How should we gather information to make effective decisions? A classical answer to this fundamental problem is given by the decision-theoretic value of information. Unfortunately, optimizing this objective is intractable, and myopic (greedy) approximations are known to perform poorly. In this paper, we introduce DiRECt, an efficient yet near-optimal algorithm for nonmyopically optimizing value of information. Crucially, DiRECt uses a novel surrogate objective that is: (1) aligned with the value of information problem (2) efficient to evaluate and (3) adaptive submodular. This latter property enables us to utilize an efficient greedy optimization while providing strong approximation guarantees. We demonstrate the utility of our approach on four diverse case-studies: touch-based robotic localization, comparison-based preference learning, wild-life conservation management, and preference elicitation in behavioral economics. In the first application, we demonstrate DiRECt in closed-loop on an actual robotic platform.

Online, sample-based planning algorithms for POMDPs have shown great promise in scaling to problems with large state spaces, but they become intractable for large action and observation spaces. This is particularly problematic in multiagent POMDPs where the action and observation space grows exponentially with the number of agents. To combat this intractability, we propose a novel scalable approach based on sample-based planning and factored value functions that exploits structure present in many multiagent settings. This approach applies not only in the planning case, but also in the Bayesian reinforcement learning setting. Experimental results show that we are able to provide high quality solutions to large multiagent planning and learning problems.

Accurate road speed predictions can help drivers in smart route planning. Although the issue has been studied previously, most existing work focus on arterial roads only, where sensors are configured closely for collecting complete real-time data. For collector roads where sensors sparsly cover, however, speed predictions are often ignored. With GPS-equipped floating car signals being available nowadays, we aim at forecasting collector road speeds by utilizing these signals. The main challenge compared with arterial roads comes from the missing data. In a time slot of the real case, over 90% of collector roads cannot be covered by enough floating cars. Thus most traditional approaches for arterial roads, relying on complete historical data, cannot be employed directly. Aiming at solving this problem, we propose a multi-view road speed prediction framework. In the first view, temporal patterns are modeled by a layered hidden Markov model; and in the second view, spatial patterns are modeled by a collective matrix factorization model. The two models are learned and inferred simultaneously in a co-regularized manner. Experiments conducted in the Beijing road network, based on 10K taxi signals in 2 years, have demonstrated that the approach outperforms traditional approaches by 10% in MAE and RMSE.

The main computational bottleneck in various sampling based and local-search based inference algorithms for Markov logic networks (e.g., Gibbs sampling, MC-SAT, MaxWalksat, etc.) is computing the number of groundings of a first-order formula that are true given a truth assignment to all of its ground atoms. We reduce this problem to the problem of counting the number of solutions of a constraint satisfaction problem (CSP) and show that during their execution, both sampling based and local-search based algorithms repeatedly solve dynamic versions of this counting problem. Deriving from the vast amount of literature on CSPs and graphical models, we propose an exact junction-tree based algorithm for computing the number of solutions of the dynamic CSP, analyze its properties, and show how it can be used to improve the computational complexity of Gibbs sampling and MaxWalksat. Empirical tests on a variety of benchmarks clearly show that our new approach is several orders of magnitude more scalable than existing approaches.

Restricted Boltzmann Machine (RBM) has been applied to a wide variety of tasks due to its advantage in feature extraction. Implementing sparsity constraint in the activated hidden units of RBM is an important improvement on RBM. The sparsity constraints in the existing methods are usually specified by users and are independent of the input data. However, the input data could be heterogeneous in content and thus naturally demand elastic and adaptive settings of the sparsity constraints. To solve this problem, we proposed a generalized model with adaptive sparsity constraint, named Gaussian Cardinality Restricted Boltzmann Machines (GC-RBM). In this model, the thresholds of hidden unit activations are decided by the input data and a given Gaussian distribution on the pre-training phase. We provide a principled method to train the GC-RBM with Gaussian prior. Experimental results on two real world data sets justify the effectiveness of the proposed method and its superiority over CaRBM in terms of classification accuracy.

