Conformant probabilistic planning (CPP) differs from conformant planning (CP) by two key elements: the initial belief state is probabilistic,and the conformant plan must achieve the goal with probability $\geq\theta$, for some $0<\theta\leq 1$. In earlier work we observed that one can reduce CPP to CP by finding a set of initial states whose probability $\geq\theta$, for whicha conformant plan exists. In previous solvers we used the underlying planner to select this set of states and to plan for them simultaneously. Here we suggest an alternative approach: start with relevance analysis to determine a promising set of initial states on which to focus. Then, call an off-the-shelf conformant planner to solve the resulting problem. This approach has a number of advantages. First, instead of depending on the heuristic function to select the set of initial states,we can introduce specific, efficient relevance reasoning techniques. Second, we can benefit from optimizations used by conformant planners that are unsound when applied to the original CPP. Finally, we are free to use any existing (or new) CP solver. Consequently, the new planner dominates previous solvers on almost all domains and scales to instances that were not solved before.

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.

Infinite SVM (iSVM) is a Dirichlet process (DP) mixture of large-margin classifiers. Though flexible in learning nonlinear classifiers and discovering latent clustering structures, iSVM has a difficult inference task and existing methods could hinder its applicability to large-scale problems. This paper presents a small-variance asymptotic analysis to derive a simple and efficient algorithm, which monotonically optimizes a max-margin DP-means (M2DPM) problem, an extension of DP-means for both predictive learning and descriptive clustering. Our analysis is built on Gibbs infinite SVMs, an alternative DP mixture of large-margin machines, which admits a partially collapsed Gibbs sampler without truncation by exploring data augmentation techniques. Experimental results show that M2DPM runs much faster than similar algorithms without sacrificing prediction accuracies.

Transfer learning considers related but distinct tasks defined on heterogenous domains and tries to transfer knowledge between these tasks to improve generalization performance. It is particularly useful when we do not have sufficient amount of labeled training data in some tasks, which may be very costly, laborious, or even infeasible to obtain. Instead, learning the tasks jointly enables us to effectively increase the amount of labeled training data. In this paper, we formulate a kernelized Bayesian transfer learning framework that is a principled combination of kernel-based dimensionality reduction models with task-specific projection matrices to find a shared subspace and a coupled classification model for all of the tasks in this subspace. Our two main contributions are: (i) two novel probabilistic models for binary and multiclass classification, and (ii) very efficient variational approximation procedures for these models. We illustrate the generalization performance of our algorithms on two different applications. In computer vision experiments, our method outperforms the state-of-the-art algorithms on nine out of 12 benchmark supervised domain adaptation experiments defined on two object recognition data sets. In cancer biology experiments, we use our algorithm to predict mutation status of important cancer genes from gene expression profiles using two distinct cancer populations, namely, patient-derived primary tumor data and in-vitro-derived cancer cell line data. We show that we can increase our generalization performance on primary tumors using cell lines as an auxiliary data source.

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.

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.

Many real-world applications involve multilabel classification, in which the labels can have strong inter-dependencies and some of them may even be missing.Existing multilabel algorithms are unable to handle both issues simultaneously.In this paper, we propose a probabilistic model that can automatically learn and exploit multilabel correlations.By integrating out the missing information, it also provides a disciplinedapproach to the handling of missing labels. The inference procedure is simple, and the optimization subproblems are convex. Experiments on a number of real-world data sets with both complete and missing labelsdemonstrate that the proposed algorithm can consistently outperform state-of-the-art multilabel classification algorithms.

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.

Many distance learning algorithms have been developed in recent years. However, few of them consider the problem when the class labels of training data are noisy, and this may lead to serious performance deterioration. In this paper, we present a robust distance learning method in the presence of label noise, by extending a previous non-parametric discriminative distance learning algorithm, i.e., Neighbourhood Components Analysis (NCA). Particularly, we analyze the effect of label noise on the derivative of likelihood with respect to the transformation matrix, and propose to model the conditional probability of the true label of each point so as to reduce that effect. The model is then optimized within the EM framework, with additional regularization used to avoid overfitting. Our experiments on several UCI datasets and a real dataset with unknown noise patterns show that the proposed RNCA is more tolerant to class label noise compared to the original NCA method.

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.