This paper presents an extension of the cascading Indian buffet process (CIBP) intended to learning arbitrary directed acyclic graph structures as opposed to the CIBP, which is limited to purely layered structures. The extended cascading Indian buffet process (eCIBP) essentially consists in adding an extra sampling step to the CIBP to generate connections between non-consecutive layers. In the context of graphical model structure learning, the proposed approach allows learning structures having an unbounded number of hidden random variables and automatically selecting the model complexity. We evaluated the extended process on multivariate density estimation and structure identification tasks by measuring the structure complexity and predictive performance. The results suggest the extension leads to extracting simpler graphs without scarifying predictive precision.

Qualitative Possibilistic Mixed-Observable MDPs (pi-MOMDPs), generalizing pi-MDPs and pi-POMDPs, are well-suited models to planning under uncertainty with mixed-observability when transition, observation and reward functions are not precisely known and can be qualitatively described. Functions defining the model as well as intermediate calculations are valued in a finite possibilistic scale L, which induces a finite belief state space under partial observability contrary to its probabilistic counterpart. In this paper, we propose the first study of factored pi-MOMDP models in order to solve large structured planning problems under qualitative uncertainty, or considered as qualitative approximations of probabilistic problems. Building upon the SPUDD algorithm for solving factored (probabilistic) MDPs, we conceived a symbolic algorithm named PPUDD for solving factored pi-MOMDPs. Whereas SPUDD's decision diagrams' leaves may be as large as the state space since their values are real numbers aggregated through additions and multiplications, PPUDD's ones always remain in the finite scale L via min and max operations only. Our experiments show that PPUDD's computation time is much lower than SPUDD, Symbolic-HSVI and APPL for possibilistic and probabilistic versions of the same benchmarks under either total or mixed observability, while still providing high-quality policies.

In the field of NLP, most of the existing domain adaptation studies belong to the feature-based adaptation, while the research of instance-based adaptation is very scarce. In this work, we propose a new instance-based adaptation model, called in-target-domain logistic approximation (ILA). In ILA, we adapt the source-domain data to the target domain by a logistic approximation. The normalized in-target-domain probability is assigned as an instance weight to each of the source-domain training data. An instance-weighted classification model is trained finally for the cross-domain classification problem. Compared to the previous techniques, ILA conducts instance adaptation in a dimensionality-reduced linear feature space to ensure efficiency in high-dimensional NLP tasks. The instance weights in ILA are learnt by leveraging the criteria of both maximum likelihood and minimum statistical distance. The empirical results on two NLP tasks including text categorization and sentiment classification show that our ILA model beats the state-of-the-art instance adaptation methods significantly, in cross-domain classification accuracy, parameter stability and computational efficiency.

In this paper we develop an algorithm to find the k-best equivalence classes of Bayesian networks. Our algorithm is capable of finding much more best DAGs than the previous algorithm that directly finds the k-best DAGs (Tian, He and Ram 2010). We demonstrate our algorithm in the task of Bayesian model averaging. Empirical results show that our algorithm significantly outperforms the k-best DAG algorithm in both time and space to achieve the same quality of approximation. Our algorithm goes beyond the maximum-a-posteriori (MAP) model by listing the most likely network structures and their relative likelihood and therefore has important applications in causal structure discovery.

Collaborative filtering algorithms generally rely on the assumption that user preference patterns remain stationary. However, real-world relational data are seldom stationary. User preference patterns may change over time, giving rise to the requirement of designing collaborative filtering systems capable of detecting and adapting to preference pattern shifts. Motivated by this observation, in this paper we propose a dynamic Bayesian probabilistic matrix factorization model, designed for modeling time-varying distributions. Formulation of our model is based on imposition of a dynamic hierarchical Dirichlet process (dHDP) prior over the space of probabilistic matrix factorization models to capture the time-evolving statistical properties of modeled sequential relational datasets. We develop a simple Markov Chain Monte Carlo sampler to perform inference. We present experimental results to demonstrate the superiority of our temporal model.

