The qualitative problem is of great importance as in several applications it is The de facto model for dynamic systems with probabilistic required that the correct behavior happens with probability 1, and nondeterministic behavior are Markov decision processes e.g., in the analysis of randomized embedded schedulers, (MDPs) (Howard 1960). MDPs provide the appropriate the important question is whether every thread progresses model to solve control and probabilistic planning problems with probability 1. Also in applications where it might be (Filar and Vrieze 1997; Puterman 1994), where the nondeterminism sufficient that the correct behavior happens with probability represents the choice of the control actions for at least λ 1,the correct choice of the threshold λ can the controller (or planner), while the stochastic response of be still challenging, due to simplifications and imprecisions the system to control actions is represented by the probabilistic introduced during modeling.

A large amount of ordinal-valued data exist in many domains, including medical and health science, social science, economics, political science, etc. Unlike image and speech datasets of real-valued data, learning with ordinal variables (i.e., features) presents unique challenges. In particular, the nominal differences between those feature values, which are just ranks, do not necessarily correspond to the real distances between the corresponding categories. Given their wide existence, it is imperative to develop machine learning algorithms that specifically address the need to model and infer with such data. In this paper, we present a novel metric learning algorithm that takes into consideration the nature of ordinal data. Our approach treats ordinal values as latent variables in intervals. Our algorithm then learns what those intervals are as well as distance metrics to measure distances between latent variables in those intervals. We derive the corresponding optimization algorithm and demonstrate how that can be solved effectively. Experimental results show that the proposed approach significantly improves baselines that do not explicitly model ordinal features.

We present a probabilistic generative model for inferring a description of coordinated, recursively structured group activities at multiple levels of temporal granularity based on observations of individuals’ trajectories. The model accommodates: (1) hierarchically structured groups, (2) activities that are temporally and compositionally recursive, (3) component roles assigning different subactivity dynamics to subgroups of participants, and (4) a nonparametric Gaussian Process model of trajectories. We present an MCMC sampling framework for performing joint inference over recursive activity descriptions and assignment of trajectories to groups, integrating out continuous parameters. We demonstrate the model’s expressive power in several simulated and complex real-world scenarios from the VIRAT and UCLA Aerial Event video data sets.

We present the first probabilistic model to capture all levels of the Minsky Frame structure, with the goal of corpus-based induction of scenario definitions. Our model unifies prior efforts in discourse-level modeling with that of Fillmore's related notion of frame, as captured in sentence-level, FrameNet semantic parses; as part of this, we resurrect the coupling among Minsky's frames, Schank's scripts and Fillmore's frames, as originally laid out by those authors. Empirically, our approach yields improved scenario representations, reflected quantitatively in lower surprisal and more coherent latent scenarios.

Time series modeling is an important problem with many applications in different domains. Here we consider discriminative learning from time series, where we seek to predict an output response variable based on time series input. We develop a method based on convolution kernels to model discriminative learning over streams of text. Our method outperforms competitive baselines in three synthetic and two real datasets, rumour frequency modeling and popularity prediction tasks.

Many natural and social phenomena can be modeled by interaction point processes (IPPs) (Diggle et al. 1994), stochastic point processes considering the interaction between points. In this paper, we propose the infinite branching model (IBM), a Bayesian statistical model that can generalize and extend some popular IPPs, e.g., Hawkes process (Hawkes 1971; Hawkes and Oakes 1974). It treats IPP as a mixture of basis point processes with the aid of a distance dependent prior over branching structure that describes the relationship between points. The IBM can estimate point event intensity, interaction mechanism and branching structure simultaneously. A generic Metropolis-within-Gibbs sampling method is also developed for model parameter inference. The experiments on synthetic and real-world data demonstrate the superiority of the IBM.

Bounding the tree-width of a Bayesian network can reduce the chance of overfitting, and allows exact inference to be performed efficiently. Several existing algorithms tackle the problem of learning bounded tree-width Bayesian networks by learning from k-trees as super-structures, but they do not scale to large domains and/or large tree-width. We propose a guided search algorithm to find k-trees with maximum Informative scores, which is a measure of quality for the k-tree in yielding good Bayesian networks. The algorithm achieves close to optimal performance compared to exact solutions in small domains, and can discover better networks than existing approximate methods can in large domains. It also provides an optimal elimination order of variables that guarantees small complexity for later runs of exact inference. Comparisons with well-known approaches in terms of learning and inference accuracy illustrate its capabilities.

We present an initial proposal for modular object-oriented MDPs, an extension of OO-MDPs that abstracts complex domains that are partially observable and stochastic with multiple goals. Modes reduce the curse of dimensionality by reducing the number of attributes, objects, and actions into only the features relevant for each goal. These modes may also be used as an abstracted domain to be transferred to other modes or to another domain.

We consider an autonomous agent operating in a stochastic, partially-observable, multiagent environment, that explicitly models the other agents as probabilistic deterministic finite-state controllers (PDFCs) in order to predict their actions. We assume that such models are not given to the agent, but instead must be learned from (possibly imperfect) observations of the other agents' behavior. The agent maintains a belief over the other agents' models, that is updated via Bayesian inference. To represent this belief we place a flexible stick-breaking distribution over PDFCs, that allows the posterior to concentrate around controllers whose size is not bounded and scales with the complexity of the observed data. Since this Bayesian inference task is not analytically tractable, we devise a Markov chain Monte Carlo algorithm to approximate the posterior distribution. The agent then embeds the result of this inference into its own decision making process using the interactive POMDP framework. We show that our learning algorithm can learn agent models that are behaviorally accurate for problems of varying complexity, and that the agent's performance increases as a result.

When learning a hidden Markov model (HMM), sequential observations can often be complemented by real-valued summary response variables generated from the path of hidden states. Such settings arise in numerous domains, including many applications in biology, like motif discovery and genome annotation. In this paper, we present a flexible framework for jointly modeling both latent sequence features and the functional mapping that relates the summary response variables to the hidden state sequence. The algorithm is compatible with a rich set of mapping functions. Results show that the availability of additional continuous response variables can simultaneously improve the annotation of the sequential observations and yield good prediction performance in both synthetic data and real-world datasets.