The recently proposed Gaussian process dynamical models (GPDMs) have been successfully applied to time series modeling. There are four learning algorithms for GPDMs: maximizing a posterior (MAP), fixing the kernel hyperparameters α _ (Fix.α _ ), balanced GPDM (B-GPDM) and two-stage MAP (T.MAP), which are designed for model training with complete data. When data are incomplete, GPDMs reconstruct the missing data using a function of the latent variables before parameter updates, which, however, may cause cumulative errors. In this paper, we present four new algorithms (MAP+, Fix.α + , B-GPDM+ and T.MAP+) for learning GPDMs with incomplete training data and a new conditional model (CM+) for recovering incomplete test data. Our methods adopt the Bayesian framework and can fully and properly use the partially observed data. We conduct experiments on incomplete motion capture data (walk, run, swing and multiple-walker) and make comparisons with the existing four algorithms as well as k-NN, spline interpolation and VGPDS. Our methods perform much better on both training with incomplete data and recovering incomplete test data.

Mobile user verification is to authenticate whether a given user is the legitimate user of a smartphone device. Unlike the current methods that commonly require users active cooperation, such as entering a short pin or a one-stroke draw pattern, we propose a new passive verification method that requires minimal imposition of users through modelling users subtle mobility patterns. Specifically, our method computes the statistical ambience features on WiFi and cell tower data from location anonymized data sets and then we customize Hidden Markov Model (HMM) to capture the spatial-temporal patterns of each user's mobility behaviors. Our learned model is subsequently validated and applied to verify a test user in a time-evolving manner through sequential likelihood test. Experimentally, our method achieves 72% verification accuracy with less than a day's data and a detection rate of 94% of illegitimate users with only 2 hours of selected data. As the first verification method that models users' mobility pattern on location-anonymized smartphone data, our achieved result is significant showing the good possibility of leveraging such information for live user authentication.

Probabilistic systems are an important theme in AI domain. As the specification language, PCTL is the most frequently used logic for reasoning about probabilistic properties. In this paper, we present a natural and succinct probabilistic extension of Mu-calculus, another prominent logic in the concurrency theory. We study the relationship with PCTL. Surprisingly, the expressiveness is highly orthogonal with PCTL. The proposed logic captures some useful properties which cannot be expressed in PCTL. We investigate the model checking and satisfiability problem, and show that the model checking problem is in UP and co-UP, and the satisfiability checking can be decided via reducing into solving parity games. This is in contrast to PCTL as well, whose satisfiability checking is still an open problem.

Recently, several Web-scale knowledge harvesting systems have been built, each of which is competent at extracting information from certain types of data (e.g., unstructured text, structured tables on the web, etc.). In order to determine the response to a new query posed to such systems (e.g., is sugar a healthy food?), it is useful to integrate opinions from multiple systems. If a response is desired within a specific time budget (e.g., in less than 2 seconds), then maybe only a subset of these resources can be queried. In this paper, we address the problem of knowledge integration for on-demand time-budgeted query answering. We propose a new method, AskWorld, which learns a policy that chooses which queries to send to which resources, by accommodating varying budget constraints that are available only at query (test) time. Through extensive experiments on real world datasets, we demonstrate AskWorld’s capability in selecting most informative resources to query within test-time constraints, resulting in improved performance compared to competitive baselines.

They concluded that UCT quickly finds Self-play Monte Carlo Tree Search (MCTS) has a good but suboptimal policy, while Outcome Sampling initially been successful in many perfect-information twoplayer learns more slowly but converges to the optimal policy games. Although these methods have been over time. In this paper, we address the question whether the extended to imperfect-information games, so far inability of UCT to converge to a Nash equilibrium can be they have not achieved the same level of practical overcome while retaining UCT's fast initial learning rate. We success or theoretical convergence guarantees focus on the full-game MCTS setting, which is an important as competing methods. In this paper we step towards developing sound variants of online MCTS in introduce Smooth UCT, a variant of the established imperfect-information games. Upper Confidence Bounds Applied to Trees In particular, we introduce Smooth UCT, which combines (UCT) algorithm.

Preventing attacks in a computer network is the core problem in network security. We introduce a new game-theoretic model of the interaction between a network administrator who uses limited resource to harden a network and an attacker who follows a multi-stage plan to attack the network. The possible plans of the attacker are compactly represented using attack graphs, while the defender adds fake targets (honeypots) to the network to deceive the attacker. The compact representation of the attacker's strategies presents a computational challenge and finding the best response of the attacker is NP-hard. We present a solution method that first translates an attack graph into an MDP and solves it using policy search with a set of pruning techniques. We present an empirical evaluation of the model and solution algorithms, evaluating scalability, the types of solutions that are generated for realistic cases, and sensitivity analysis.

Recent work has shown that the quality of work produced in a crowdsourcing working session can be influenced by the presence of performance-contingent financial incentives, such as bonuses for exceptional performance, in the session. We take an algorithmic approach to decide when to offer bonuses in a working session to improve the overall utility that a requester derives from the session. Specifically, we propose and train an input-output hidden Markov model to learn the impact of bonuses on work quality and then use this model to dynamically decide whether to offer a bonus on each task in a working session to maximize a requester’s utility. Experiments on Amazon Mechanical Turk show that our approach leads to higher utility for the requester than fixed and random bonus schemes do. Simulations on synthesized data sets further demonstrate the robustness of our approach against different worker population and worker behavior in improving requester utility.

The intractability in cooperative, decentralized control models is mainly due to prohibitive memory requirements in both optimal policies and value functions. The complexity analysis has emerged as the standard method to estimating the memory needed for solving a given computational problem, but complexity results may be somewhat limited. This paper introduces a general methodology — structural analysis — for the design of optimality-preserving concise policies and value functions, which will eventually lead to the development of efficient theory and algorithms. For the first time, we show that memory requirements for policies and value functions may be asymmetric, resulting in cooperative, decentralized control models with exponential reductions in memory requirements.

We introduce and demonstrate a new approach to inference in expressive probabilistic programming languages based on particle Markov chain Monte Carlo. Our approach is simple to implement and easy to parallelize. It applies to Turing-complete probabilistic programming languages and supports accurate inference in models that make use of complex control flow, including stochastic recursion. It also includes primitives from Bayesian nonparametric statistics. Our experiments show that this approach can be more efficient than previously introduced single-site Metropolis-Hastings methods.

The Boltzmann machine provides a useful framework to learn highly complex, multimodal and multiscale data distributions that occur in the real world. The default method to learn its parameters consists of minimizing the Kullback-Leibler (KL) divergence from training samples to the Boltzmann model. We propose in this work a novel approach for Boltzmann training which assumes that a meaningful metric between observations is given. This metric can be represented by the Wasserstein distance between distributions, for which we derive a gradient with respect to the model parameters. Minimization of this new Wasserstein objective leads to generative models that are better when considering the metric and that have a cluster-like structure. We demonstrate the practical potential of these models for data completion and denoising, for which the metric between observations plays a crucial role.