Recently, Liu et al. [6, 7] introduced the notion of a nonparanormal distribution, which generalizes Instead of only analyzing the standard covariance matrix, we show that it is often fruitful to augment the usual covariance matrix with higher-order interaction terms. Other related work on graphical model selection for discrete graphs includes the Classic Chow-Liu algorithm for trees [8]; nodewise logistic regression for discrete models with pairwise interactions [9, 10]; and techniques based on conditional entropy or mutual information [11, 12].

The primary application of collaborate filtering (CF) is to recommend a small set of items to a user, which entails ranking. Most approaches, however, formulate the CF problem as rating prediction, overlooking the ranking perspective. In this work we present a method for collaborative ranking that leverages the strengths of the two main CF approaches, neighborhood-and model-based. Our novel method is highly efficient, with only seventeen parameters to optimize and a single hyperparameter totune, and beats the state-of-the-art collaborative ranking methods. We also show that parameters learned on datasets from one item domain yield excellent resultson a dataset from very different item domain, without any retraining.

One of the main challenges in data clustering is to define an appropriate similarity measure between two objects. Crowdclustering addresses this challenge by defining the pairwise similarity based on the manual annotations obtained through crowdsourcing. Despite its encouraging results, a key limitation of crowdclustering is that it can only cluster objects when their manual annotations are available. To address this limitation, we propose a new approach for clustering, called \textit{semi-crowdsourced clustering} that effectively combines the low-level features of objects with the manual annotations of a subset of the objects obtained via crowdsourcing. The key idea is to learn an appropriate similarity measure, based on the low-level features of objects, from the manual annotations of only a small portion of the data to be clustered. One difficulty in learning the pairwise similarity measure is that there is a significant amount of noise and inter-worker variations in the manual annotations obtained via crowdsourcing. We address this difficulty by developing a metric learning algorithm based on the matrix completion method. Our empirical study with two real-world image data sets shows that the proposed algorithm outperforms state-of-the-art distance metric learning algorithms in both clustering accuracy and computational efficiency.

Label space dimension reduction (LSDR) is an efficient and effective paradigm for multi-label classification with many classes. Existing approaches to LSDR, such as compressive sensing and principal label space transformation, exploit only the label part of the dataset, but not the feature part. In this paper, we propose a novel approach to LSDR that considers both the label and the feature parts. The approach, called conditional principal label space transformation, is based on minimizing an upper bound of the popular Hamming loss. The minimization step of the approach can be carried out efficiently by a simple use of singular value decomposition. In addition, the approach can be extended to a kernelized version that allows the use of sophisticated feature combinations to assist LSDR. The experimental results verify that the proposed approach is more effective than existing ones to LSDR across many real-world datasets.

This paper proposes a novel image representation called a Graphical Gaussian Vector, which is a counterpart of the codebook and local feature matching approaches. In our method, we model the distribution of local features as a Gaussian Markov Random Field (GMRF) which can efficiently represent the spatial relationship among local features. We consider the parameter of GMRF as a feature vector of the image. Using concepts of information geometry, proper parameters and a metric from the GMRF can be obtained. Finally we define a new image feature by embedding the metric into the parameters, which can be directly applied to scalable linear classifiers. Our method obtains superior performance over the state-of-the-art methods in the standard object recognition datasets and comparable performance in the scene dataset. As the proposed method simply calculates the local auto-correlations of local features, it is able to achieve both high classification accuracy and high efficiency.

The minimax KL-divergence of any distribution from all distributions in a collection P has several practical implications. In compression, it is called redundancy and represents the least additional number of bits over the entropy needed to encode the output of any distribution in P. In online estimation andlearning, it is the lowest expected log-loss regret when guessing a sequence of random values generated by a distribution in P. In hypothesis testing, it upper bounds the largest number of distinguishable distributions in P. Motivated by problems ranging from population estimation to text classification and speech recognition, several machine-learning and information-theory researchers have recently considered label-invariant observations and properties induced by i.i.d.

Time delay is pervasive in neural information processing. To achieve real-time tracking, it is critical to compensate the transmission and processing delays in a neural system. In the present study we show that dynamical synapses with short-term depression can enhance the mobility of a continuous attractor network to the extent that the system tracks time-varying stimuli in a timely manner. The state of the network can either track the instantaneous position of a moving stimulus perfectly (with zero-lag) or lead it with an effectively constant time, in agreement with experiments on the head-direction systems in rodents. The parameter regions for delayed, perfect and anticipative tracking correspond to network states that are static, ready-to-move and spontaneously moving, respectively, demonstrating the strong correlation between tracking performance and the intrinsic dynamics of the network. We also find that when the speed of the stimulus coincides with the natural speed of the network state, the delay becomes effectively independent of the stimulus amplitude.

Active learning can substantially improve the yield of neurophysiology experiments by adaptively selecting stimuli to probe a neuron's receptive field (RF) in real time. Bayesian active learning methods maintain a posterior distribution over the RF, and select stimuli to maximally reduce posterior entropy on each time step. However, existing methods tend to rely on simple Gaussian priors, and do not exploit uncertainty at the level of hyperparameters when determining an optimal stimulus. This uncertainty can play a substantial role in RF characterization, particularly when RFs are smooth, sparse, or local in space and time. In this paper, we describe a novel framework for active learning under hierarchical, conditionally Gaussian priors. Our algorithm uses sequential Markov Chain Monte Carlo sampling (''particle filtering'' with MCMC) over hyperparameters to construct a mixture-of-Gaussians representation of the RF posterior, and selects optimal stimuli using an approximate infomax criterion. The core elements of this algorithm are parallelizable, making it computationally efficient for real-time experiments. We apply our algorithm to simulated and real neural data, and show that it can provide highly accurate receptive field estimates from very limited data, even with a small number of hyperparameter samples.

A new methodology is developed for joint analysis of a matrix and accompanying documents, with the documents associated with the matrix rows/columns. The documents are modeled with a focused topic model, inferring interpretable latent binary features for each document. A new matrix decomposition is developed, with latent binary features associated with the rows/columns, and with imposition of a low-rank constraint. The matrix decomposition and topic model are coupled by sharing the latent binary feature vectors associated with each. The model is applied to roll-call data, with the associated documents defined by the legislation. Advantages of the proposed model are demonstrated for prediction of votes on a new piece of legislation, based only on the observed text of legislation. The coupling of the text and legislation is also shown to yield insight into the properties of the matrix decomposition for roll-call data.

In this paper, we consider the $\ell_1$ regularized sparse inverse covariance matrix estimation problem with a very large number of variables. Even in the face of this high dimensionality, and with limited number of samples, recent work has shown this estimator to have strong statistical guarantees in recovering the true structure of the sparse inverse covariance matrix, or alternatively the underlying graph structure of the corresponding Gaussian Markov Random Field. Our proposed algorithm divides the problem into smaller sub-problems, and uses the solutions of the sub-problems to build a good approximation for the original problem. We derive a bound on the distance of the approximate solution to the true solution. Based on this bound, we propose a clustering algorithm that attempts to minimize this bound, and in practice, is able to find effective partitions of the variables. We further use the approximate solution, i.e., solution resulting from solving the sub-problems, as an initial point to solve the original problem, and achieve a much faster computational procedure. As an example, a recent state-of-the-art method, QUIC requires 10 hours to solve a problem (with 10,000 nodes) that arises from a climate application, while our proposed algorithm, Divide and Conquer QUIC (DC-QUIC) only requires one hour to solve the problem.