title string | paper_url string | authors list | type string | primary_area string | abstract large_string | keywords list | TL;DR large_string | submission_number int64 | arxiv_id string | arxiv_id_source string | embedding list |
|---|---|---|---|---|---|---|---|---|---|---|---|
An Optimal Policy for Target Localization with Application to Electron Microscopy | https://proceedings.mlr.press/v28/sznitman13.html | [
"Raphael Sznitman",
"Aurelien Lucchi",
"Peter Frazier",
"Bruno Jedynak",
"Pascal Fua"
] | null | null | This paper considers the task of finding a target location by making a limited number of sequential observations. Each observation results from evaluating an imperfect classifier of a chosen cost and accuracy on an interval of chosen length and position. Within a Bayesian framework, we study the problem of minimizing... | [] | null | 1 | null | null | [
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Domain Generalization via Invariant Feature Representation | https://proceedings.mlr.press/v28/muandet13.html | [
"Krikamol Muandet",
"David Balduzzi",
"Bernhard Schölkopf"
] | null | null | This paper investigates domain generalization: How to take knowledge acquired from an arbitrary number of related domains and apply it to previously unseen domains? We propose Domain-Invariant Component Analysis (DICA), a kernel-based optimization algorithm that learns an invariant transformation by minimizing the diss... | [] | null | 2 | 1301.2115 | title_snapshot | [
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A Spectral Learning Approach to Range-Only SLAM | https://proceedings.mlr.press/v28/boots13.html | [
"Byron Boots",
"Geoff Gordon"
] | null | null | We present a novel spectral learning algorithm for simultaneous localization and mapping (SLAM) from range data with known correspondences. This algorithm is an instance of a general spectral system identification framework, from which it inherits several desirable properties, including statistical consistency and no ... | [] | null | 3 | 1207.2491 | title_snapshot | [
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Near-Optimal Bounds for Cross-Validation via Loss Stability | https://proceedings.mlr.press/v28/kumar13a.html | [
"Ravi Kumar",
"Daniel Lokshtanov",
"Sergei Vassilvitskii",
"Andrea Vattani"
] | null | null | Multi-fold cross-validation is an established practice to estimate the error rate of a learning algorithm. Quantifying the variance reduction gains due to cross-validation has been challenging due to the inherent correlations introduced by the folds. In this work we introduce a new and weak measure of stability... | [] | null | 4 | null | null | [
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Sparsity-Based Generalization Bounds for Predictive Sparse Coding | https://proceedings.mlr.press/v28/mehta13.html | [
"Nishant Mehta",
"Alexander Gray"
] | null | null | The goal of predictive sparse coding is to learn a representation of examples as sparse linear combinations of elements from a dictionary, such that a learned hypothesis linear in the new representation performs well on a predictive task. Predictive sparse coding has demonstrated impressive performance on a variety of ... | [] | null | 5 | null | null | [
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Sparse Uncorrelated Linear Discriminant Analysis | https://proceedings.mlr.press/v28/zhang13.html | [
"Xiaowei Zhang",
"Delin Chu"
] | null | null | In this paper, we develop a novel approach for sparse uncorrelated linear discriminant analysis (ULDA). Our proposal is based on characterization of all solutions of the generalized ULDA. We incorporate sparsity into the ULDA transformation by seeking the solution with minimum \ell_1-norm from all minimum dimension sol... | [] | null | 6 | null | null | [
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Block-Coordinate Frank-Wolfe Optimization for Structural SVMs | https://proceedings.mlr.press/v28/lacoste-julien13.html | [
"Simon Lacoste-Julien",
"Martin Jaggi",
"Mark Schmidt",
"Patrick Pletscher"
] | null | null | We propose a randomized block-coordinate variant of the classic Frank-Wolfe algorithm for convex optimization with block-separable constraints. Despite its lower iteration cost, we show that it achieves a similar convergence rate in duality gap as the full Frank-Wolfe algorithm. We also show that, when applied to the d... | [] | null | 7 | 1207.4747 | title_snapshot | [
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Fast Probabilistic Optimization from Noisy Gradients | https://proceedings.mlr.press/v28/hennig13.html | [
"Philipp Hennig"
] | null | null | Stochastic gradient descent remains popular in large-scale machine learning, on account of its very low computational cost and robustness to noise. However, gradient descent is only linearly efficient and not transformation invariant. Scaling by a local measure can substantially improve its performance. One natural cho... | [] | null | 8 | null | null | [
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Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes | https://proceedings.mlr.press/v28/shamir13.html | [
"Ohad Shamir",
"Tong Zhang"
] | null | null | Stochastic Gradient Descent (SGD) is one of the simplest and most popular stochastic optimization methods. While it has already been theoretically studied for decades, the classical analysis usually required non-trivial smoothness assumptions, which do not apply to many modern applications of SGD with non-smooth object... | [] | null | 9 | 1212.1824 | title_snapshot | [
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Stochastic Alternating Direction Method of Multipliers | https://proceedings.mlr.press/v28/ouyang13.html | [
