistical significance as reported in Appendix H.1.1, with performance stable as increases.

Discussion  To gain more insight into the VBAgg model, we look at the calibration of our two different Bayesian models: VBAgg-Exp and VBAgg-Square. We compute their respective posterior quantiles and observe the ratio of times the true lie in these quantiles. We present these in Appendix H.1.1. The calibration plots reveal an interesting nature about using the two different approximations for using versus for . While experiments showed that the two model perform similarly in terms of NLL, the calibration of the models is very different. While the VBAgg-Square is well calibrated in general, the VBAgg-Exp suffers from poor calibration. This is not surprising, as VBAgg-Exp uses an additional lower bound on model evidence. Thus, uncertainty estimates given by VBAgg-Exp should be treated with care.

4.2 Malaria Incidence Prediction

We now demonstrate the proposed methodology on an important real life malaria prediction problem for an endemic country from the Malaria Atlas Project database333Due to confidentiality reasons, we do not report country or plot the full map of our results.. In this problem, we would like to predict the underlying malaria incidence rate in each km by km region (referred to as a pixel), while having only observed aggregated incidences of malaria at much larger regional levels, which are treated as bags of pixels. These bags are non-overlapping administrative units, with pixels per bag ranging from 13 to 6,667, with a total of 1,044,683 pixels. In total, data is available for bags444We consider bags for train, bags each for validation and early-stop, with bags for testing, with different splits across different trials, selecting them to ensure distributions of labels are similar across sets.. Along with these pixels, we also have population estimates (per people) for pixel in bag , spatial coordinates given by , as well as covariates , collected by remote sensing. Some examples of covariates includes accessibility, distance to water, mean of land surface temperature and stable night lights. It is clear that rather than expecting malaria incidence rate to be constant throughout the entire bag (as in Figure 4.2), we expect pixel incidence rate to vary, depending on social, economic and environmental factors [32]. Our goal is therefore to build models that can predict malaria incidence rates at a pixel level.

We assume a Poisson model on each individual pixel, i.e. , where is the underlying pixel incidence rate of malaria per people that we are interested in predicting. We consider the VBAgg, Nyström and NN as prediction models and use a kernel given as a sum of an ARD (automatic relevance determination) kernel on covariates and a Matérn kernel on spatial locations for the VBAgg and Nyström methods, learning all kernel parameters (the kernel expression is provided in Appendix G). We use the same kernel for manifold regularisation in the NN model. This kernel choice incorporates spatial information, while allowing feature selection amongst other covariates. For choice of landmarks, we ensure landmarks are placed evenly throughout space by using one landmark point per training bag (selected by k-means++). This is so that the uncertainty estimates we obtain are not too sensitive to the choice of landmarks.


figureTriangle denotes approximate start and end of river location, crosses denotes non-train set bags. Malaria incidence rate is per people. Left, Middle: , with constant model (Left), and VBAgg-Obj-Sq (tuned on ) (Middle). Right: Standard deviation of the posterior in (9) with VBAgg-Obj-Sq.

In this problem, no individual-level labels are available, so we report Bag NLL and MSE (on observed incidences) on the test bags in Appendix G over different re-splits of the data. Although we can see that Nyström is the best performing method, the improvement over VBAgg models is not statistically significant. On the other hand, both VBAgg and Nyström models statistically significantly outperform NN, which also has some instability in its predictions, as discussed in Appendix G.1. However, a caution should be exercised when using the measure of performance at the bag level as a surrogate for the measure of performance at the individual level: in order to perform well at the bag level, one can simply utilise spatial coordinates and ignore other covariates, as malaria intensity appears to smoothly vary between the bags (Left of Figure 4.2). However, we do not expect this to be true at the individual level.

To further investigate this, we consider a particular region, and look at the predicted individual malaria incidence rate, with results found in Figure 4.2 and in Appendix G.1 across different data splits, where the behaviours of each of these models can be observed. While Nyström and VBAgg methods both provide good bag-level performance, Nyström and VBAgg-Exp can sometimes provide overly-smooth spatial patterns, which does not seem to be the case for the VBAgg-Sq method (recall that VBAgg-Sq performed best in both prediction and calibration for the toy experiments). In particular, VBAgg-Sq consistently predicts higher intensity along rivers (a known factor [31]; indicated by triangles in Figure 4.2) using only coarse aggregated intensities, demonstrating that prediction of (unobserved) pixel-level intensities is possible using fine-scale environmental covariates, especially ones known to be relevant such as covariates indicated by the Topographic Wetness Index, a measure of wetness, see Appendix G.2 for more details.

