Costly preference elicitation has also been studied The last two decades have seen a growing interest in the setting of auctions; notably by Conen and Sandholm in the development of automated agents that are [2001] and Parkes [2005]. These works are primarily able to negotiate on the user's behalf. When representing aimed at designing mechanisms that can avoid unnecessary a user in a negotiation, it is essential for the elicitation. Costly preference elicitation may alternatively be agent to understand the user's preferences, without cast as a problem in which agents have to allocate costly computational exposing them to elicitation fatigue. To this end, we resources to compute their valuation [Larson and propose a new model in which a negotiating agent Sandholm, 2001], but this work focuses on interactions between may incrementally elicit the user's preference during different strategies.

Craig Knoblock (University of Southern California, USA) Hiroaki Kitano (Sony Computer Science Laboratories, Inc., Japan) Sebastian run (Stanford University, USA) Raj Reddy (Carnegie Mellon University, USA) Ramasamy Uthurusamy (General Motors Corporation, retired) Erik Sandewall (Linköping Universit...

List of committee members on the program committees of the IJCAI 2015 conference.

List of chairs of the 2015 IJCAI conference programs.

This is an exciting time to be an artificial intelligence researcher. AI technologies and applications have truly entered our everyday lives, with AI systems in use throughout society. Against this backdrop of AI’s remarkable success, the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI-2015), to be held in Buenos Aires, Argentina between 25 and 31 July 2015, is poised to break several records. This is the first time the flagship international AI conference has been held in South America, and the number of submissions to the technical program has reached an historical high. These proceedings collect some of the most exciting research taking place in AI today and offer a window into the future. The theme of this year’s conference is Artificial Intelligence and Arts. Being held in Argentina, the home of Tango, the conference will feature invited talks, performances, demos and a technical track dedicated to the exploration and celebration of AI’s growing role in the Arts, both in enriching and producing Arts and in injecting art into AI to make it an elegant and more accessible scientific discipline.

We study the problem of structured output learning from a regression perspective. We first provide a general formulation of the kernel dependency estimation (KDE) approach to this problem using operator-valued kernels. Our formulation overcomes the two main limitations of the original KDE approach, namely the decoupling between outputs in the image space and the inability to use a joint feature space. We then propose a covariance-based operator-valued kernel that allows us to take into account the structure of the kernel feature space. This kernel operates on the output space and only encodes the interactions between the outputs without any reference to the input space. To address this issue, we introduce a variant of our KDE method based on the conditional covariance operator that in addition to the correlation between the outputs takes into account the effects of the input variables. Finally, we evaluate the performance of our KDE approach on three structured output problems, and compare it to the state-of-the-art kernelbased structured output regression methods.

What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, gender, religious practice) and an explicit description of the process. When the process is implemented using computers, determining disparate impact (and hence bias) is harder. It might not be possible to disclose the process. In addition, even if the process is open, it might be hard to elucidate in a legal setting how the algorithm makes its decisions. Instead of requiring access to the algorithm, we propose making inferences based on the data the algorithm uses. We make four contributions to this problem. First, we link the legal notion of disparate impact to a measure of classification accuracy that while known, has received relatively little attention. Second, we propose a test for disparate impact based on analyzing the information leakage of the protected class from the other data attributes. Third, we describe methods by which data might be made unbiased. Finally, we present empirical evidence supporting the effectiveness of our test for disparate impact and our approach for both masking bias and preserving relevant information in the data. Interestingly, our approach resembles some actual selection practices that have recently received legal scrutiny.

Abstract--Subspace models play an important role in a wide range of signal processing tasks, and this paper explores how the pairwise geometry of subspaces influences the probability of misclassification. When the mismatch between the signal and the model is vanishingly small, the probability of misclassification is determined by the product of the sines of the principal angles between subspaces. When the mismatch is more significant, the probability of misclassification is determined by the sum of the squares of the sines of the principal angles. Reliability of classification is derived in terms of the distribution of signal energy across principal vectors. Larger principal angles lead to smaller classification error, motivating a linear transform that optimizes principal angles. The transform presented here (TRAIT) preserves some specific characteristic of each individual class, and this approach is shown to be complementary to a previously developed transform (LRT) that enlarges inter-class distance while suppressing intra-class dispersion. Theoretical results are supported by demonstration of superior classification accuracy on synthetic and measured data even in the presence of significant model mismatch. IGNALS that are nominally high dimensional often exhibit a low dimensional geometric structure. For example, fixed-pose images of human faces are recorded using more than 1000 pixels, but can be represented by a 9-dimensional harmonic subspace [1]. Motion trajectories of a rigid body might be recorded by hundreds of sensors, but must intrinsically be represented by a 4-dimensional subspace [2]. There are many more examples where a low-dimensional subspace model captures intrinsic geometric structure, ranging from user ratings in a recommendation system [3] to signals emitted by multiple sources impinging at an antenna array [4]. The subspace geometry has assisted tasks of interest to both signal processing [5], [6] and machine learning communities [7], [8]. It can be used to approximate a nonlinear manifold by fitting mixture components to local patches of the manifold [5], [9], hence providing a high fidelity representation of a wide variety of signal geometries.

Active learning aims to obtain a classifier of high accuracy by using fewer label requests in comparison to passive learning by selecting effective queries. Many active learning methods have been developed in the past two decades, which sample queries based on informativeness or representativeness of unlabeled data points. In this work, we explore a novel querying criterion based on statistical leverage scores. The statistical leverage scores of a row in a matrix are the squared row-norms of the matrix containing its (top) left singular vectors and is a measure of influence of the row on the matrix. Leverage scores have been used for detecting high influential points in regression diagnostics (Chatterjee & Hadi, 1986) and have been recently shown to be useful for data analysis (Drineas et al., 2008) and randomized low-rank matrix approximation algorithms (Gittens & Mahoney, 2013). We explore how sampling data instances with high statistical leverage scores perform in active learning. Our empirical comparison on several binary classification datasets indicate that querying high leverage points is an effective strategy.

Gaussian processes (GP) are powerful tools for probabilistic modeling purposes. They can be used to define prior distributions over latent functions in hierarchical Bayesian models. The prior over functions is defined implicitly by the mean and covariance function, which determine the smoothness and variability of the function. The inference can then be conducted directly in the function space by evaluating or approximating the posterior process. Despite their attractive theoretical properties GPs provide practical challenges in their implementation. GPstuff is a versatile collection of computational tools for GP models compatible with Linux and Windows MATLAB and Octave. It includes, among others, various inference methods, sparse approximations and tools for model assessment. In this work, we review these tools and demonstrate the use of GPstuff in several models.