"Hua Ouyang",
"Niao He",
"Long Tran",
"Alexander Gray"
] | null | null | The Alternating Direction Method of Multipliers (ADMM) has received lots of attention recently due to the tremendous demand from large-scale and data-distributed machine learning applications. In this paper, we present a stochastic setting for optimization problems with non-smooth composite objective functions. To solv... | [] | null | 10 | null | null | [
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Noisy Sparse Subspace Clustering | https://proceedings.mlr.press/v28/wang13.html | [
"Yu-Xiang Wang",
"Huan Xu"
] | null | null | This paper considers the problem of subspace clustering under noise. Specifically, we study the behavior of Sparse Subspace Clustering (SSC) when either adversarial or random noise is added to the unlabelled input data points, which are assumed to lie in a union of low-dimensional subspaces. We show that a modified ve... | [] | null | 11 | 1309.1233 | title_snapshot | [
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Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models | https://proceedings.mlr.press/v28/williamson13.html | [
"Sinead Williamson",
"Avinava Dubey",
"Eric Xing"
] | null | null | Nonparametric mixture models based on the Dirichlet process are an elegant alternative to finite models when the number of underlying components is unknown, but inference in such models can be slow. Existing attempts to parallelize inference in such models have relied on introducing approximations, which can lead to in... | [] | null | 12 | null | null | [
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Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction | https://proceedings.mlr.press/v28/giguere13.html | [
"Sébastien Giguère",
"François Laviolette",
"Mario Marchand",
"Khadidja Sylla"
] | null | null | We provide rigorous guarantees for the regression approach to structured output prediction. We show that the quadratic regression loss is a convex surrogate of the prediction loss when the output kernel satisfies some condition with respect to the prediction loss. We provide two upper bounds of the prediction risk that... | [] | null | 13 | null | null | [
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Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures | https://proceedings.mlr.press/v28/bergstra13.html | [
"James Bergstra",
"Daniel Yamins",
"David Cox"
] | null | null | Many computer vision algorithms depend on configuration settings that are typically hand-tuned in the course of evaluating the algorithm for a particular data set. While such parameter tuning is often presented as being incidental to the algorithm, correctly setting these parameter choices is frequently critical to rea... | [] | null | 14 | null | null | [
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Gibbs Max-Margin Topic Models with Fast Sampling Algorithms | https://proceedings.mlr.press/v28/zhu13.html | [
"Jun Zhu",
"Ning Chen",
"Hugh Perkins",
"Bo Zhang"
] | null | null | Existing max-margin supervised topic models rely on an iterative procedure to solve multiple latent SVM subproblems with additional mean-field assumptions on the desired posterior distributions. This paper presents Gibbs max-margin supervised topic models by minimizing an expected margin loss, an upper bound of the exi... | [] | null | 15 | null | null | [
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Cost-Sensitive Tree of Classifiers | https://proceedings.mlr.press/v28/xu13.html | [
"Zhixiang Xu",
"Matt Kusner",
"Kilian Weinberger",
"Minmin Chen"
] | null | null | Recently, machine learning algorithms have successfully entered large-scale real-world industrial applications (e.g. search engines and email spam filters). Here, the CPU cost during test-time must be budgeted and accounted for. In this paper, we address the challenge of balancing test-time cost and the classifier accu... | [] | null | 16 | 1210.2771 | title_snapshot | [
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Learning Hash Functions Using Column Generation | https://proceedings.mlr.press/v28/li13a.html | [
"Xi Li",
"Guosheng Lin",
"Chunhua Shen",
"Anton Hengel",
"Anthony Dick"
] | null | null | Fast nearest neighbor searching is becoming an increasingly important tool in solving many large-scale problems. Recently a number of approaches to learning data-dependent hash functions have been developed. In this work, we propose a column generation based method for learning data-dependent hash functions on t... | [] | null | 17 | 1303.0339 | title_snapshot | [
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Combinatorial Multi-Armed Bandit: General Framework and Applications | https://proceedings.mlr.press/v28/chen13a.html | [
"Wei Chen",
"Yajun Wang",
"Yang Yuan"
] | null | null | We define a general framework for a large class of combinatorial multi-armed bandit (CMAB) problems, where simple arms with unknown istributions form \em super arms. In each round, a super arm is played and the outcomes of its related simple arms are observed, which helps the selection of super arms in future rounds. ... | [] | null | 18 | null | null | [
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Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization | https://proceedings.mlr.press/v28/chen13b.html | [
"Yuxin Chen",
"Andreas Krause"
] | null | null | Active learning can lead to a dramatic reduction in labeling effort. However, in many practical implementations (such as crowdsourcing, surveys, high-throughput experimental design), it is preferable to query labels for batches of examples to be labelled in parallel. While several heuristics have been proposed for bat... | [] | null | 19 | null | null | [
