Path queries on a knowledge graph can be used to answer compositional questions such as "What languages are spoken by people living in Lisbon?". However, knowledge graphs often have missing facts (edges) which disrupts path queries. Recent models for knowledge base completion impute missing facts by embedding knowledge graphs in vector spaces. We show that these models can be recursively applied to answer path queries, but that they suffer from cascading errors. This motivates a new "compositional" training objective, which dramatically improves all models' ability to answer path queries, in some cases more than doubling accuracy. On a standard knowledge base completion task, we also demonstrate that compositional training acts as a novel form of structural regularization, reliably improving performance across all base models (reducing errors by up to 43%) and achieving new state-of-the-art results.

In multi-label learning, each object is represented by a single instance while associated with a set of class labels. Due to the huge (exponential) number of possible label sets for prediction, existing approaches mainly focus on how to exploit label correlations to facilitate the learning process. Nevertheless, an intrinsic characteristic of learning from multi-label data, i.e. the widely-existing class-imbalance among labels, has not been well investigated. Generally, the number of positive training instances w.r.t. each class label is far less than its negative counterparts, which may lead to performance degradation for most multi-label learning techniques. In this paper, a new multi-label learning approach named Cross-Coupling Aggregation (COCOA) is proposed, which aims at leveraging the exploitation of label correlations as well as the exploration of class-imbalance. Briefly, to induce the predictive model on each class label, one binary-class imbalance learner corresponding to the current label and several multi-class imbalance learners coupling with other labels are aggregated for prediction. Extensive experiments clearly validate the effectiveness of the proposed approach, especially in terms of imbalance-specific evaluation metrics such as F-measure and area under the ROC curve.

However, by being autonomous, structure of the policy, which is often critical for understanding these approaches have the problem of discovering the demonstration, is unobserved. We unnatural hierarchies, which may be difficult to interpret and formulate this problem as active learning of Probabilistic communicate to people. State-Dependent Grammars (PSDGs) from In this paper, we study the problem of learning policies demonstrations. Given a set of expert demonstrations, with hierarchical structure from demonstrations of a teacher our approach learns a hierarchical policy by whose policy is structured hierarchically, with natural applications actively selecting demonstrations and using queries to problems such as tutoring arithmetic, cooking, and to explicate their intentional structure at selected furniture assembly. A key challenge in this problem is that the points. Our contributions include a new algorithm demonstrations do not reveal the hierarchical task structure of for imitation learning of hierarchical policies and the teacher. Rather, only ground states and teacher actions are principled heuristics for the selection of demonstrations directly observable. This can lead to significant ambiguity in and queries.

In this paper, we present a novel approach for medical synonym extraction. We aim to integrate the term embedding with the medical domain knowledge for healthcare applications. One advantage of our method is that it is very scalable. Experiments on a dataset with more than 1M term pairs show that the proposed approach outperforms the baseline approaches by a large  margin.

Knowledge base (KB) completion adds new facts to a KB by making inferences from existing facts, for example by inferring with high likelihood nationality(X,Y) from bornIn(X,Y). Most previous methods infer simple one-hop relational synonyms like this, or use as evidence a multi-hop relational path treated as an atomic feature, like bornIn(X,Z) -> containedIn(Z,Y). This paper presents an approach that reasons about conjunctions of multi-hop relations non-atomically, composing the implications of a path using a recursive neural network (RNN) that takes as inputs vector embeddings of the binary relation in the path. Not only does this allow us to generalize to paths unseen at training time, but also, with a single high-capacity RNN, to predict new relation types not seen when the compositional model was trained (zero-shot learning). We assemble a new dataset of over 52M relational triples, and show that our method improves over a traditional classifier by 11%, and a method leveraging pre-trained embeddings by 7%.

We present Vector-Space Markov Random Fields (VS-MRFs), a novel class of undirected graphical models where each variable can belong to an arbitrary vector space. VS-MRFs generalize a recent line of work on scalar-valued, uni-parameter exponential family and mixed graphical models, thereby greatly broadening the class of exponential families available (e.g., allowing multinomial and Dirichlet distributions). Specifically, VS-MRFs are the joint graphical model distributions where the node-conditional distributions belong to generic exponential families with general vector space domains. We also present a sparsistent $M$-estimator for learning our class of MRFs that recovers the correct set of edges with high probability. We validate our approach via a set of synthetic data experiments as well as a real-world case study of over four million foods from the popular diet tracking app MyFitnessPal. Our results demonstrate that our algorithm performs well empirically and that VS-MRFs are capable of capturing and highlighting interesting structure in complex, real-world data. All code for our algorithm is open source and publicly available.

There is rising interest in vector-space word embeddings and their use in NLP, especially given recent methods for their fast estimation at very large scale. Nearly all this work, however, assumes a single vector per word type--ignoring polysemy and thus jeopardizing their usefulness for downstream tasks. We present an extension to the Skip-gram model that efficiently learns multiple embeddings per word type. It differs from recent related work by jointly performing word sense discrimination and embedding learning, by non-parametrically estimating the number of senses per word type, and by its efficiency and scalability. We present new state-of-the-art results in the word similarity in context task and demonstrate its scalability by training with one machine on a corpus of nearly 1 billion tokens in less than 6 hours.

Linear classifiers play a central role in supervised learning, with a rich and elegant theory. This setting assumes data is represented as points in a Hilbert space, either explicitly as feature vectors or implicitly via a kernel. A significant strength of the Hilbert-space model is its inner-product structure, which has been exploited statistically and algorithmically by sophisticated techniques from geometric and functional analysis, placing the celebrated hyperplane methods on a solid foundation. However, the success of the Hilbert-space model obscures its limitations -- perhaps the most significant of which is that it cannot represent many norms and distance functions that arise naturally in applications.

We have recently shown how to combine random walk inference over knowledge bases with vector space representations of surface forms, improving performance on knowledge base inference. In this paper, we formalize the connection of our prior work to logical inference rules, giving some general observations about methods for incorporating vector space representations into symbolic logic systems. Additionally, we present some promising preliminary work that extends these techniques to learning open-domain relations for the purpose of answering multiple choice questions, achieving 67% accuracy on a small test set.

Traditional approaches to knowledge base completion have been based on symbolic representations. Low-dimensional vector embedding models proposed recently for this task are attractive since they generalize to possibly unlimited sets of relations. A significant draw- back of previous embedding models for KB completion is that they merely support reasoning on individual relations (e.g., bornIn ( X, Y ) ⇒ nationality ( X, Y ) ). In this work, we develop models for KB completion that support chains of reasoning on paths of any length using compositional vector space models. We construct compositional vector representations for the paths in the KB graph from the semantic vector representations of the binary relations in that path and perform inference directly in the vector space. Unlike previous methods, our approach can generalize to paths that are unseen in training and, in a zero-shot setting, predict target relations without supervised training data for that relation.