In summary, by optimising the lower bound to the marginal likelihood, the proposed variational methods are able to learn useful relations between the covariates and pixel level intensities, while avoiding the issue of overfitting to spatial coordinates. Furthermore, they also give uncertainty estimates (Figure 4.2, right), which are essential for problems like these, where validation of predictions is difficult, but they may guide policy and planning.

5 Conclusion

Motivated by the vitally important problem of malaria, which is the direct cause of around 187 million clinical cases [3] and 631,000 deaths [5] each year in sub-Saharan Africa, we have proposed a general framework of aggregated observation models using Gaussian processes, along with scalable variational methods for inference in those models, making them applicable to large datasets. The proposed method allows learning in situations where outputs of interest are available at a much coarser level than that of the inputs, while explicitly quantifying uncertainty of predictions. The recent uptake of digital health information systems offers a wealth of new data which is abstracted to the aggregate or regional levels to preserve patient anonymity. The volume of this data, as well as the availability of much more granular covariates provided by remote sensing and other geospatially tagged data sources, allows to probabilistically disaggregate outputs of interest for finer risk stratification, e.g. assisting public health agencies to plan the delivery of disease interventions. This task demands new high-performance machine learning methods and we see those that we have developed here as an important step in this direction.


We thank Kaspar Martens for useful discussions, and Dougal Sutherland for providing the code base in which this work was based on. HCLL is supported by the EPSRC and MRC through the OxWaSP CDT programme (EP/L016710/1). HCLL and KF are supported by JSPS KAKENHI 26280009. EC and KB are supported by OPP1152978, TL by OPP1132415 and the MAP database by OPP1106023. DS is supported in part by the ERC (FP7/617071) and by The Alan Turing Institute (EP/N510129/1). The data were provided by the Malaria Atlas Project supported by the Bill and Melinda Gates Foundation.


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Appendix A Aggregated Exponential Family Models

Consider an observation model of the form


where response is one-dimensional, is a natural parameter corresponding to the statistic , is a dispersion parameter, and is base measure. For simplicity, we will assume that natural parameters corresponding to the other parts of the sufficient statistic are fixed and folded into the base measure. Let be the corresponding mean parameter, i.e.

and be the link function mapping from mean to the natural parameters and its inverse. We wish to model the mean parameter using a Gaussian process on a domain together with a function which transforms the GP value to the natural parameter space, i.e.


For example, the mean parameter for some models is restricted to the positive part of the real line, while the GP values cover the whole real line. We will consider the following examples:

  • Normal (with fixed variance). and can be identity, too, as there are no restrictions on the mean parameter space.

  • Poisson. , . should take a positive value, so we consider or .

  • Exponential. and , , . should take a positive value, so we consider or

Note that the link function is concave for all the examples above.

a.1 Bag model

We will consider the aggregation in the mean parameter space. Namely, let be independent aggregate responses for each of the bags of covariates , . We assume the following aggregation model:


where are fixed weights to adjust the scales among the individuals and the bag (e.g., adjusting for population size).

We also can model individual (unobserved) variables (, which follow:


Note that we consider aggregation in mean parameters of responses, not in the responses themselves. If we consider a case where underlying individual responses aggregate to as a weighted sum, the form of the bag likelihood and individual likelihood would be different unless we restrict attention to distribution families which are closed under both scaling and convolution. However, when aggregation occurs in the mean parameter space, the form of the bag likelihood and individual likelihood is always the same. This corresponds to the following measurement process:

  • Each individual has a mean parameter - if it were possible to sample a response for that particular individual, we would obtain a sample

  • However, we cannot sample the individual and we can only observe a bag response. But in that case, only a single bag response is taken and depends on all individuals simultaneously. Each individual contributes in terms of an increase in a mean bag response, but this measurement process is different from the two-stage procedure by which we aggregate individual responses.

a.2 Marginal likelihood and ELBO

Let (bag observations). With the inducing points , the marginal likelihood is


The evidence lower bound can be derived as


where .