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Convex formulations of radius-margin based Support Vector Machines | https://proceedings.mlr.press/v28/do13.html | [
"Huyen Do",
"Alexandros Kalousis"
] | null | null | We consider Support Vector Machines (SVMs) learned together with linear transformations of the feature spaces on which they are applied. Under this scenario the radius of the smallest data enclosing sphere is no longer fixed. Therefore optimizing the SVM error bound by considering both the radius and the margin has the... | [] | null | 20 | null | null | [
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Modelling Sparse Dynamical Systems with Compressed Predictive State Representations | https://proceedings.mlr.press/v28/hamilton13.html | [
"William L. Hamilton",
"Mahdi Milani Fard",
"Joelle Pineau"
] | null | null | Efficiently learning accurate models of dynamical systems is of central importance for developing rational agents that can succeed in a wide range of challenging domains. The difficulty of this learning problem is particularly acute in settings with large observation spaces and partial observability. We present a new a... | [] | null | 21 | null | null | [
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A Machine Learning Framework for Programming by Example | https://proceedings.mlr.press/v28/menon13.html | [
"Aditya Menon",
"Omer Tamuz",
"Sumit Gulwani",
"Butler Lampson",
"Adam Kalai"
] | null | null | Learning programs is a timely and interesting challenge. In Programming by Example (PBE), a system attempts to infer a program from input and output examples alone, by searching for a composition of some set of base functions. We show how machine learning can be used to speed up this seemingly hopeless search problem, ... | [] | null | 22 | null | null | [
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Discriminatively Activated Sparselets | https://proceedings.mlr.press/v28/girshick13.html | [
"Ross Girshick",
"Hyun Oh Song",
"Trevor Darrell"
] | null | null | Shared representations are highly appealing due to their potential for gains in computational and statistical efficiency. Compressing a shared representation leads to greater computational savings, but at the same time can severely decrease performance on a target task. Recently, sparselets (Song et al., 2012) wer... | [] | null | 23 | null | null | [
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The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification | https://proceedings.mlr.press/v28/pele13.html | [
"Ofir Pele",
"Ben Taskar",
"Amir Globerson",
"Michael Werman"
] | null | null | Linear classiffers are much faster to learn and test than non-linear ones. On the other hand, non-linear kernels offer improved performance, albeit at the increased cost of training kernel classiffers. To use non-linear mappings with efficient linear learning algorithms, explicit embeddings that approximate popular ker... | [] | null | 24 | null | null | [
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Fixed-Point Model For Structured Labeling | https://proceedings.mlr.press/v28/li13b.html | [
"Quannan Li",
"Jingdong Wang",
"David Wipf",
"Zhuowen Tu"
] | null | null | In this paper, we propose a simple but effective solution to the structured labeling problem: a fixed-point model. Recently, layered models with sequential classifiers/regressors have gained an increasing amount of interests for structural prediction. Here, we design an algorithm with a new perspective on layered... | [] | null | 25 | null | null | [
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Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation | https://proceedings.mlr.press/v28/gong13.html | [
"Boqing Gong",
"Kristen Grauman",
"Fei Sha"
] | null | null | Learning domain-invariant features is of vital importance to unsupervised domain adaptation, where classifiers trained on the source domain need to be adapted to a different target domain for which no labeled examples are available. In this paper, we propose a novel approach for learning such features. The central idea... | [] | null | 26 | null | null | [
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Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization | https://proceedings.mlr.press/v28/kumar13b.html | [
"Abhishek Kumar",
"Vikas Sindhwani",
"Prabhanjan Kambadur"
] | null | null | The separability assumption (Arora et al., 2012; Donoho & Stodden, 2003) turns non-negative matrix factorization (NMF) into a tractable problem. Recently, a new class of provably-correct NMF algorithms have emerged under this assumption. In this paper, we reformulate the separable NMF problem as that of finding the ext... | [] | null | 27 | 1210.1190 | title_snapshot | [
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Principal Component Analysis on non-Gaussian Dependent Data | https://proceedings.mlr.press/v28/han13.html | [
"Fang Han",
"Han Liu"
] | null | null | In this paper, we analyze the performance of a semiparametric principal component analysis named Copula Component Analysis (COCA) (Han & Liu, 2012) when the data are dependent. The semiparametric model assumes that, after unspecified marginally monotone transformations, the distributions are multivariate Gaussian. We s... | [] | null | 28 | null | null | [
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Learning Linear Bayesian Networks with Latent Variables | https://proceedings.mlr.press/v28/anandkumar13.html | [
"Animashree Anandkumar",
"Daniel Hsu",
"Adel Javanmard",
"Sham Kakade"
