This paper considers the multi-task learning problem and in the setting where some relevant features could be shared across few related tasks. Most of the existing methods assume the extent to which the given tasks are related or share a common feature space to be known apriori. In real-world applications however, it is desirable to automatically discover the groups of related tasks that share a feature space. In this paper we aim at searching the exponentially large space of all possible groups of tasks that may share a feature space. The main contribution is a convex formulation that employs a graph-based regularizer and simultaneously discovers few groups of related tasks, having close-by task parameters, as well as the feature space shared within each group. The regularizer encodes an important structure among the groups of tasks leading to an efficient algorithm for solving it: if there is no feature space under which a group of tasks has close-by task parameters, then there does not exist such a feature space for any of its supersets. An efficient active set algorithm that exploits this simplification and performs a clever search in the exponentially large space is presented. The algorithm is guaranteed to solve the proposed formulation (within some precision) in a time polynomial in the number of groups of related tasks discovered. Empirical results on benchmark datasets show that the proposed formulation achieves good generalization and outperforms state-of-the-art multi-task learning algorithms in some cases.

We present a hybrid algorithm for optimizing a convex, smooth function over the cone of positive semidefinite matrices. Our algorithm converges to the global optimal solution and can be used to solve general large-scale semidefinite programs and hence can be readily applied to a variety of machine learning problems. We show experimental results on three machine learning problems (matrix completion, metric learning, and sparse PCA) . Our approach outperforms state-of-the-art algorithms.

We present a framework for online inference in the presence of a nonexhaustively defined set of classes that incorporates supervised classification with class discovery and modeling. A Dirichlet process prior (DPP) model defined over class distributions ensures that both known and unknown class distributions originate according to a common base distribution. In an attempt to automatically discover potentially interesting class formations, the prior model is coupled with a suitably chosen data model, and sequential Monte Carlo sampling is used to perform online inference. Our research is driven by a biodetection application, where a new class of pathogen may suddenly appear, and the rapid increase in the number of samples originating from this class indicates the onset of an outbreak.

This paper revisits the problem of analyzing multiple ratings given by different judges. Different from previous work that focuses on distilling the true labels from noisy crowdsourcing ratings, we emphasize gaining diagnostic insights into our in-house well-trained judges. We generalize the well-known DawidSkene model (Dawid & Skene, 1979) to a spectrum of probabilistic models under the same "TrueLabel + Confusion" paradigm, and show that our proposed hierarchical Bayesian model, called HybridConfusion, consistently outperforms DawidSkene on both synthetic and real-world data sets.

Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society. From this perspective, there exist glaring limitations in the data sets we investigate, the metrics we employ for evaluation, and the degree to which results are communicated back to their originating domains. What changes are needed to how we conduct research to increase the impact that ML has? We present six Impact Challenges to explicitly focus the field's energy and attention, and we discuss existing obstacles that must be addressed. We aim to inspire ongoing discussion and focus on ML that matters.

From this excellent collection of papers, three for presentation at ICAPS 2012, the were selected for special recognition. ICAPS continues Nguyen, Vien Tran, Tran Cao Son and Enrico the traditional high standards of AIPS and ECP Pontelli were selected for Best Student Paper as an archival forum for new research in the Award. In addition to the oral presentation of these e 45 papers included in this volume, consisting papers, the technical program of this year's of 37 long papers and 8 short papers, are ICAPS conference includes invited talks by those selected for plenary presentation at three distinguished speakers: Robert O. Ambrose ICAPS 2012 from a total of 132 submissions. Topics under various constraints and assumptions, included real-time planning, planning in mixed to empirical evaluation of planning and discrete-continuous domains, planning for systems scheduling techniques in practical applications. Papers in the subareas of optimal planning, probabilistic were encouraged from a range of neighboring and non-deterministic planning, planning disciplines, including model-based and scheduling for transportation, robot path reasoning, hybrid systems, run-time verification, planning, and new developments in heuristics control and robotics.

Portfolio planners and parameter tuning are two ideas that have recently attracted significant attention in the domain-independent planning community. We combine these two ideas and present a portfolio planner that runs automatically configured planners. We let the automatic parameter tuning framework ParamILS find fast configurations of the Fast Downward planning system for a number of planning domains. Afterwards we learn a portfolio of those planner configurations. Evaluation of our portfolio planner on the IPC 2011 domains shows that it has a significantly higher IPC score than the winner of the sequential satisficing track.

This work describes an approach that automatically extracts standard metadata information from e-learning contents, combines it with the student preferences/goals and creates PDDL planning domains+problems.These PDDL problems can be solved by current planners, although we motivate the use and benefits of case-based planning techniques, to obtain fully tailored learning routes that significantly enhance the learning process. During the execution of a given route, a monitoring phase is used to detect discrepancies, i.e. flaws that prevent the student from continuing with the original plan. In such a situation, an adaptation mechanism becomes necessary to fix the flaws, while also trying to minimise the differences between the original and the new route. We have integrated this approach on top of Moodle and experimented with 100 benchmark problems to evaluate the quality, scalability and viability of the system.

We study the generalization performance of online learning algorithms trained on samples coming from a dependent source of data. We show that the generalization error of any stable online algorithm concentrates around its regret--an easily computable statistic of the online performance of the algorithm--when the underlying ergodic process is $\beta$- or $\phi$-mixing. We show high probability error bounds assuming the loss function is convex, and we also establish sharp convergence rates and deviation bounds for strongly convex losses and several linear prediction problems such as linear and logistic regression, least-squares SVM, and boosting on dependent data. In addition, our results have straightforward applications to stochastic optimization with dependent data, and our analysis requires only martingale convergence arguments; we need not rely on more powerful statistical tools such as empirical process theory.

Social causality is the inference an entity makes about the social behavior of other entities and self. Besides physical cause and effect, social causality involves reasoning about epistemic states of agents and coercive circumstances. Based on such inference, responsibility judgment is the process whereby one singles out individuals to assign responsibility, credit or blame for multi-agent activities. Social causality and responsibility judgment are a key aspect of social intelligence, and a model for them facilitates the design and development of a variety of multi-agent interactive systems. Based on psychological attribution theory, this paper presents a domain-independent computational model to automate social inference and judgment process according to an agents causal knowledge and observations of interaction. We conduct experimental studies to empirically validate the computational model. The experimental results show that our model predicts human judgments of social attributions and makes inferences consistent with what most people do in their judgments. Therefore, the proposed model can be generically incorporated into an intelligent system to augment its social and cognitive functionality.