Preference Learning

This report documents the program and the outcomes of Dagstuhl Seminar 14101 “Preference Learning”. Preferences have recently received considerable attention in disciplines such as machine learning, knowledge discovery, information retrieval, statistics, social choice theory, multiple criteria decision making, decision under risk and uncertainty, operations research, and others. The motivation for this seminar was to showcase recent progress in these different areas with the goal of working towards a common basis of understanding, which should help to facilitate future synergies

Machine Learning Methods for Activity Detection in Wearable Sensor Data Streams

Wearable wireless sensors have the potential for transformative impact on the fields of health and behavioral science. Recent advances in wearable sensor technology have made it possible to simultaneously collect multiple streams of physiological and context data from individuals in natural environments; however, extracting reliable high-level inferences from these raw data streams remains a key data analysis challenge. In this dissertation, we address three challenges that arise when trying to perform activity detection from wearable sensor streams. First, we address the challenge of learning from small amounts of noisy data by proposing a class of conditional random field models for activity detection. We apply this model class to three different activity detection problems, improving performance in all three when compared with standard independent and structured models. Second, we address the challenge of inferring activities from long input sequences by evaluating strategies for pruning the inference dynamic programs used in structured prediction models. We apply these strategies to the proposed structured activity detection models resulting in inference speedups ranging from 66x to 257x with little to no decrease in predictive performance. Finally, we address the challenge of learning from imprecise annotations by proposing a weak supervision framework for learning discrete-time detection models from imprecise continuous-time observations. We apply this framework to both independent and structured models and demonstrate improved performance over weak supervision baselines

Learning Discriminative Features and Structured Models for Segmentation in Microscopy and Natural Images

Segmenting images is a significant challenge that has drawn a lot of attention from different fields of artificial intelligence and has many practical applications. One such challenge addressed in this thesis is the segmentation of electron microscope (EM) imaging of neural tissue. EM microscopy is one of the key tools used to analyze neural tissue and understand the brain, but the huge amounts of data it produces make automated analysis necessary. In addition to the challenges specific to EM data, the common problems encountered in image segmentation must also be addressed. These problems include extracting discriminative features from the data and constructing a statistical model using ground-truth data. Although complex models appear to be more attractive because they allow for more expressiveness, they also lead to a higher computational complexity. On the other hand, simple models come with a lower complexity but less faithfully express the real world. Therefore, one of the most challenging tasks in image segmentation is in constructing models that are expressive enough while remaining tractable. In this work, we propose several automated graph partitioning approaches that address these issues. These methods reduce the computational complexity by operating on supervoxels instead of voxels, incorporating features capable of describing the 3D shape of the target objects and using structured models to account for correlation in output variables. One of the non-trivial issues with such models is that their parameters must be carefully chosen for optimal performance. A popular approach to learning model parameters is a maximum-margin approach called Structured SVM (SSVM) that provides optimality guarantees but also suffers from two main drawbacks. First, SSVM-based approaches are usually limited to linear kernels, since more powerful nonlinear kernels cause the learning to become prohibitively expensive. In this thesis, we introduce an approach to “kernelize” the features so that a linear SSVM framework can leverage the power of nonlinear kernels without incurring their high computational cost. Second, the optimality guarentees are violated for complex models with strong inter-relations between the output variables. We propose a new subgradient-based method that is more robust and leads to improved convergence properties and increased reliability. The different approaches presented in this thesis are applicable to both natural and medical images. They are able to segment mitochondria at a performance level close to that of a human annotator, and outperform state-of-the-art segmentation techniques while still benefiting from a low learning time

Learning Probabilistic Graphical Models for Image Segmentation

Probabilistic graphical models provide a powerful framework for representing image structures. Based on this concept, many inference and learning algorithms have been developed. However, both algorithm classes are NP-hard combinatorial problems in the general case. As a consequence, relaxation methods were developed to approximate the original problems but with the benefit of being computationally efficient. In this work we consider the learning problem on binary graphical models and their relaxations. Two novel methods for determining the model parameters in discrete energy functions from training data are proposed. Learning the model parameters overcomes the problem of heuristically determining them. Motivated by common learning methods which aim at minimizing the training error measured by a loss function we develop a new learning method similar in fashion to structured SVM. However, computationally more efficient. We term this method as linearized approach (LA) as it is restricted to linearly dependent potentials. The linearity of LA is crucial to come up with a tight convex relaxation, which allows to use off-the-shelf inference solvers to approach subproblems which emerge from solving the overall problem. However, this type of learning methods almost never yield optimal solutions or perfect performance on the training data set. So what happens if the learned graphical model on the training data would lead to exact ground segmentation? Will this give a benefit when predicting? Motivated by the idea of inverse optimization, we take advantage of inverse linear programming to develop a learning approach, referred to as inverse linear programming approach (invLPA). It further refines the graphical models trained, using the previously introduced methods and is capable to perfectly predict ground truth on training data. The empirical results from implementing invLPA give answers to our questions posed before. LA is able to learn both unary and pairwise potentials jointly while with invLPA this is not possible due to the representation we use. On the other hand, invLPA does not rely on a certain form for the potentials and thus is flexible in the choice of the fitting method. Although the corrected potentials with invLPA always result in ground truth segmentation of the training data, invLPA is able to find corrections on the foreground segments only. Due to the relaxed problem formulation this does not affect the final segmentation result. Moreover, as long as we initialize invLPA with model parameters of a learning method performing sufficiently well, this drawback of invLPA does not significantly affect the final prediction result. The performance of the proposed learning methods is evaluated on both synthetic and real world datasets. We demonstrate that LA is competitive in comparison to other parameter learning methods using loss functions based on Maximum a Posteriori Marginal (MPM) and Maximum Likelihood Estimation (MLE). Moreover, we illustrate the benefits of learning with inverse linear programming. In a further experiment we demonstrate the versatility of our learning methods by applying LA to learning motion segmentation in video sequences and comparing it to state-of-the-art segmentation algorithms