$k$ Nearest Neighbors ($k$NN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, especially when a very limited amount of labeled samples are available. In this paper, we propose a new graph-based $k$NN algorithm which can effectively handle both Gaussian distributed data and nonlinear manifold distributed data. To achieve this goal, we first propose a constrained Tired Random Walk (TRW) by constructing an $R$-level nearest-neighbor strengthened tree over the graph, and then compute a TRW matrix for similarity measurement purposes. After this, the nearest neighbors are identified according to the TRW matrix and the class label of a query point is determined by the sum of all the TRW weights of its nearest neighbors. To deal with online situations, we also propose a new algorithm to handle sequential samples based a local neighborhood reconstruction. Comparison experiments are conducted on both synthetic data sets and real-world data sets to demonstrate the validity of the proposed new $k$NN algorithm and its improvements to other version of $k$NN algorithms. Given the widespread appearance of manifold structures in real-world problems and the popularity of the traditional $k$NN algorithm, the proposed manifold version $k$NN shows promising potential for classifying manifold-distributed data.

Traditional disease surveillance can be augmented with a wide variety of real-time sources such as, news and social media. However, these sources are in general unstructured and, construction of surveillance tools such as taxonomical correlations and trace mapping involves considerable human supervision. In this paper, we motivate a disease vocabulary driven word2vec model (Dis2Vec) to model diseases and constituent attributes as word embeddings from the HealthMap news corpus. We use these word embeddings to automatically create disease taxonomies and evaluate our model against corresponding human annotated taxonomies. We compare our model accuracies against several state-of-the art word2vec methods. Our results demonstrate that Dis2Vec outperforms traditional distributed vector representations in its ability to faithfully capture taxonomical attributes across different class of diseases such as endemic, emerging and rare.

We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries. G-CNNs use G-convolutions, a new type of layer that enjoys a substantially higher degree of weight sharing than regular convolution layers. G-convolutions increase the expressive capacity of the network without increasing the number of parameters. Group convolution layers are easy to use and can be implemented with negligible computational overhead for discrete groups generated by translations, reflections and rotations. G-CNNs achieve state of the art results on CIFAR10 and rotated MNIST.

The problem of cluster recovery under the stochastic block model has been intensely studied in statistics [24, 44, 6, 8, 47, 19], computer science (where it is known as the planted partition problem) [17, 25, 13, 31, 11, 12, 9, 4, 10], and theoretical statistical physics [14, 48, 15]. In the simplest binary form, the stochastic block model assumes that n vertices are partitioned into two clusters with edge probability a/n within the first cluster, c/n within the second cluster, and b/n across the two clusters. The goal is to reconstruct the underlying clusters from the observation of the graph. Different reconstruction goals can be considered depending on how the model parameters a, b, c scale with n (See [2] for more discussions): - Exact recovery (strong consistency). If the average degree is Ω(log n), it is possible to exactly recover the clusters (up to a permutation of cluster indices) with high probability.

The clustering ensemble technique aims to combine multiple clusterings into a probably better and more robust clustering and has been receiving an increasing attention in recent years. There are mainly two aspects of limitations in the existing clustering ensemble approaches. Firstly, many approaches lack the ability to weight the base clusterings without access to the original data and can be affected significantly by the low-quality, or even ill clusterings. Secondly, they generally focus on the instance level or cluster level in the ensemble system and fail to integrate multi-granularity cues into a unified model. To address these two limitations, this paper proposes to solve the clustering ensemble problem via crowd agreement estimation and multigranularity link analysis. We present the normalized crowd agreement index (NCAI) to evaluate the quality of base clusterings in an unsupervised manner and thus weight the base clusterings in accordance with their clustering validity. To explore the relationship between clusters, the source aware connected triple (SACT) similarity is introduced with regard to their common neighbors and the source reliability. Present address: School of Information Science and Technology, Sun Yat-sen University, Guangzhou Higher Education Mega Center, Panyu District, Guangzhou, Guangdong, 510006, P. R. China. The experiments are conducted on eight real-world datasets. The experimental results demonstrate the effectiveness and robustness of the proposed methods. Keywords: Clustering ensemble, Clustering aggregation, Weighted evidence accumulation clustering, Graph partitioning with multi-granularity link analysis 1. Introduction Data clustering is a fundamental and very challenging problem in data mining and machine learning.