] | null | null | This work considers the problem of learning linear Bayesian networks when some of the variables are unobserved. Identifiability and efficient recovery from low-order observable moments are established under a novel graphical constraint. The constraint concerns the expansion properties of the underlying directed ac... | [] | null | 29 | null | null | [
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Multiple Identifications in Multi-Armed Bandits | https://proceedings.mlr.press/v28/bubeck13.html | [
"Séebastian Bubeck",
"Tengyao Wang",
"Nitin Viswanathan"
] | null | null | We study the problem of identifying the top m arms in a multi-armed bandit game. Our proposed solution relies on a new algorithm based on successive rejects of the seemingly bad arms, and successive accepts of the good ones. This algorithmic contribution allows to tackle other multiple identifications settings that wer... | [] | null | 30 | 1205.3181 | title_snapshot | [
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Learning Optimally Sparse Support Vector Machines | https://proceedings.mlr.press/v28/cotter13.html | [
"Andrew Cotter",
"Shai Shalev-Shwartz",
"Nati Srebro"
] | null | null | We show how to train SVMs with an optimal guarantee on the number of support vectors (up to constants), and with sample complexity and training runtime bounds matching the best known for kernel SVM optimization (i.e. without any additional asymptotic cost beyond standard SVM training). Our method is simple to implement... | [] | null | 31 | null | null | [
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Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks | https://proceedings.mlr.press/v28/heaukulani13.html | [
"Creighton Heaukulani",
"Zoubin Ghahramani"
] | null | null | Current Bayesian models for dynamic social network data have focused on modelling the influence of evolving unobserved structure on observed social interactions. However, an understanding of how observed social relationships from the past affect future unobserved structure in the network has been neglected. In this pa... | [] | null | 32 | null | null | [
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Efficient Sparse Group Feature Selection via Nonconvex Optimization | https://proceedings.mlr.press/v28/xiang13.html | [
"Shuo Xiang",
"Xiaoshen Tong",
"Jieping Ye"
] | null | null | Sparse feature selection has been demonstrated to be effective in handling high-dimensional data. While promising, most of the existing works use convex methods, which may be suboptimal in terms of the accuracy of feature selection and parameter estimation. In this paper, we expand a nonconvex paradigm to sparse group ... | [] | null | 33 | 1205.5075 | title_snapshot | [
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Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation Model | https://proceedings.mlr.press/v28/xiao13.html | [
"Min Xiao",
"Yuhong Guo"
] | null | null | In this paper, we propose to address the problem of domain adaptation for sequence labeling tasks via distributed representation learning by using a log-bilinear language adaptation model. The proposed neural probabilistic language model simultaneously models two different but related data distributions in the source a... | [] | null | 34 | null | null | [
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Maximum Variance Correction with Application to A* Search | https://proceedings.mlr.press/v28/chen13c.html | [
"Wenlin Chen",
"Kilian Weinberger",
"Yixin Chen"
] | null | null | In this paper we introduce Maximum Variance Correction (MVC), which finds large-scale feasible solutions to Maximum Variance Unfolding (MVU) by post-processing embeddings from any manifold learning algorithm. It increases the scale of MVU embeddings by several orders of magnitude and is naturally parallel. This unprece... | [] | null | 35 | null | null | [
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Adaptive Sparsity in Gaussian Graphical Models | https://proceedings.mlr.press/v28/wong13.html | [
"Eleanor Wong",
"Suyash Awate",
"P. Thomas Fletcher"
] | null | null | An effective approach to structure learning and parameter estimation for Gaussian graphical models is to impose a sparsity prior, such as a Laplace prior, on the entries of the precision matrix. Such an approach involves a hyperparameter that must be tuned to control the amount of sparsity. In this paper, we introduce ... | [] | null | 36 | null | null | [
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Average Reward Optimization Objective In Partially Observable Domains | https://proceedings.mlr.press/v28/grinberg13.html | [
"Yuri Grinberg",
"Doina Precup"
] | null | null | We consider the problem of average reward optimization in domains with partial observability, within the modeling framework of linear predictive state representations (PSRs). The key to average-reward computation is to have a well-defined stationary behavior of a system, so the required averages can be computed. If, ad... | [] | null | 37 | null | null | [
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Feature Selection in High-Dimensional Classification | https://proceedings.mlr.press/v28/kolar13.html | [
"Mladen Kolar",
"Han Liu"
] | null | null | High-dimensional discriminant analysis is of fundamental importance in multivariate statistics. Existing theoretical results sharply characterize different procedures, providing sharp convergence results for the classification risk, as well as the l2 convergence results to the discriminative rule. However, sharp theore... | [] | null | 38 | null | null | [
