Inductive Learning Algorithms for Complex System Modeling

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Unlike other approaches, MI-CRF considers all bags jointly during training as well as during testing. This makes it possible to classify test bags in an imputation setup. Prediction markets are used in real life to predict outcomes of interest such as presidential elections. In this work we introduce a mathematical theory for Artificial Prediction Markets for supervised classifier aggregation and probability estimation. We introduce the artificial prediction market as a novel way to aggregate classifiers. We derive the market equations to enforce total budget conservation, show the market price uniqueness and give efficient algorithms for computing it.

We show how to train the market participants by updating their budgets using training examples. We introduce classifier specialization as a new differentiating characteristic between classifiers. Finally, we present experiments using random decision rules as specialized classifiers and show that the prediction market consistently outperforms Random Forest on real and synthetic data of varying degrees of difficulty.

We consider the fully automated recognition of actions in uncontrolled environment. Most existing work relies on domain knowledge to construct complex handcrafted features from inputs. In addition, the environments are usually assumed to be controlled. Convolutional neural networks CNNs are a type of deep models that can act directly on the raw inputs, thus automating the process of feature construction. However, such models are currently limited to handle 2D inputs. In this paper, we develop a novel 3D CNN model for action recognition. This model extracts features from both spatial and temporal dimensions by performing 3D convolutions, thereby capturing the motion information encoded in multiple adjacent frames.

The developed model generates multiple channels of information from the input frames, and the final feature representation is obtained by combining information from all channels. We apply the developed model to recognize human actions in real-world environment, and it achieves superior performance without relying on handcrafted features.

Semi-supervised learning has emerged as a popular framework for improving modeling accuracy while controlling labeling cost. Based on an extension of stochastic composite likelihood we quantify the asymptotic accuracy of generative semi-supervised learning. In doing so, we complement distribution-free analysis by providing an alternative framework to measure the value associated with different labeling policies and resolve the fundamental question of how much data to label and in what manner. We establish the intractability of two basiccomputational tasks involving RBMs, even if only a coarseapproximation to the correct output is required.

We first show that assuming P!

Deep Learning on Steroids with the Power of Knowledge Transfer!

We then show that assuming RP! We consider the problem of learning from noisy side information in the form of pairwise constraints.

Although many algorithms have been developed to learn from side information, most of them assume perfect pairwise constraints. Given the pairwise constraints are often extracted from data sources such as paper citations, they tend to be noisy and inaccurate.

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In this paper, we introduce the generalization of maximum entropy model and propose a framework for learning from noisy side information based on the generalized maximum entropy model. The theoretic analysis shows that under certain assumption, the classification model trained from the noisy side information can be very close to the one trained from the perfect side information.

Extensive empirical studies verify the effectiveness of the proposed framework. We describe an algorithm for clustering using a similarity graph. We describe some experimentsrunning the algorithm and a few related algorithms on random graphswith partitions generated using a Chinese Restaurant Processes, andsome results of applying the algorithm to cluster DBLP titles. We propose a new dimensionality reduction method, the elastic embedding EE , that optimises an intuitive, nonlinear objective function of the low-dimensional coordinates of the data.

The method reveals a fundamental relation betwen a spectral method, Laplacian eigenmaps, and a nonlinear method, stochastic neighbour embedding; and shows that EE can be seen as learning both the coordinates and the affinities between data points. We give a homotopy method to train EE, characterise the critical value of the homotopy parameter, and study the method's behaviour.

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For a fixed homotopy parameter, we give a globally convergent iterative algorithm that is very effective and requires no user parameters. Finally, we give an extension to out-of-sample points. In standard datasets, EE obtains results as good or better than those of SNE, but more efficiently and robustly. This paper examines two-stage techniques for learning kernels based on a notion of alignment. It presents a number of novel theoretical, algorithmic, and empirical results for alignment-based techniques.

Our results build on previous work by Cristianini et al. In this paper, we study how to robustly computethe modes of a graph, namely the densesubgraphs, which characterize the underlyingcompact patterns and are thus useful formany applications. We first define the modesbased on graph density function, then proposethe graph shift algorithm, which startsfrom each vertex and iteratively shifts towardsthe nearest mode of the graph alonga certain trajectory. Both theoretic analysisand experiments show that graph shift algorithmis very efficient and robust, especiallywhen there exist large amount of noises andoutliers.