Training a structured prediction model involves performing several loss-augmented inference steps. Over the lifetime of the training, many of these inference problems, although different, share the same solution. We propose AI-DCD, an Amortized Inference framework for Dual Coordinate Descent method, an approximate learning algorithm, that accelerates the training process by exploiting this redundancy of solutions, without compromising the performance of the model. We show the efficacy of our method by training a structured SVM using dual coordinate descent for an entityrelation extraction task. Our method learns the same model as an exact training algorithm would, but call the inference engine only in 10% – 24% of the inference problems encountered during training. We observe similar gains on a multi-label classification task and with a Structured Perceptron model for the entity-relation task.

We consider the problem of, given a probabilistic model on a set of random variables, how to add a new variable that depends on the other variables, without changing the original distribution. In particular, we consider relational models (such as Markov logic networks (MLNs)), where we cannot directly define conditional probabilities. In relational models, there may be an unbounded number of parents in the grounding, and conditional distributions need to be defined in terms of aggregators. The question we ask is whether and when it is possible to represent conditional probabilities at all in various relational models. Some aggregators have been shown to be representable by MLNs, by adding auxiliary variables; however it was unknown whether they could be defined without auxiliary variables. For other aggregators, it was not known whether they can be represented by MLNs at all. We obtained surprisingly strong negative results on the capability of flexible undirected relational models such as MLNs to represent aggregators without affecting the original model's distribution. We provide a map of what aspects of the models, including the use of auxiliary variables and quantifiers, result in the ability to represent various aggregators. In addition, we provide proof techniques which can be used to facilitate future theoretic results on relational models, and demonstrate them on relational logistic regression (RLR).

The hierarchical Dirichlet process (HDP) is a powerful nonparametric Bayesian approach to modeling groups of data which allows the mixture components in each group to be shared. However, in many cases the groups themselves are also in latent groups (categories) which may impact the modeling a lot. In order to utilize the unknown category information of grouped data, we present the hybrid nested/ hierarchical Dirichlet process (hNHDP), a prior that blends the desirable aspects of both the HDP and the nested Dirichlet Process (NDP). Specifically, we introduce a clustering structure for the groups. The prior distribution for each cluster is a realization of a Dirichlet process. Moreover, the set of cluster-specific distributions can share part of atoms between groups, and the shared atoms and specific atoms are generated separately. We apply the hNHDP to document modeling and bring in a mechanism to identify discriminative words and topics. We derive an efficient Markov chain Monte Carlo scheme for posterior inference and present experiments on document modeling.

Identifying aspect-based opinions has been studied extensively in recent years. However, existing work primarily focused on adjective, adverb, and noun expressions. Clearly, verb expressions can imply opinions too. We found that in many domains verb expressions can be even more important to applications because they often describe major issues of products or services. These issues enable brands and businesses to directly improve their products or services. To the best of our knowledge, this problem has not received much attention in the literature. In this paper, we make an attempt to solve this problem. Our proposed method first extracts verb expressions from reviews and then employs Markov Networks to model rich linguistic features and long distance relationships to identify negative issue expressions. Since our training data is obtained from titles of reviews whose labels are automatically inferred from review ratings, our approach is applicable to any domain without manual involvement. Experimental results using real-life review datasets show that our approach outperforms strong baselines.

The classical planning problem can be enriched with quantitative and qualitative user-defined preferences on how the system behaves on achieving the goal. In this paper, we propose the probabilistic preference planning problem for Markov decision processes, where the preferences are based on an enriched probabilistic LTL-style logic. We develop P4Solver, an SMT-based planner computing the preferred plan by reducing the problem to quadratic programming problem, which can be solved using SMT solvers such as Z3. We illustrate the framework by applying our approach on two selected case studies.