1 Introduction
Selecting a representative sample of data from a large pool of available candidates is an essential step of a large class of machine learning problems: noteworthy examples include automatic summarization, matrix approximation, and minibatch selection. Such problems require sampling schemes that calibrate the tradeoff between the pointwise
quality – e.g. the relevance of a sentence to a document summary – of selected elements and the setwise diversity^{1}^{1}1Here, we use diversity to mean useful coverage across dissimilar examples in a meaningful feature space, rather than other definitions diversity that may appear in ML fairness literature. of the sampled set as a whole.Determinantal Point Processes (Dpps) are probabilistic models over subsets of a ground set that elegantly model the tradeoff between these often competing notions of quality and diversity. Given a ground set of size , Dpps allow for sampling over all possible subsets of elements, assigning to any subset of a ground setof elements the probability
^{2}^{2}2We are adopting here the Ensemble construction (Borodin & Rains, 2005) of a Dpp.(1) 
where is the Dpp kernel and denotes the principal submatrix of indexed by items in . Intuitively, Dpps measure the volume spanned by the feature embedding of the items in feature space (Figure 1). First introduced by Macchi (1975) to model the distribution of possible states of fermions obeying the Pauli exclusion principle, the properties of Dpps have since then been studied in depth (Hough et al., 2006; Borodin, 2009, see e.g.). As Dpps capture repulsive forces between similar elements, they arise in many natural processes, such as the distribution of nonintersecting random walks (Johansson, 2004)
, spectra of random matrix ensembles
(Mehta & Gaudin, 1960; Ginibre, 1965), and zerocrossings of polynomials with Gaussian coefficients (Hough et al., 2009). More recently, Dpps have become a prominent tool in machine learning due to their elegance and tractability: recent applications include video recommendation (Chen et al., 2018), minibatch selection (Zhang et al., 2017), and kernel approximation (Li et al., 2016b; Mariet et al., 2018). However, the sampling cost makes the practical application of Dpps intractable for large datasets, requiring additional work such as subsampling from , structured kernels (Gartrell et al., 2017; Mariet & Sra, 2016b), or approximate sampling methods (Anari et al., 2016a; Li et al., 2016a; Affandi et al., 2013). Nonetheless, even such methods require significant preprocessing time, and scale poorly with the size of the dataset. Furthermore, when dealing with ground sets with variable components, preprocessing costs cannot be amortized, significantly impeding the application of Dpps in practice. These setbacks motivate us to investigate the use of more scalable models to generate highquality, diverse samples from datasets to obtain highlyscalable methods with flexibility to adapt to constantly changing datasets. Specifically, we use generative deep models to approximate the Dppdistribution over a ground set of items with both fixed and variable feature representations. We show that a simple, carefully constructed neural network,
DppNet, can generate Dpplike samples with very little overhead, while maintaining fundamental theoretical properties of Dpp measures. Furthermore, we show that DppNets can be trivially employed to sample from a conditional Dpp (i.e. sampling such that is predefined) and for greedy mode approximation.Contributions.

[leftmargin=*]

We introduce DppNet, a deep network trained to generate Dpplike samples based on static and variable ground sets of possible items.

We derive theoretical conditions under which the DppNets inherit the Dpp’s logsubmodularity.