By setting the variational distribution as Gaussian, the third term is tractable. The first and second terms are however tractable only in limited cases. The cases we develop are the Poisson bag model, described in the main text, as well as the normal bag model and the exponential bag model, described below.

a.3 Normal bag model

is identity and , which makes both the first and the second terms tractable with the choice of . Moreover, the viewpoints of aggregating in the mean parameters and in the individual responses are equivalent for this model and we can also allow different variance parameters for different bags (and individuals).

Consider a bag of items . Each item is assumed to have a weight . At the individual level, we model the (unobserved) responses as


where , thus is a mean parameter per unit weight corresponding to the item and it is assumed to be a function of both . Similarly, is a variance parameter per unit weight. At the bag level, we consider the following model for the observed aggregate response , assuming conditional independence of individual responses given covariates :


where and are the mean and variance parameters per unit weight of the whole bag and is the total weight of bag . Although we can take to also be a function of the covariates, here for simplicity, we take to be constant per bag (note the abuse of notation). We can now compute the negative log-likelihood (NLL) across bags (assuming conditional independence given the ):


where is the function we are interested in, and are the variance parameters to be learnt.

We can now consider the lower bound to the marginal likelihood as below (assuming here to simplify notation, while the analogous expression with non-uniform weights is straightforward):


Using again a Gaussian distribution for , we have , which is a normal distribution and let be its marginal normal distribution of with mean and covariance given by and as before in (9).

Then all expectations with respect to are tractable and the ELBO is simply


a.4 Exponential bag model

In this case, we have . We can apply the similar argument as in Lemma 1. For any with , by the concavity of ,

For , the last line is equal to

When using a normal , this is tractable for several choices of including and . If we let , and maximize

under the constraint , we obtain

Finally, we have a lower bound



which is tractable for a Gaussian variational family. Also with an explicit form of , it is easy to take the derivatives of the resulting lower bound with respect to the variational parameters in .

Appendix B Alternative approaches


For the Poisson model, we can take , a constant rate across the bag, then:

hence the individual level predictive distribution is the form , and for unseen bag , , with predictive distribution given by .

bag-pixel: Bag as Individual

Another baseline is to train a model from the weighted average of the covariates, given by in the Poisson case, and in the normal case. The purpose of this baseline is to demonstrate that modelling at the individual level is important during training. Since we now have labels and covariates at the bag level, we can consider the following model:

with for the Poisson model. For the normal model, we have:

where and is a parameter to be learnt (assuming constant across bags). Now we observe that these models are identical to the individual model, except for a difference in indexing. Hence, after learning the function at the bag level, we can transfer the model to the individual level. Essentially here we have created fake individual level instances by aggregation of individual covariates inside a bag.

Nyström: Bayesian MAP for Poisson regression on explicit feature maps

Instead of the posterior based on the model (6), we can also consider an explicit feature map in order to directly construct a MAP estimator. While this method does not provide posterior uncertainty over , it does provide an interesting connection to the settings we have considered and also manifold-regularized neural networks, as discussed below. Let be the covariance function defined on covariates , and consider its low rank approximation with landmark points and . By using landmark points , we have avoided computation of the full kernel matrix, reducing computational complexity. Under this setup, we have that , with being the explicit (Nyström) feature map. Using this explicit feature map , we have the following model:

where is a prior parameter and is the corresponding row of . We can then consider a MAP estimator of the model coefficients :


This essentially recovers the same model as in (3) with the standard loss regularising the complexity of the function. This model can be thought of in several different ways, for example as a weight space view of the GP ([26] for an overview), or as a MAP of the Subset of Regressors (SoR) approximation [27] of the GP when . Additional we may include manifold regulariser as part of the prior, see discussion below about neural network.

NN: Manifold-regularized neural networks

The next approach we consider is a parametric model for as in [13], and search the best parameter to minimize negative log-likelihood across bags. This paper considers a neural network with parameters for the model , and uses the back-propagation to learn and hence individual level model . However, since we only have aggregated observations at the bag level, but lots of individual covariate information, it is useful to incorporate this information also, by enforcing smoothness on the data manifold given by the unlabelled data. To do this, following [13] and [22], we pursue a semisupervised view of the problem and include an additional manifold regularisation term [2] (rescaling with during implementation):