Indeed, to the best of our knowledge, we lack forgotten over time (Wu and Humberman 2007). For example, a probabilistic framework to model and predict the popularity videos on YouTube or stories on Digg gain their popularity dynamics of individual items. The reason behind this is by striving for views or votes (Szabo and Huberman partly illustrated in Figure 1, suggesting that the dynamical 2010); papers increase their visibility by competing for citations processes governing individual items appear too noisy to be from new papers (Ren et al. 2010; Wang, Song, and amenable to quantification. Barabási 2013); tweets or Hashtags in Twitter become more In this paper, we model the stochastic popularity dynamics popular as being retweeted (Hong, Dan, and Davison 2011) using reinforced Poisson processes, capturing simultaneously and so do webpages as being attached by incoming hyperlinks three key ingredients: fitness of an item, characterizing (Ratkiewicz et al. 2010). An ability to predict the popularity its inherent competitiveness against other items; a general of individual items within a dynamically evolving system temporal relaxation function, corresponding to the aging not only probes our understanding of complex systems, in the ability to attract new attentions; and a reinforcement but also has important implications in a wide range of domains, mechanism, documenting the well-known "rich-get-richer" from marketing and traffic control to policy making phenomenon. The benefit of the proposed model is threefold: and risk management. Despite recent advances of empirical (1) It models the arrival process of individual attentions methods, we lack a general modeling framework to predict directly in contrast to relying on aggregated popularity the popularity of individual items within a complex evolving time series; (2) As a generative probabilistic model, it can be system.

Recent works have explored the use of brain signals to directly control virtual and robotic agents in sequential tasks. So far in such brain-computer interfaces (BCI), an explicit calibration phase was required to build a decoder that translates raw electroencephalography (EEG) signals from the brain of each user into meaningful instructions. This paper proposes a method that removes the calibration phase, and allows a user to control an agent to solve a sequential task. The proposed method assumes a distribution of possible tasks, and infers the interpretation of EEG signals and the task by selecting the hypothesis which best explains the history of interaction. We introduce a measure of uncertainty on the task and on the EEG signal interpretation to act as an exploratory bonus for a planning strategy. This speeds up learning by guiding the system to regions that better disambiguate among task hypotheses. We report experiments where four users use BCI to control an agent on a virtual world to reach a target without any previous calibration process.

Restricted Boltzmann machines (RBMs) are powerful machine learning models, but learning and some kinds of inference in the model require sampling-based approximations, which, in classical digital computers, are implemented using expensive MCMC. Physical computation offers the opportunity to reduce the costof sampling by building physical systems whose natural dynamics correspond to drawing samples from the desired RBM distribution. Such a system avoids the burn-in and mixing cost of a Markov chain. However, hardware implementations of this variety usually entail limitations such as low-precision and limited range of the parameters and restrictions on the size and topology of the RBM. We conduct software simulations to determine how harmful each of these restrictions is. Our simulations are based on the D-Wave Two computer, but the issues we investigate arise in most forms of physical computation.Our findings suggest that designers of new physical computing hardware and algorithms for physical computers should focus their efforts on overcoming the limitations imposed by the topology restrictions of currently existing physical computers.

The area of cognitive robotics is often subject to the criticism that the proposals investigated in the literature are too far removed from the kind of continuous uncertainty and noise seen in actual real-world robotics. This paper proposes a new language and an implemented system, called PREGO, based on the situation calculus, that is able to reason effectively about degrees of belief against noisy sensors and effectors in continuous domains. It embodies the representational richness of conventional logic-based action languages, such as context-sensitive successor state axioms, but is still shown to be efficient using a number of empirical evaluations. We believe that PREGO is a powerful framework for exploring real-time reactivity and an interesting bridge between logic and probability for cognitive robotics applications.

Selecting good conference keywords is important because they often determine the composition of review committees and hence which papers are reviewed by whom. But presently conference keywords are generated in an ad-hoc manner by a small set of conference organizers. This approach is plainly not ideal. There is no guarantee, for example, that the generated keyword set aligns with what the community is actually working on and submitting to the conference in a given year. This is especially true in fast moving fields such as AI. The problem is exacerbated by the tendency of organizers to draw heavily on preceding years' keyword lists when generating a new set. Rather than a select few ordaining a keyword set that that represents AI at large, it would be preferable to generate these keywords more directly from the data, with input from research community members. To this end, we solicited feedback from seven AAAI PC members regarding a previously existing keyword set and used these 'community-sourced constraints' to inform a clustering over the abstracts of all submissions to AAAI 2013. We show that the keywords discovered via this data-driven, human-in-the-loop method are at least as preferred (by AAAI PC members) as 2013's manually generated set, and that they include categories previously overlooked by organizers. Many of the discovered terms were used for this year's conference.