We show empirically that DppNets provide an accurate approximation to Dpps and drastically speed up Dpp sampling.
2 Related work
Dpps belong to the class of Strongly Rayleigh (SR) measures; these measures benefit from the strongest characterization of negative association between similar items; as such, SR measures have benefited from significant interest in the mathematics community (Pemantle, 2000; Borcea et al., 2009; Borcea & Brändén, 2008; Marcus et al., 2015) and more recently in machine learning (Anari et al., 2016b; Li et al., 2016a; Mariet et al., 2018). This, combined with their tractability, makes Dpps a particularly attractive tool for subset selection in machine learning, and is one of the key motivations for our work. The application of Dpps to machine learning problems spans fields from document and video summarization (Gong et al., 2014; Chao et al., 2015), recommender systems (Zhou et al., 2010; Chen et al., 2018) and object retrieval (Affandi et al., 2014) to kernel approximation (Li et al., 2016b), neural network pruning (Mariet & Sra, 2016a), and minibatch selection (Zhang et al., 2017). Zou & Adams (2012) developed DPP priors for encouraging di
diversity in generative models and Snoek et al. (2013) showed that DPPs accurately model inhibition in neural spiking data. In the general case, sampling exactly from a Dpp requires an initial eigendecomposition of the kernel matrix , incurring a cost. In order to avoid this timeconsuming step, several approximate sampling methods have been derived; Affandi et al. (2013) approximate the Dpp kernel during sampling; more recently, results by Anari et al. (2016a) followed by Li et al. (2016a) showed that DPPs are amenable to efficient MCMCbased sampling methods.Exact methods that significantly speed up sampling by leveraging specific structure in the Dpp kernel have also been developed (Mariet & Sra, 2016b; Gartrell et al., 2017). Of particular interest is the dual sampling method introduced in Kulesza & Taskar (2012): if the DPP kernel can be composed as an inner product over a finite basis, i.e. there exists a feature matrix such that the Dpp kernel is given by , exact sampling can be done in .However, MCMC sampling requires variable amounts of sampling rounds, which is unfavorable for parallelization; dual Dpp sampling requires an explicit feature matrix . Motivated by recent work on modeling set functions with neural networks (Zaheer et al., 2017; Cotter et al., 2018), we propose instead to generate approximate samples via a generative network; this allows for simple parallelization while simultaneously benefiting from recent improvements in specialized architectures for neural network models (e.g. parallelized matrix multiplications). We furthermore show that, extending the abilities of dual Dpp sampling, neural networks may take as input variable feature matrices and sample from nonlinear kernels .
3 Generating Dpp samples with deep models
In this section, we build up a framework that allows the computational cost associated with Dpp sampling to be addressed via approximate sampling with a neural network. Given a positive semidefinite matrix , we take to represent the distribution modeled by a Dpp with kernel over the power set of . Given , we write and the submatrix of indexed by items in .
3.1 Motivating the model choice
Although the elegant quality/diversity tradeoff modeled by Dpps is a key reason for their recent success in many different applications, they benefit from other properties that make them particularly wellsuited to machine learning problems. We now focus on how these properties can be maintained with a deep generative model with the right architecture.
Closure under conditioning.
Dpps are closed under conditioning: given , the conditional distribution over given by (for ) is also Dpp with kernel (Borodin & Rains, 2005). Although conditioning comes at the cost of an expensive matrix inversion, this property make Dpps wellsuited to applications requiring diversity in conditioned sets, such as basket completion for recommender systems. Standard deep generative models such as (Variational) AutoEncoders (Kingma & Welling, 2014) (VAEs) and Generative Adversarial Networks (Goodfellow et al., 2014) (GANs) would not enable simple conditioning operations during sampling. Instead, we develop a model that given an input set , returns a prediction vector such that where : in other words, is the marginal probability of item being included in the final set, given that is a subset of the final set. Mathematically, we can compute as