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Human Boosting | https://proceedings.mlr.press/v28/pareek13.html | [
"Harsh Pareek",
"Pradeep Ravikumar"
] | null | null | Humans may be exceptional learners but they have biological limitations and moreover, inductive biases similar to machine learning algorithms. This puts limits on human learning ability and on the kinds of learning tasks humans can easily handle. In this paper, we consider the problem of “boosting” human learners to ex... | [] | null | 39 | null | null | [
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Efficient Dimensionality Reduction for Canonical Correlation Analysis | https://proceedings.mlr.press/v28/avron13.html | [
"Haim Avron",
"Christos Boutsidis",
"Sivan Toledo",
"Anastasios Zouzias"
] | null | null | We present a fast algorithm for approximate Canonical Correlation Analysis (CCA). Given a pair of tall-and-thin matrices, the proposed algorithm first employs a randomized dimensionality reduction transform to reduce the size of the input matrices, and then applies any standard CCA algorithm to the new pair of matrice... | [] | null | 40 | 1209.2185 | title_snapshot | [
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Parsing epileptic events using a Markov switching process model for correlated time series | https://proceedings.mlr.press/v28/wulsin13.html | [
"Drausin Wulsin",
"Emily Fox",
"Brian Litt"
] | null | null | Patients with epilepsy can manifest short, sub-clinical epileptic “bursts” in addition to full-blown clinical seizures. We believe the relationship between these two classes of events—something not previously studied quantitatively—could yield important insights into the nature and intrinsic dynamics of seizures. A goa... | [] | null | 41 | null | null | [
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Optimal rates for stochastic convex optimization under Tsybakov noise condition | https://proceedings.mlr.press/v28/ramdas13.html | [
"Aaditya Ramdas",
"Aarti Singh"
] | null | null | We focus on the problem of minimizing a convex function f over a convex set S given T queries to a stochastic first order oracle. We argue that the complexity of convex minimization is only determined by the rate of growth of the function around its minimum x^*_f,S, as quantified by a Tsybakov-like noise condition. Spe... | [] | null | 42 | 1207.3012 | title_judge | [
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A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning | https://proceedings.mlr.press/v28/afkanpour13.html | [
"Arash Afkanpour",
"András György",
"Csaba Szepesvari",
"Michael Bowling"
] | null | null | We consider the problem of simultaneously learning to linearly combine a very large number of kernels and learn a good predictor based on the learnt kernel. When the number of kernels d to be combined is very large, multiple kernel learning methods whose computational cost scales linearly in d are intractable. We propo... | [] | null | 43 | 1205.0288 | title_snapshot | [
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Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery | https://proceedings.mlr.press/v28/chen13d.html | [
"Yudong Chen",
"Constantine Caramanis"
] | null | null | Many models for sparse regression typically assume that the covariates are known completely, and without noise. Particularly in high-dimensional applications, this is often not the case. Worse yet, even estimating statistics of the noise (the noise covariance) can be a central challenge. In this paper we develop a simp... | [] | null | 44 | null | null | [
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Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method | https://proceedings.mlr.press/v28/suzuki13.html | [
"Taiji Suzuki"
] | null | null | We develop new stochastic optimization methods that are applicable to a wide range of structured regularizations. Basically our methods are combinations of basic stochastic optimization techniques and Alternating Direction Multiplier Method (ADMM). ADMM is a general framework for optimizing a composite function, ... | [] | null | 45 | null | null | [
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A New Frontier of Kernel Design for Structured Data | https://proceedings.mlr.press/v28/shin13.html | [
"Kilho Shin"
] | null | null | Many kernels for discretely structured data in the literature are designed within the framework of the convolution kernel and its generalization, the mapping kernel. The two most important advantages to use this framework is an easy-to-check criteria of positive definiteness and efficient computation based on the dynam... | [] | null | 46 | null | null | [
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Learning with Marginalized Corrupted Features | https://proceedings.mlr.press/v28/vandermaaten13.html | [
"Laurens Maaten",
"Minmin Chen",
"Stephen Tyree",
"Kilian Weinberger"
] | null | null | The goal of machine learning is to develop predictors that generalize well to test data. Ideally, this is achieved by training on very large (infinite) training data sets that capture all variations in the data distribution. In the case of finite training data, an effective solution is to extend the training set with a... | [] | null | 47 | null | null | [
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Approximation properties of DBNs with binary hidden units and real-valued visible units | https://proceedings.mlr.press/v28/krause13.html | [
"Oswin Krause",
"Asja Fischer",
"Tobias Glasmachers",
"Christian Igel"
