How to forecast next year's portfolio-wide credit default rate based on last year's default observations and the current score distribution? A classical approach to this problem consists of fitting a mixture of the conditional score distributions observed last year to the current score distribution. This is a special (simple) case of a finite mixture model where the mixture components are fixed and only the weights of the components are estimated. The optimum weights provide a forecast of next year's portfolio-wide default rate. We point out that the maximum-likelihood (ML) approach to fitting the mixture distribution not only gives an optimum but even an exact fit if we allow the mixture components to vary but keep their density ratio fix. From this observation we can conclude that the standard default rate forecast based on last year's conditional default rates will always be located between last year's portfolio-wide default rate and the ML forecast for next year. As an application example, then cost quantification is discussed. We also discuss how the mixture model based estimation methods can be used to forecast total loss. This involves the reinterpretation of an individual classification problem as a collective quantification problem.

Learning structured outputs with general structures is computationally challenging, except for tree-structured models. Thus we propose an efficient boosting-based algorithm AdaBoost.MRF for this task. The idea is based on the realization that a graph is a superimposition of trees. Different from most existing work, our algorithm can handle partial labelling, and thus is particularly attractive in practice where reliable labels are often sparsely observed. In addition, our method works exclusively on trees and thus is guaranteed to converge. We apply the AdaBoost.MRF algorithm to an indoor video surveillance scenario, where activities are modelled at multiple levels.

Markov Logic Networks (MLNs) provide a unifying framework that incorporates first-order logic and probability. However, learning the structure of MLNs is a computationally hard task due to the large search space and the intractable clause evaluation. In this paper, we propose a random walk-based approach to learn MLN structure in a scalable manner. It uses the interactions existing among the objects to constrain the search space of candidate clauses. Specifically, we obtain representative subset of simple paths by sampling from all sequences of distinct objects. We then transform each sampled path into possible ground atoms, and use them to form clauses. Based on the resulting ground network, we finally attach a set of weights to the clauses by optimizing l 1 -constrained conditional likelihood. The experimental results demonstrate that our approach performs favorably compared to previous approaches.

Automatically computing a cinematographically consistent sequence of shots over a set of actions occurring in a 3D world is a complex task that requires not only the computation of appropriate shots (viewpoints) and appropriate transitions between shots (cuts), but more importantly the ability to encode and reproduce elements of cinematographic style that exist in real movies. In this paper, we propose an expressive automated cinematography model that learns some elements of style from real movies and reproduces them in synthetic movies. The model relies on a Hidden Markov Model representation of the editing process. The proposed model is more general than existing representations that encode cinematographic idioms and proves to be more expressive in the possible variations of style it offers.

Intractable inference has been a major barrier to the wide adoption of statistical relational models. Existing exact methods suffer from a lack of scalability, and approximate methods tend to be unreliable. Sum-product networks (SPNs; Poon and Domingos 2011) are a recently-proposed probabilistic architecture that guarantees tractable exact inference, even on many high-treewidth models. SPNs are a propositional architecture, treating the instances as independent and identically distributed. In this paper, we extend SPNs to the relational setting, resulting in Relational Sum-Product Networks (RSPNs). Previous tractable statistical relational models (Domingos and Webb 2012; Webb and Domingos 2013) defined their models over a pre-determined set of objects, and therefore could not be generalized to new mega-examples. In contrast, RSPNs can be learned and applied to previous unseen examples. We present a learning algorithm for RSPNs; in preliminary experiments, RSPNs outperform Markov Logic Networks (Richardson and Domingos 2006) in both running time and predictive accuracy.

Transfer learning can improve the reinforcement learning of a new task by allowing the agent to reuse knowledge acquired from other source tasks. Despite their success, transfer learning methods rely on having relevant source tasks; transfer from inappropriate tasks can inhibit performance on the new task. For fully autonomous transfer, it is critical to have a method for automatically choosing relevant source tasks, which requires a similarity measure between Markov Decision Processes (MDPs). This issue has received little attention, and is therefore still a largely open problem. This paper presents a data-driven automated similarity measure for MDPs. This novel measure is a significant step toward autonomous reinforcement learning transfer, allowing agents to: (1) characterize when transfer will be useful and, (2) automatically select tasks to use for transfer. The proposed measure is based on the reconstruction error of a restricted Boltzmann machine that attempts to model the behavioral dynamics of the two MDPs being compared. Empirical results illustrate that this measure is correlated with the performance of transfer and therefore can be used to identify similar source tasks for transfer learning.

Complex decision making scenarios require maintaining high level of concentration and acquiring knowledge about the context of the task in hand. Focus of attention is not only affected by contextual factors but also by the way operators interact with the information. Conversely, determining optimal ways to interact with this information can augment operators’ cognition. However, challenges exist for determining efficient mathematical frameworks and sound metrics to infer, reason and assess the level of attention during spatio-temporal complex problem solving in hybrid human-machine systems. This paper proposes a computational framework based on a Bayesian approach (BAN) to infer users’ focus of attention based on physical expression generated from embodied interaction and further support decision-making in an unobtrusive manner. Experiments involving five interaction modalities (vision-based gesture interaction, glove-based gesture interaction, speech, feet, and body balance) were conducted to assess the proposed framework’s feasibility including the likelihood of assessed attention from enhanced BAN and task performance. Results confirm that physical expressions have a determining effect in the quality of the solutions in spatio-navigational type of problems.

Mining companies typically process all the material extracted from a mine site using processes which are extremely consumptive of energy and chemicals. Sorting the good material from the bad would effectively reduce required resources by leaving behind the bad material and only transporting and processing the good material. We use a relational influence diagram with an explicit utility model applied to the scenario in which an unknown number of rocks in unknown positions with unknown mineral compositions pass over 7 sensors toward 7 diverters on a high-throughput rock-sorting machine developed by MineSense Technologies Ltd. After receiving noisy sensor data, the system has 400 ms to decide whether to activate diverters which will divert the rocks into either a keep or discard bin. We learn the model offline and do online inference. Our result improves over the current state-of-the-art.

We study the problem of reasoning in the probabilistic Description Logic BEL. Using a novel structure, we show that probabilistic reasoning in this logic can be reduced in polynomial time to standard inferences over a Bayesian network. This reduction provides tight complexity bounds for probabilistic reasoning in BEL.

Using Markov logic to integrate logical and distributional information in natural-language semantics results in complex inference problems involving long, complicated formulae. Current inference methods for Markov logic are ineffective on such problems. To address this problem, we propose a new inference algorithm based on SampleSearch that computes probabilities of complete formulae rather than ground atoms. We also introduce a modified closed-world assumption that significantly reduces the size of the ground network, thereby making inference feasible. Our approach is evaluated on the recognizing textual entailment task, and experiments demonstrate its dramatic impact on the efficiency of inference.