(2) 
for (Kulesza & Taskar, 2012); for , we simply set . With this architecture, we sample a set via Algorithm 1, which allows for trivial basketcompletion type conditioning operations. Furthermore, Algorithm 1 can be modified to implement a greedy sampling algorithm without any additional cost.
Logsubmodularity.
As mentioned above, Dpps are included in the larger class of Strongly Rayleigh (SR) measures over subsets. Although being SR is a delicate property, which is maintained by only few operations (Borcea et al., 2009), logsubmodularity^{3}^{3}3Recall that a function over subsets is logsubmodular if and only if for any sets , we have . (which is implied by SRness) is more robust, as well as a fundamental property in discrete optimization (Zagoruyko & Komodakis, 2016; Buchbinder et al., 2012; Grötschel et al., 1981) and machine learning (Iyer & Bilmes, 2015; Wei et al., 2015). Crucially, we show in the following that (log)submodularity can be inherited by a generative model trained on a logsubmodular distribution:
Theorem 1.
Let be a strictly submodular function over subsets of , and be a function over the same space such that
(3) 
where indicates the total variational distance. Then is also submodular.
Corollary 1.1.
Let be a strictly logsubmodular Dpp over and DppNet be a network trained on the Dpp probabilities
, with a loss function of the form
where is a norm and (resp. ) is the probability vector assigned by the Dpp (resp. the DppNet) to each subset of . Let . If DppNet converges to a loss smaller thanits generative distribution is logsubmodular.
The result follows directly from Thm. 3 and the equivalence of norms in finite dimensional spaces.
Remark 1.
Cor. 1.1 is generalizable to the KL divergence loss via Pinsker’s inequality.
For this reason, we train our models by minimizing the distance between the predicted and target probabilities, rather than optimizing the loglikelihood of generative samples under the true Dpp.
Leveraging the sampling path.
When drawing samples from a Dpp, the standard Dpp sampling algorithm (Kulesza & Taskar, 2012, Alg. 1) generates the sample as a sequence, adding items one after the other until reaching a predetermined size^{4}^{4}4When not fixed by the user as in Dpps (Kulesza & Taskar, 2011), the expected sampled set size under a Dpp depends on the eigenspectrum of the kernel ., similarly to Alg. 1. We take advantage of this by recording all intermediary subsets generated by the Dpp when sampling training data: in practice, instead of training on subsets of size , we train on subsets of size
. Thus, our model is very much like an unrolled recurrent neural network.
3.2 Sampling over fixed and varying ground sets
In the simplest setting, we may wish to draw many samples over a ground set with a fixed feature embedding. In this case, we wish to model a Dpp with a fixed kernel via a generative neural network. Specifically, we consider a fixed Dpp with kernel and wish to obtain a generative model such that its distribution is close (under a chosen metric) to the Dpp distribution .
More generally, in many cases we may care about sampling from a Dpp over a ground set of items that varies: this may be the case for example with a pool of products that are available for sale at a given time, or social media posts with a relevance that varies based on context. To leverage the speedup provided by dual Dpp sampling, we can only sample from the Dpp with kernel given by ; for more complex kernels, we once again incur the cost. Furthermore, training a static neural network for each new feature embedding may be too costly. Instead, we augment the static DppNet to include the feature matrix representing the ground set of all items as input to the network. Specifically, we draw inspiration for the dotproduct attention introduced in (Vaswani et al., 2017). In the original paper, the attention mechanism takes as input 3 matrices: the keys , the values , and the query . Attention is computed as where is the dimension of each query/key: the inner product acts as a proxy to the similarity between each query and each key. Finally, the reweighted value matrix is fed to the trainable neural network. Here, the feature representation of items in the input set acts as the query ; the feature representation of our ground set is both the keys and the values. In order for the attention mechanism to make sense in the framework of Dpp modeling, we make two modifications to the attention in (Vaswani et al., 2017):