When random forests are used for binary classification, an ensemble of $t=1,2,\dots$ randomized classifiers is generated, and the predictions of the classifiers are aggregated by majority vote. Due to the randomness in the algorithm, there is a natural tradeoff between statistical performance and computational cost. On one hand, as $t$ increases, the (random) prediction error of the ensemble tends to decrease and stabilize. On the other hand, larger ensembles require greater computational cost for training and making new predictions. The present work offers a new approach for quantifying this tradeoff: Given a fixed training set $\mathcal{D}$, let the random variables $\text{Err}_{t,0}$ and $\text{Err}_{t,1}$ denote the class-wise prediction error rates of a randomly generated ensemble of size $t$. As $t\to\infty$, we provide a general bound on the "algorithmic variance", $\text{var}(\text{Err}_{t,l}|\mathcal{D})\leq \frac{f_l(1/2)^2}{4t}+o(\frac{1}{t})$, where $l\in\{0,1\}$, and $f_l$ is a density function that arises from the ensemble method. Conceptually, this result is somewhat surprising, because $\text{var}(\text{Err}_{t,l}|\mathcal{D})$ describes how $\text{Err}_{t,l}$ varies over repeated runs of the algorithm, and yet, the formula leads to a method for bounding $\text{var}(\text{Err}_{t,l}|\mathcal{D})$ with a single ensemble. The bound is also sharp in the sense that it is attained by an explicit family of randomized classifiers. With regard to the task of estimating $f_l(1/2)$, the presence of the ensemble leads to a unique twist on the classical setup of non-parametric density estimation --- wherein the effects of sample size and computational cost are intertwined. In particular, we propose an estimator for $f_l(1/2)$, and derive an upper bound on its MSE that matches "standard optimal non-parametric rates" when $t$ is sufficiently large.

Driving styles have a great influence on vehicle fuel economy, active safety, and drivability. To recognize driving styles of path-tracking behaviors for different divers, a statistical pattern-recognition method is developed to deal with the uncertainty of driving styles or characteristics based on probability density estimation. First, to describe driver path-tracking styles, vehicle speed and throttle opening are selected as the discriminative parameters, and a conditional kernel density function of vehicle speed and throttle opening is built, respectively, to describe the uncertainty and probability of two representative driving styles, e.g., aggressive and normal. Meanwhile, a posterior probability of each element in feature vector is obtained using full Bayesian theory. Second, a Euclidean distance method is involved to decide to which class the driver should be subject instead of calculating the complex covariance between every two elements of feature vectors. By comparing the Euclidean distance between every elements in feature vector, driving styles are classified into seven levels ranging from low normal to high aggressive. Subsequently, to show benefits of the proposed pattern-recognition method, a cross-validated method is used, compared with a fuzzy logic-based pattern-recognition method. The experiment results show that the proposed statistical pattern-recognition method for driving styles based on kernel density estimation is more efficient and stable than the fuzzy logic-based method.

We discuss a variant of Thompson sampling for nonparametric reinforcement learning in a countable classes of general stochastic environments. These environments can be non-Markov, non-ergodic, and partially observable. We show that Thompson sampling learns the environment class in the sense that (1) asymptotically its value converges to the optimal value in mean and (2) given a recoverability assumption regret is sublinear.

We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparameterizing the weights in this way we improve the conditioning of the optimization problem and we speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise-sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed-up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time. We demonstrate the usefulness of our method on applications in supervised image recognition, generative modelling, and deep reinforcement learning.

Tribune Publishing, the parent company that owns several storied and proud newspapers in the US including the Chicago Tribune and the Los Angeles Times, announced on Thursday that it would be changing its name to "tronc Inc." In a press release, the company said that tronc Inc would be "a content curation and monetization company focused on creating and distributing premium, verified content across all channels". The name, according to the release, is a shortening of Tribune Online Content. "tronc pools the company's leading media brands and leverages innovative technology to deliver personalized and interactive experiences to its 60m monthly users," the release continued, using the lower-case t despite the word coming at the beginning of the sentence. The release also announced the launch of "troncX", an "online curation and monetization engine" which utilizes artificial intelligence technology "to accelerate digital growth".