Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research

Incremental Sparse Bayesian Ordinal Regression

Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning. An important class of approaches to OR models the problem as a linear combination of basis functions that map features to a high dimensional non-linear space. However, most of the basis function-based algorithms are time consuming. We propose an incremental sparse Bayesian approach to OR tasks and introduce an algorithm to sequentially learn the relevant basis functions in the ordinal scenario. Our method, called Incremental Sparse Bayesian Ordinal Regression (ISBOR), automatically optimizes the hyper-parameters via the type-II maximum likelihood method. By exploiting fast marginal likelihood optimization, ISBOR can avoid big matrix inverses, which is the main bottleneck in applying basis function-based algorithms to OR tasks on large-scale datasets. We show that ISBOR can make accurate predictions with parsimonious basis functions while offering automatic estimates of the prediction uncertainty. Extensive experiments on synthetic and real word datasets demonstrate the efficiency and effectiveness of ISBOR compared to other basis function-based OR approaches

Identification of functionally related enzymes by learning-to-rank methods

Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored. In this work we show that rankings of that kind can be substantially improved by applying kernel-based learning algorithms. This approach enables the detection of statistical dependencies between similarities of the active cleft and the biological function of annotated enzymes. This is in contrast to search-based approaches, which do not take annotated training data into account. Similarity measures based on the active cleft are known to outperform sequence-based or structure-based measures under certain conditions. We consider the Enzyme Commission (EC) classification hierarchy for obtaining annotated enzymes during the training phase. The results of a set of sizeable experiments indicate a consistent and significant improvement for a set of similarity measures that exploit information about small cavities in the surface of enzymes

Neural Collaborative Filtering

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -- collaborative filtering -- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.Comment: 10 pages, 7 figure

Learning of classification models from group-based feedback

Learning of classification models in practice often relies on a nontrivial amount of human annotation effort. The most widely adopted human labeling process assigns class labels to individual data instances. However, such a process is very rigid and may end up being very time-consuming and costly to conduct in practice. Finding more effective ways to reduce human annotation effort has become critical for building machine learning systems that require human feedback. In this thesis, we propose and investigate a new machine learning approach - Group-Based Active Learning - to learn classification models from limited human feedback. A group is defined by a set of instances represented by conjunctive patterns that are value ranges over the input features. Such conjunctive patterns define hypercubic regions of the input data space. A human annotator assesses the group solely based on its region-based description by providing an estimate of the class proportion for the subpopulation covered by the region. The advantage of this labeling process is that it allows a human to label many instances at the same time, which can, in turn, improve the labeling efficiency. In general, there are infinitely many regions one can define over a real-valued input space. To identify and label groups/regions important for classification learning, we propose and develop a Hierarchical Active Learning framework that actively builds and labels a hierarchy of input regions. Briefly, our framework starts by identifying general regions covering substantial portions of the input data space. After that, it progressively splits the regions into smaller and smaller sub-regions and also acquires class proportion labels for the new regions. The proportion labels for these regions are used to gradually improve and refine a classification model induced by the regions. We develop three versions of the idea. The first two versions aim to build a single hierarchy of regions. One builds it statically using hierarchical clustering, while the other one builds it dynamically, similarly to the decision tree learning process. The third approach builds multiple hierarchies simultaneously, and it offers additional flexibility for identifying more informative and simpler regions. We have conducted comprehensive empirical studies to evaluate our framework. The results show that the methods based on the region-based active learning can learn very good classifiers from a very few and simple region queries, and hence are promising for reducing human annotation effort needed for building a variety of classification models