[leftmargin=*]

We want our network to attend to items that are dissimilar to the query (input subset): for each item in the input subset , we compute its pairwise dissimilarity to each item in as the vector .

Instead of returning this matrix of dissimilarities , we return a vector in the probability simplex such that . This allows us to have a fixedsize input to the neural network, and simultaneously enforces the desirable property that similarity to a single item is enough to disqualify an element from the ground set. Note that we could also return in the form of a matrix, but this would be counterproductive to speeding up Dpp sampling.
Putting everything together, our attention vector is computed via the inhibitive attention mechanism
(4) 
where represents the rowwise multiplication operator; this vector can be computed in time. The attention component of the neural network finally feeds the elementwise multiplication of each row of with to the feedforward component. Given and a subset , the network is trained as in the static case to learn the marginal probabilities of adding any item in to under a Dpp with a kernel dependent on . In practice, we set to be an exponentiated quadratic kernel constructed with the features .
Remark 2.
Dual sampling for Dpps as introduced in (Kulesza & Taskar, 2012) is efficient only when sampling from a Dpp with kernel ; for nonlinear kernels, a lowrank decomposition of must first be obtained, which in the worst case requires operations. In comparison, the dynamic DppNet can be trained on any Dpp kernel, while only requiring as input.
4 Experimental results
To evaluate DppNet, we look at its performance both as a proxy for a static Dpp (Section 4.1) and as a tool for generating diverse subsets of varying ground sets (Section 4.2
). Our models are trained with TensorFlow, using the Adam optimizer. Hyperparameters are tuned to maximize the normalized loglikelihood of generated subsets. We compare
DppNet to Dpp performance as well as two additional baselines:
[leftmargin=*]

Unif: Uniform sampling over the ground set.

Medoids: The medoids clustering algorithm (Hastie et al., 2001, 14.3.10), applied to items in the ground set, with distance between points computed as the same distance metric used by the Dpp. Conversely to means, Med uses data points as centers for each cluster.
We use the negative loglikelihood (NLL) of a subset under a Dpp constructed over the ground set to evaluate the subsets obtained by all methods. This choice is motivated by the following considerations: Dpps have become a standard way of measuring and enforcing diversity over subsets of data in machine learning, and b) to the extent of our knowledge, there is no other standard method to benchmark the diversity of a selected subset that depends on specific dataset encodings.
4.1 Sampling over the unit square
We begin by analyzing the performance of a DppNet trained on a Dpp with fixed kernel over the unit square. This is motivated by the need for diverse sampling methods on the unit hypercube, motivated by e.g. quasiMonte Carlo methods, latin hypercube sampling (McKay et al., 1979) and low discrepancy sequences. The ground set consists of the 100 points lying at the intersections of the grid on the unit square. The Dpp is defined by setting its kernel to . As the Dpp kernel is fixed, these experiments exclude the inhibitive attention mechanism. We report the performance of the different sampling methods in Figure 3. Visually (Figure 2(a)) and quantitively (Figure 2(b)), DppNet improves significantly over all other baselines. The NLL of DppNet samples is almost identical to that of true Dpp samples. Furthermore, greedily sampling the mode from the DppNet achieves a better NLL than Dpp samples themselves. Numerical results are reported in Table 1.


(1 hidden layer with 841 neurons) trained on a single
Dpp kernel. Visually, DppNet gives similar results to the full Dpp (left). As evaluated by Dpp NLL, the DppNet’s mode achieves superior performance to the full Dpp, and DppNet sampling overlaps completely with Dpp sampling (right).Uniform  Medoids  DppNet  Dpp 

4.2 Sampling over variable ground sets
We evaluate the performance of DppNets on varying ground set sizes through the MNIST (LeCun & Cortes, 2010), CelebA (Liu et al., 2015), and MovieLens (Harper & Konstan, 2015) datasets. For MNIST and CelebA, we generate feature representations of length 32 by training a Variational AutoEncoder (Kingma & Welling, 2014) on the dataset^{5}^{5}5Details on the encoders are provided in Appendix B.; for MovieLens, we obtain a feature vector for each movie by applying nonnegative matrix factorization the rating matrix, obtaining features of length 10. Experimental results presented below were obtained using feature representations obtained via the test instances of each dataset. The DppNet is trained based on samples from Dpps with a linear kernel for MovieLens and with an exponentiated quadratic kernel for the image datasets. Bandwidths were set to for MNIST and for CelebA in order to obtain a Dpp average sample size 20: recall that for a Dpp with kernel , the expected sample size is given by the formula
For MNIST, Figure 4 shows images selected by the baselines and the DppNet, chosen among 100 digits with either random labels or all identical labels; visually, DppNet and Dpp samples provide a wider coverage of writing styles. However, the NLL of samples from DppNet decay significantly, whereas the DppNet mode continues to maintain competitive performance with Dpp samples. Numerical results for MNIST are reported in Table 2; additionally to the previous baselines, we also consider two further ways of generating subsets. InhibAttn samples items from the multinomial distribution generated by the inhibitive attention mechanism only (without the subsequent neural network). NoAttn
is a pure feedforward neural network without attention; after hyperparameter tuning, we found that the best architecture for this model consisted in 6 layers of 585 neurons each. Table
2 reveals that both the attention mechanism and the subsequent neural network are crucial to modeling Dpp samples. Strikingly, DppNet performs significantly better than other baselines even on feature matrices drawn from a single class of digits (Table 2), despite the training distribution over feature matrices being much less specialized. This implies that DppNet sampling for dataset summarization may be leveraged to focus on subareas of datasets that are identified as areas of interest. Numerical results for CelebA and MovieLens are reported in Table 3, confirming the modeling ability of DppNets.All  0  1  2  3  4  5  6  7  8  9  

