Scientific Track

Concurrent Inference of Topic Models and Distributed Vector Representations

Topic modeling techniques have been widely used to uncover dominant themes hidden inside an unstructured document collection. Though these techniques first originated inthe probabilistic analysis of word distributions, many deep learning approaches have been adopted recently. In this paper, we propose a novel neural network based architecture that produces distributed representation of topics to capture topical themes in a dataset.

Weighted Rank Correlation: A Flexible Approach based on Fuzzy Order Relations

Measures of rank correlation are commonly used in statistics to capture the degree of concordance between two orderings of the same set of items. Standard measures like the well-known Kendall tau and Spearman rho coefficients put equal emphasis on each position of a ranking. Yet, motivated by applications in which some of the positions (typically those on the top) are more important than others, a few weighted variants of these measures have been proposed. Most of these generalizations fail to meet desirable formal properties, however.

The blind leading the blind: Network-based location estimation under uncertainty

We propose a probabilistic method for inferring the geographical locations of linked objects, such as users in a social network. Unlike existing methods, our model does not assume that the exact locations of any subset of the linked objects, like neighbors in a social network, are known. The method efficiently leverages prior knowledge on the locations, resulting in high geolocation accuracies even if none of the locations are initially known.

Temporally Coherent Role-Topic Models (TCRTM): Deinterlacing Overlapping Activity Patterns

The Temporally Coherent Role-Topic Model (TCRTM)is a probabilistic graphical modelfor analyzing overlapping, loosely temporally structured activitiesin heterogeneous populations.Such structure appears in many domainswhere activities have temporal coherence, but no strong ordering.For instance, editing a PowerPoint presentationmay involve opening files, typing text, anddownloading images.

Structured Prediction of Sequences and Trees using Infinite Contexts

Linguistic structures exhibit a rich array of global phenomena, however commonly used Markov models are unable to adequately describe these phenomena due to their strong locality assumptions. We propose a novel hierarchical model for structured prediction over sequences and trees which exploits global context by conditioning each generation decision on an unbounded context of prior decisions. This builds on the success of Markov models but without imposing a fixed bound in order to better represent global phenomena.

Sparse Bayesian Recurrent Neural Networks

Recurrent neural networks (RNNs) have recently gained renewed attention from the machine learning community as effective methods for modeling variable-length sequences. Language modeling, handwriting recognition, and speech recognition are only few of the application domains where RNN-based models have achieved the state-of-the-art performance currently reported in the literature. Typically, RNN architectures utilize simple linear, logistic, or softmax output layers to perform data modeling and prediction generation.

Simplifying, Regularizing and Strengthening Sum-Product Network Structure Learning

The need for feasible inference in Probabilistic Graphical Models (PGMs) has lead to tractable models like Sum-Product Networks (SPNs). Their highly expressive power and their ability to provide exact and tractable inference make them very attractive for several real world applications, from computer vision to NLP.

Planning in Discrete and Continuous Markov Decision Processes by Probabilistic Programming

Real-world planning problems frequently involve mixtures of continuous and discrete state variables and actions, and are formulated in environments with an unknown number of objects. In recent years, probabilistic programming has emerged as a natural approach to capture and characterize such complex probability distributions with general-purpose inference methods. While it is known that a probabilistic programming language can be easily extended to represent Markov Decision Processes (MDPs) for planning tasks, solving such tasks is challenging.

Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs

We introduce an adaptive output-sensitive Metropolis-Hastings algorithm for probabilistic models expressed as programs, Adaptive Lightweight Metropolis-Hastings (AdLMH). The algorithm extends Lightweight Metropolis-Hastings (LMH) by adjusting the probabilities of proposing random variables for modification to improve convergence of the program output. We show that AdLMH converges to the correct equilibrium distribution and compare convergence of AdLMH to that of LMH on several test problems to highlight different aspects of the adaptation scheme.

Message Scheduling Methods for Belief Propagation

Approximate inference in large and densely connected graphical models is a challenging but highly relevant problem. Belief propagation, as a method for performing approximate inference in loopy graphs, has shown empirical success in many applications. However, convergence of belief propagation can only be guaranteed for simple graphs. Whether belief propagation converges depends strongly on the applied message update scheme, and specialized schemes can be highly beneficial. Yet, residual belief propagation is the only established method utilizing this fact to improve convergence properties.