] | null | null | Deep belief networks (DBNs) can approximate any distribution over fixed-length binary vectors. However, DBNs are frequently applied to model real-valued data, and so far little is known about their representational power in this case. We analyze the approximation properties of DBNs with two layers of binary hidden uni... | [] | null | 48 | null | null | [
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Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization | https://proceedings.mlr.press/v28/jaggi13.html | [
"Martin Jaggi"
] | null | null | We provide stronger and more general primal-dual convergence results for Frank-Wolfe-type algorithms (a.k.a. conditional gradient) for constrained convex optimization, enabled by a simple framework of duality gap certificates. Our analysis also holds if the linear subproblems are only solved approximately (as well as i... | [] | null | 49 | null | null | [
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General Functional Matrix Factorization Using Gradient Boosting | https://proceedings.mlr.press/v28/chen13e.html | [
"Tianqi Chen",
"Hang Li",
"Qiang Yang",
"Yong Yu"
] | null | null | Matrix factorization is among the most successful techniques for collaborative filtering. One challenge of collaborative filtering is how to utilize available auxiliary information to improve prediction accuracy. In this paper, we study the problem of utilizing auxiliary information as features of factorization and ... | [] | null | 50 | null | null | [
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Iterative Learning and Denoising in Convolutional Neural Associative Memories | https://proceedings.mlr.press/v28/karbasi13.html | [
"Amin Karbasi",
"Amir Hesam Salavati",
"Amin Shokrollahi"
] | null | null | The task of a neural associative memory is to retrieve a set of previously memorized patterns from their noisy versions by using a network of neurons. Hence, an ideal network should be able to 1) gradually learn a set of patterns, 2) retrieve the correct pattern from noisy queries and 3) maximize the number of memorize... | [] | null | 51 | null | null | [
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Scaling Multidimensional Gaussian Processes using Projected Additive Approximations | https://proceedings.mlr.press/v28/gilboa13.html | [
"Elad Gilboa",
"Yunus Saatçi",
"John Cunningham",
"Elad Gilboa"
] | null | null | Exact Gaussian Process (GP) regression has O(N^3) runtime for data size N, making it intractable for large N. Advances in GP scaling have not been extended to the multidimensional input setting, despite the preponderance of multidimensional applications. This paper introduces and tests a novel method of projected add... | [] | null | 52 | 1209.4120 | title_judge | [
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Active Learning for Multi-Objective Optimization | https://proceedings.mlr.press/v28/zuluaga13.html | [
"Marcela Zuluaga",
"Guillaume Sergent",
"Andreas Krause",
"Markus Püschel"
] | null | null | In many fields one encounters the challenge of identifying, out of a pool of possible designs, those that simultaneously optimize multiple objectives. This means that usually there is not one optimal design but an entire set of Pareto-optimal ones with optimal tradeoffs in the objectives. In many applications, evaluati... | [] | null | 53 | null | null | [
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A Generalized Kernel Approach to Structured Output Learning | https://proceedings.mlr.press/v28/kadri13.html | [
"Hachem Kadri",
"Mohammad Ghavamzadeh",
"Philippe Preux"
] | null | null | 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 decouplin... | [] | null | 54 | 1205.2171 | title_snapshot | [
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Efficient Active Learning of Halfspaces: an Aggressive Approach | https://proceedings.mlr.press/v28/gonen13.html | [
"Alon Gonen",
"Sivan Sabato",
"Shai Shalev-Shwartz"
] | null | null | We study pool-based active learning of half-spaces. We revisit the aggressive approach for active learning in the realizable case, and show that it can be made efficient and practical, while also having theoretical guarantees under reasonable assumptions. We further show, both theoretically and experimentally, that it... | [] | null | 55 | 1208.3561 | title_snapshot | [
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Enhanced statistical rankings via targeted data collection | https://proceedings.mlr.press/v28/osting13.html | [
"Braxton Osting",
"Christoph Brune",
"Stanley Osher"
] | null | null | Given a graph where vertices represent alternatives and pairwise comparison data, y_ij, is given on the edges, the statistical ranking problem is to find a potential function, defined on the vertices, such that the gradient of the potential function agrees with pairwise comparisons. We study the dependence of the stati... | [] | null | 56 | null | null | [
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Online Feature Selection for Model-based Reinforcement Learning | https://proceedings.mlr.press/v28/nguyen13.html | [
"Trung Nguyen",
"Zhuoru Li",
"Tomi Silander",
"Tze Yun Leong"
] | null | null | We propose a new framework for learning the world dynamics of feature-rich environments in model-based reinforcement learning. The main idea is formalized as a new, factored state-transition representation that supports efficient online-learning of the relevant features. We construct the transition models through predi... | [] | null | 57 | null | null | [
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ELLA: An Efficient Lifelong Learning Algorithm | https://proceedings.mlr.press/v28/ruvolo13.html | [
"Paul Ruvolo",
"Eric Eaton"