Dpp  49.2 0.1  52.2 0.1  60.5 0.1  49.8 0.0  50.7 0.1  51.0 0.1  50.4 0.1  51.6 0.1  51.5 0.1  50.9 0.1  52.7 0.1 
Unif  51.6 0.1  54.9 0.1  65.1 0.1  51.5 0.1  52.9 0.1  53.3 0.1  52.4 0.1  54.6 0.1  55.1 0.1  53.3 0.1  56.2 0.1 
Medoids  51.0 0.1  55.1 0.1  65.0 0.1  51.5 0.0  52.9 0.1  53.1 0.1  52.4 0.0  54.4 0.1  55.1 0.1  53.2 0.1  56.1 0.1 
InhibAttn  51.3 0.1  54.7 0.1  65.0 0.1  51.4 0.1  52.8 0.1  53.0 0.1  52.1 0.1  54.5 0.1  54.9 0.1  53.2 0.1  55.9 0.1 
NoAttn  51.4 0.1  54.9 0.1  65.4 0.1  51.5 0.1  52.9 0.1  53.3 0.1  52.2 0.1  54.6 0.1  55.2 0.1  53.3 0.1  56.1 0.1 
DPPNet Mode  48.6 0.2  53.6 0.3  63.6 0.4  50.8 0.2  51.4 0.3  51.6 0.4  51.8 0.3  52.8 0.3  52.7 0.4  50.9 0.3  55.0 0.4 
Dataset  Kernel  Dpp Goal  Uniform  Medoids  DppNet Mode 

CelebA  Exp. quadratic  49.04 2.03  50.84 1.53  51.18 1.34  49.28 1.57 
MovieLens  Linear  84.29 0.20  92.04 0.17  88.90 0.16  80.21 0.33 
Finally, we verify that DppNet allows for significantly faster sampling by running Dpp and DppNet sampling for subsets of size 20 drawn from a ground set of size 100 with both a standard Dpp and DppNet (using the MNIST architecture). Both methods were implemented in graphmode TensorFlow. Sampling batches of size 32, standard Dpp sampling costs 2.74 0.02 seconds; DppNet sampling takes 0.10 0.001 seconds, amounting to an almost 30fold speed improvement.
4.3 DppNet for kernel reconstruction


