Scalable Training of Mixture Models via Coresets

Neural Information Processing Systems

How can we train a statistical mixture model on a massive data set? In this paper, we show how to construct coresets for mixtures of Gaussians and natural generalizations. A coreset is a weighted subset of the data, which guarantees that models fitting the coreset will also provide a good fit for the original data set. We show that, perhaps surprisingly, Gaussian mixtures admit coresets of size independent of the size of the data set. More precisely, we prove that a weighted set of $O(dk 3/\eps 2)$ data points suffices for computing a $(1 \eps)$-approximation for the optimal model on the original $n$ data points.

On Coresets for Logistic Regression

Neural Information Processing Systems

Coresets are one of the central methods to facilitate the analysis of large data. We continue a recent line of research applying the theory of coresets to logistic regression. First, we show the negative result that no strongly sublinear sized coresets exist for logistic regression. To deal with intractable worst-case instances we introduce a complexity measure $\mu(X)$, which quantifies the hardness of compressing a data set for logistic regression. For data sets with bounded $\mu(X)$-complexity, we show that a novel sensitivity sampling scheme produces the first provably sublinear $(1\pm\eps)$-coreset.

Distributed k-means and k-median Clustering on General Topologies

Neural Information Processing Systems

This paper provides new algorithms for distributed clustering for two popular center-based objectives, $k$-median and $k$-means. These algorithms have provable guarantees and improve communication complexity over existing approaches. Following a classic approach in clustering by \cite{har2004coresets}, we reduce the problem of finding a clustering with low cost to the problem of finding a coreset' of small size. We provide a distributed method for constructing a global coreset which improves over the previous methods by reducing the communication complexity, and which works over general communication topologies. We provide experimental evidence for this approach on both synthetic and real data sets.

Coresets for Scalable Bayesian Logistic Regression

Neural Information Processing Systems

The use of Bayesian methods in large-scale data settings is attractive because of the rich hierarchical models, uncertainty quantification, and prior specification they provide. Standard Bayesian inference algorithms are computationally expensive, however, making their direct application to large datasets difficult or infeasible. Recent work on scaling Bayesian inference has focused on modifying the underlying algorithms to, for example, use only a random data subsample at each iteration. We leverage the insight that data is often redundant to instead obtain a weighted subset of the data (called a coreset) that is much smaller than the original dataset. We can then use this small coreset in any number of existing posterior inference algorithms without modification.

Dimensionality Reduction of Massive Sparse Datasets Using Coresets

Neural Information Processing Systems

In this paper we present a practical solution with performance guarantees to the problem of dimensionality reduction for very large scale sparse matrices. We show applications of our approach to computing the Principle Component Analysis (PCA) of any $n\times d$ matrix, using one pass over the stream of its rows. Our solution uses coresets: a scaled subset of the $n$ rows that approximates their sum of squared distances to \emph{every} $k$-dimensional \emph{affine} subspace. An open theoretical problem has been to compute such a coreset that is independent of both $n$ and $d$. An open practical problem has been to compute a non-trivial approximation to the PCA of very large but sparse databases such as the Wikipedia document-term matrix in a reasonable time.

Coresets for k-Segmentation of Streaming Data

Neural Information Processing Systems

Life-logging video streams, financial time series, and Twitter tweets are a few examples of high-dimensional signals over practically unbounded time. We consider the problem of computing optimal segmentation of such signals by k-piecewise linear function, using only one pass over the data by maintaining a coreset for the signal. The coreset enables fast further analysis such as automatic summarization and analysis of such signals. A coreset (core-set) is a compact representation of the data seen so far, which approximates the data well for a specific task -- in our case, segmentation of the stream. We show that, perhaps surprisingly, the segmentation problem admits coresets of cardinality only linear in the number of segments k, independently of both the dimension d of the signal, and its number n of points.

k-Means Clustering of Lines for Big Data

Neural Information Processing Systems

The input to the \emph{$k$-mean for lines} problem is a set $L$ of $n$ lines in $\mathbb{R} d$, and the goal is to compute a set of $k$ centers (points) in $\mathbb{R} d$ that minimizes the sum of squared distances over every line in $L$ and its nearest center. This is a straightforward generalization of the $k$-mean problem where the input is a set of $n$ points instead of lines. We suggest the first PTAS that computes a $(1 \epsilon)$-approximation to this problem in time $O(n \log n)$ for any constant approximation error $\epsilon \in (0, 1)$, and constant integers $k, d \geq 1$. This is by proving that there is always a weighted subset (called coreset) of $dk {O(k)}\log (n)/\epsilon 2$ lines in $L$ that approximates the sum of squared distances from $L$ to \emph{any} given set of $k$ points. Using traditional merge-and-reduce technique, this coreset implies results for a streaming set (possibly infinite) of lines to $M$ machines in one pass (e.g.

Coresets for Clustering with Fairness Constraints

Neural Information Processing Systems

In a recent work, \cite{chierichetti2017fair} studied the following fair'' variants of classical clustering problems such as k-means and k-median: given a set of n data points in R d and a binary type associated to each data point, the goal is to cluster the points while ensuring that the proportion of each type in each cluster is roughly the same as its underlying proportion. Subsequent work has focused on either extending this setting to when each data point has multiple, non-disjoint sensitive types such as race and gender \cite{bera2019fair}, or to address the problem that the clustering algorithms in the above work do not scale well. The main contribution of this paper is an approach to clustering with fairness constraints that involve {\em multiple, non-disjoint} attributes, that is {\em also scalable}. Our approach is based on novel constructions of coresets: for the k-median objective, we construct an \eps-coreset of size O(\Gamma k 2 \eps {-d}) where \Gamma is the number of distinct collections of groups that a point may belong to, and for the k-means objective, we show how to construct an \eps-coreset of size O(\Gamma k 3\eps {-d-1}). The former result is the first known coreset construction for the fair clustering problem with the k-median objective, and the latter result removes the dependence on the size of the full dataset as in \cite{schmidt2018fair} and generalizes it to multiple, non-disjoint attributes.

Coresets for Archetypal Analysis

Neural Information Processing Systems

Archetypal analysis represents instances as linear mixtures of prototypes (the archetypes) that lie on the boundary of the convex hull of the data. Archetypes are thus often better interpretable than factors computed by other matrix factorization techniques. However, the interpretability comes with high computational cost due to additional convexity-preserving constraints. In this paper, we propose efficient coresets for archetypal analysis. Theoretical guarantees are derived by showing that quantization errors of k-means upper bound archetypal analysis; the computation of a provable absolute-coreset can be performed in only two passes over the data.

Nonparametric Bayesian Structure Adaptation for Continual Learning Machine Learning

Continual Learning is a learning paradigm where machine learning mode ls are trained with sequential or streaming tasks. Two notable directions among the recent adva nces in continual learning with neural networks are ( i) variational Bayes based regularization by learning priors from pre vious tasks, and, ( ii) learning the structure of deep networks to adapt to new tasks. S o far, these two approaches have been orthogonal. We present a principled nonparametric Bayesian appr oach for learning the structure of feed-forward neural networks, addressing the shortcomings o f both these approaches. In our model, the number of nodes in each hidden layer can automatically grow with the in troduction of each new task, and inter-task transfer occurs through the overlapping of differ ent sparse subsets of weights learned by different tasks. On benchmark datasets, our model performs comparably or better than the state-of-the-art approaches, while also being able to adaptively infer the evolving network structure in the continual learning setting.