] | null | null | The problem of learning multiple consecutive tasks, known as lifelong learning, is of great importance to the creation of intelligent, general-purpose, and flexible machines. In this paper, we develop a method for online multi-task learning in the lifelong learning setting. The proposed Efficient Lifelong Learning Al... | [] | null | 58 | null | null | [
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A Structural SVM Based Approach for Optimizing Partial AUC | https://proceedings.mlr.press/v28/narasimhan13.html | [
"Harikrishna Narasimhan",
"Shivani Agarwal"
] | null | null | The area under the ROC curve (AUC) is a widely used performance measure in machine learning. Increasingly, however, in several applications, ranging from ranking and biometric screening to medical diagnosis, performance is measured not in terms of the full area under the ROC curve, but instead, in terms of the partial ... | [] | null | 59 | null | null | [
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Convex Relaxations for Learning Bounded-Treewidth Decomposable Graphs | https://proceedings.mlr.press/v28/kumar13c.html | [
"K. S. Sesh Kumar",
"Francis Bach"
] | null | null | We consider the problem of learning the structure of undirected graphical models with bounded treewidth, within the maximum likelihood framework. This is an NP-hard problem and most approaches consider local search techniques. In this paper, we pose it as a combinatorial optimization problem, which is then relaxed to a... | [] | null | 60 | 1212.2573 | title_snapshot | [
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Adaptive Task Assignment for Crowdsourced Classification | https://proceedings.mlr.press/v28/ho13.html | [
"Chien-Ju Ho",
"Shahin Jabbari",
"Jennifer Wortman Vaughan"
] | null | null | Crowdsourcing markets have gained popularity as a tool for inexpensively collecting data from diverse populations of workers. Classification tasks, in which workers provide labels (such as “offensive” or “not offensive”) for instances (such as websites), are among the most common tasks posted, but due to a mix of human... | [] | null | 61 | null | null | [
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Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning | https://proceedings.mlr.press/v28/maillard13.html | [
"Odalric-Ambrym Maillard",
"Phuong Nguyen",
"Ronald Ortner",
"Daniil Ryabko"
] | null | null | We consider an agent interacting with an environment in a single stream of actions, observations, and rewards, with no reset. This process is not assumed to be a Markov Decision Process (MDP). Rather, the agent has several representations (mapping histories of past interactions to a discrete state space) of the environ... | [] | null | 62 | 1302.2553 | title_snapshot | [
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Better Mixing via Deep Representations | https://proceedings.mlr.press/v28/bengio13.html | [
"Yoshua Bengio",
"Gregoire Mesnil",
"Yann Dauphin",
"Salah Rifai"
] | null | null | It has been hypothesized, and supported with experimental evidence, that deeper representations, when well trained, tend to do a better job at disentangling the underlying factors of variation. We study the following related conjecture: better representations, in the sense of better disentangling, can be exploited to ... | [] | null | 63 | 1207.4404 | title_snapshot | [
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Online Latent Dirichlet Allocation with Infinite Vocabulary | https://proceedings.mlr.press/v28/zhai13.html | [
"Ke Zhai",
"Jordan Boyd-Graber"
] | null | null | Topic models based on latent Dirichlet allocation (LDA) assume a predefined vocabulary a priori. This is reasonable in batch settings, but it is not reasonable when data are revealed over time, as is the case with streaming / online algorithms. To address this lacuna, we extend LDA by drawing topics from a Dirichlet pr... | [] | null | 64 | null | null | [
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Characterizing the Representer Theorem | https://proceedings.mlr.press/v28/yu13.html | [
"Yaoliang Yu",
"Hao Cheng",
"Dale Schuurmans",
"Csaba Szepesvari"
] | null | null | The representer theorem assures that kernel methods retain optimality under penalized empirical risk minimization. While a sufficient condition on the form of the regularizer guaranteeing the representer theorem has been known since the initial development of kernel methods, necessary conditions have only been investig... | [] | null | 65 | null | null | [
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Dynamical Models and tracking regret in online convex programming | https://proceedings.mlr.press/v28/hall13.html | [
"Eric Hall",
"Rebecca Willett"
] | null | null | This paper describes a new online convex optimization method which incorporates a family of candidate dynamical models and establishes novel tracking regret bounds that scale with comparator’s deviation from the best dynamical model in this family. Previous online optimization methods are designed to have a total accum... | [] | null | 66 | 1301.1254 | title_snapshot | [
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Large-Scale Bandit Problems and KWIK Learning | https://proceedings.mlr.press/v28/abernethy13.html | [
"Jacob Abernethy",
"Kareem Amin",
"Michael Kearns",
"Moez Draief"
] | null | null | We show that parametric multi-armed bandit (MAB) problems with large state and action spaces can be algorithmically reduced to the supervised learning model known as Knows What It Knows or KWIK learning. We give matching impossibility results showing that the KWIK learnability requirement cannot be replaced by weaker s... | [] | null | 67 | null | null | [