Harmonic analysis, and in particular the relation between function smoothnessand approximate sparsity of its wavelet coefficients, has played a key role insignal processing and statistical inference for low dimensional data. In contrast, harmonic analysis has thus far had little impact in modern problemsinvolving high dimensional data, or data encoded as graphs or networks. The main contribution of this paper is the development of a harmonic analysisapproach, including both learning algorithms and supporting theory, applicable to these moregeneral settings.

Given data be it high dimensional, graph or network that is representedby one or more hierarchical trees, we first construct multiscale wavelet-likeorthonormal baseson it.

Second, we prove that in analogyto the Euclidean case, function smoothness with respectto a specific metric induced by the tree is equivalent to exponential rate of coefficient decay,that is, to approximate sparsity. These results readily translate to simple practicalalgorithms for various learning tasks. We present an application to transductive semi-supervised learning.

Deep learning has been successfully applied to learn non-linear feature mappings and to perform dimensionality reduction. In this paper, we present supervised embedding techniques that use a deep neural network to collapse classes. The network is pre-trained using a stack of Restricted Boltzmann Machines RBMs , and finetuned using approaches that try to collapse classes.


The finetuning is inspired by ideas from Neighborhood Components Analysis NCA , but it uses a Student t-distribution to model the probabilities of pairwise data points belonging to the same class in the embedding. Our experiments on two handwritten digit datasets reveal the strong performance of dt-MCML in supervised parametric data visualization, whereas dt-NCA outperforms alternative techniques when embeddings with more than two or three dimensions are constructed, e.

Overall, our results demonstrate the advantage of using a deep architecture and a heavy-tailed t-distribution for measuring pairwise similarities in supervised embedding. In this paper we propose a novel clustering algorithm based on maximizing the mutual information between data points and clusters. Unlike previous methods, we neitherassume the data are given in terms of distributions nor impose any parametric model on the within-cluster distribution.

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Instead, we utilize a non-parametric estimation of the average cluster entropies and search for a clustering that maximizes the estimated mutual information between data points and clusters. The improved performance of the proposed algorithm is demonstrated on several standard datasets. We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points.

The model can be used to locate change points in an online manner;and, unlike other Bayesian online change point detection algorithms, is applicable when temporal correlations in a regime are expected. We show three variations on how to apply Gaussian processes in the change point context, each with their own advantages. We present methods to reduce the computational burden of these models and demonstrate it on several real world data sets.

Yutian Chen Univ. We show that our multivariate volatility models significantly outperform all related Garch and stochastic volatility models which are in popular use in the quantitative finance community. The resulting incremental classification algorithm, called Margin Perceptron with Unlearning MPU , provably converges in a finite number of updates to any desirable chosen before running approximation of either the maximal margin or the optimal 1-norm soft margin solution.

Moreover, an experimental comparative evaluation involving representative linear Support Vector Machines reveals that the MPU algorithm is very competitive. Hashing based Approximate Nearest Neighbor ANN search has attracted much attention due to its fast query time and drastically reduced storage. However, most of the hashing methods either use random projections or extract principal directions from the data to derive hash functions.

The resulting embedding suffers from poor discrimination when compact codes are used. In this paper, we propose a novel data-dependent projection learning method such that each hash function is designed to correct the errors made by the previous one sequentially. The proposed method easily adapts to both unsupervised and semi-supervised scenarios and shows significant performance gains over the state-of-the-art methods on two large datasets containing up to 1 million points.

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This paper presents several novel generalization bounds for the problem of learning kernels based on a combinatorial analysis of the Rademacher complexity of the corresponding hypothesis sets. Experiments with a large number of kernels further validate the behavior of the generalization error as a function of p predicted by our bounds.

Supervised learning methods in modeling of CD4+ T cell heterogeneity

Transfer learning can be described as the distillation of abstract knowledge from one learning domain or task and the reuse of that knowledge in a related domain or task. In categorization settings, transfer learning is the modification by past experience of prior expectations about what types of categories are likely to exist in the world. While transfer learning is an important and active research topic in machine learning, there have been few studies of transfer learning in human categorization. We propose an explanation for transfer learning effects in human categorization, implementing a model from the statistical machine learning literature -- the hierarchical Dirichlet process HDP -- to make empirical evaluations of its ability to explain these effects.

We present two laboratory experiments which measure the degree to which people engage in transfer learning in a controlled setting, and we compare our model to their performance. We find that the HDP provides a good explanation for transfer learning exhibited by human learners.