As a final experiment, we evaluate DppNet’s performance on a downstream task for which Dpps have been shown to be useful: kernel reconstruction using the Nyström method (Nyström, 1930; Williams & Seeger, 2001). Given a positive semidefinite matrix , the Nyström method approximates by where denotes the pseudoinverse of and (resp. ) is the submatrix of formed by its rows (resp. columns) indexed by . The Nyström method is a popular method to scale up kernel methods and has found many applications in machine learning (see e.g. (Bac, ; She, ; Fow, ; Tal, )). Importantly, the approximation quality directly depends on the choice of subset . Recently, Dpps have been shown to be a competitive approach for selecting (Mariet et al., 2018; Li et al., 2016b). Following the approach of Li et al. (2016b), we evaluate the quality of the kernel reconstruction by learning a regression kernel on a training set, and reporting the prediction error on the test set using the Nyström reconstructed kernel . Additionally to the full Dpp, we also compare DppNet to the MCMC sampling method with quadrature acceleration (Li et al., 2016b, c) Figure 5 reports our results on the Ailerons dataset^{6}^{6}6http://www.dcc.fc.up.pt/~ltorgo/Regression/DataSets.html also used in Li et al. (2016b). We start with a ground set size of 1000 and compute the resulting root mean squared error (RMSE) of the regression using various sized subsets selected by sampling from a DPP, the MCMC method of Li et al. (2016c), using the full ground set and DPPnet. Figure 4(b) reports the runtimes for each method. We note that while all methods were run on CPU, DPPNet is more amenable to acceleration using GPUs.
5 Conclusion and discussion
We introduced DppNets, generative networks trained on Dpps over static and varying ground sets which enable fast and modular sampling in a wide variety of scenarios. We showed experimentally on several datasets and standard Dpp applications that DppNets obtain competitive performance as evaluated in terms of NLLs, while being amenable to the extensive recent advances in speeding up computation for neural network architectures. Although we trained our models on Dpps on exponentiated quadratic and linear kernels; we can train on any kernel type built from a feature representations of the dataset. This is not the case for dual Dpp exact sampling, which requires that the Dpp kernel be for faster sampling. DppNets are not exchangeable: that is, two sequences and where is a permutation of , which represent the same set of items, will not in general have the same probability under a DppNet. Exchangeability can be enforced by leveraging previous work (Zaheer et al., 2017); however, nonexchangeability can be an asset when sampling a ranking of items. Our models are trained to take as input a fixedsize subset representation; we aim to investigate the ability to take a variablelength encoding as input as future work. The scaling of the DppNet’s complexity with the ground set size also remains an open question. However, standard tricks to enforce fixedsize ground sets such as subsampling from the dataset may be applied to DppNets. Similarly, if further speedups are necessary, subsampling from the ground set – a standard approach for Dpp sampling over very large set sizes – can be combined with DppNet sampling. In light of our results on dataset sampling, the question of whether encoders can be trained to produce encodings conducive to dataset summarization via DppNets seems of particular interest. Assuming knowledge of the (encodingindependent) relative diversity of a large quantity of subsets, an endtoend training of the encoder and the DppNet simultaneously may yield interesting results. Finally, although Corollary 1.1 shows the logsubmodularity of the Dpp can be transferred to a generative model, understanding which additional properties of training distributions may be conserved through careful training remains an open question which we believe to be of high significance to the machine learning community in general.
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Appendix A Maintaining logsubmodularity in the generative model
Theorem 2.
Let be a strictly submodular distribution over subsets of a ground set , and be a distribution over the same space such that
(5) 
Then is also submodular.
Proof.
In all the following, we assume that are subsets of a ground set such that and (the inequalities being immediate in these corner cases). Let
By the strict submodularity hypothesis, we know . Let such that and . To show the logsubmodularity of , it suffices to show that
By definition of ,
From equation 5, we know that
It follows that
which proves the submodularity of . ∎
Appendix B Encoder details
For the MNIST encodings, the VAE encoder consists of a 2dconvolutional layer with 64 filters of height and width 4 and strides of 2, followed by a 2d convolution layer with 128 filters (same height, width and strides), then by a dense layer of 1024 neurons. The encodings are of length 32.
CelebA encodings were generated by a VAE using a Wide Residual Network (Zagoruyko & Komodakis, 2016) encoder with 10 layers and filtermultiplier , a latent space of 32 fullcovariance Gaussians, and a deconvolutional decoder trained endtoend using an ELBO loss. In detail, the decoder architecture consists of a 16K dense layer followed by a sequence of convolutions with filters interleaved with upsampling layers and a final convolution with output channels for each of 5 components in a mixture of quantized logistic distributions representing the decoded image.
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