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Vanishing Component Analysis | https://proceedings.mlr.press/v28/livni13.html | [
"Roi Livni",
"David Lehavi",
"Sagi Schein",
"Hila Nachliely",
"Shai Shalev-Shwartz",
"Amir Globerson"
] | null | null | The vanishing ideal of a set of n points S, is the set of all polynomials that attain the value of zero on all the points in S. Such ideals can be compactly represented using a small set of polynomials known as generators of the ideal. Here we describe and analyze an efficient procedure that constructs a set of generat... | [] | null | 68 | null | null | [
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Learning an Internal Dynamics Model from Control Demonstration | https://proceedings.mlr.press/v28/golub13.html | [
"Matthew Golub",
"Steven Chase",
"Byron Yu"
] | null | null | Much work in optimal control and inverse control has assumed that the controller has perfect knowledge of plant dynamics. However, if the controller is a human or animal subject, the subject’s internal dynamics model may differ from the true plant dynamics. Here, we consider the problem of learning the subject’s inte... | [] | null | 69 | null | null | [
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Robust Structural Metric Learning | https://proceedings.mlr.press/v28/lim13.html | [
"Daryl Lim",
"Gert Lanckriet",
"Brian McFee"
] | null | null | Metric learning algorithms produce a linear transformation of data which is optimized for a prediction task, such as nearest-neighbor classification or ranking. However, when the input data contains a large portion of non-informative features, existing methods fail to identify the relevant features, and performance de... | [] | null | 70 | null | null | [
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Constrained fractional set programs and their application in local clustering and community detection | https://proceedings.mlr.press/v28/buhler13.html | [
"Thomas Bühler",
"Shyam Sundar Rangapuram",
"Simon Setzer",
"Matthias Hein"
] | null | null | The (constrained) minimization of a ratio of set functions is a problem frequently occurring in clustering and community detection. As these optimization problems are typically NP-hard, one uses convex or spectral relaxations in practice. While these relaxations can be solved globally optimally, they are often too loos... | [] | null | 71 | 1306.3409 | title_snapshot | [
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Efficient Semi-supervised and Active Learning of Disjunctions | https://proceedings.mlr.press/v28/balcan13.html | [
"Nina Balcan",
"Christopher Berlind",
"Steven Ehrlich",
"Yingyu Liang"
] | null | null | We provide efficient algorithms for learning disjunctions in the semi-supervised setting under a natural regularity assumption introduced by (Balcan & Blum, 2005). We prove bounds on the sample complexity of our algorithms under a mild restriction on the data distribution. We also give an active learning algorithm with... | [] | null | 72 | null | null | [
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Convex Adversarial Collective Classification | https://proceedings.mlr.press/v28/torkamani13.html | [
"MohamadAli Torkamani",
"Daniel Lowd"
] | null | null | In this paper, we present a novel method for robustly performing collective classification in the presence of a malicious adversary that can modify up to a fixed number of binary-valued attributes. Our method is formulated as a convex quadratic program that guarantees optimal weights against a worst-case adversary... | [] | null | 73 | null | null | [
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Rounding Methods for Discrete Linear Classification | https://proceedings.mlr.press/v28/chevaleyre13.html | [
"Yann Chevaleyre",
"Frédéerick Koriche",
"Jean-daniel Zucker"
] | null | null | Learning discrete linear functions is a notoriously difficult challenge. In this paper, the learning task is cast as combinatorial optimization problem: given a set of positive and negative feature vectors in the Euclidean space, the goal is to find a discrete linear function that minimizes the cumulative hinge loss of... | [] | null | 74 | null | null | [
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Mixture of Mutually Exciting Processes for Viral Diffusion | https://proceedings.mlr.press/v28/yang13a.html | [
"Shuang-Hong Yang",
"Hongyuan Zha"
] | null | null | \emphDiffusion network inference and \emphmeme tracking have been two key challenges in viral diffusion. This paper shows that these two tasks can be addressed simultaneously with a probabilistic model involving a mixture of mutually exciting point processes. A fast learning algorithms is developed based on mean-field ... | [] | null | 75 | null | null | [
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Gaussian Process Vine Copulas for Multivariate Dependence | https://proceedings.mlr.press/v28/lopez-paz13.html | [
"David Lopez-Paz",
"Jose Miguel Hernández-Lobato",
"Ghahramani Zoubin"
] | null | null | Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, ... | [] | null | 76 | 1302.3979 | title_snapshot | [
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Stochastic Simultaneous Optimistic Optimization | https://proceedings.mlr.press/v28/valko13.html | [
"Michal Valko",
"Alexandra Carpentier",
"Rémi Munos"
] | null | null | We study the problem of global maximization of a function f given a finite number of evaluations perturbed by noise. We consider a very weak assumption on the function, namely that it is locally smooth (in some precise sense) with respect to some semi-metric, around one of its global maxima. Compared to previous works ... | [] | null | 77 | 2604.24537 | title_snapshot | [
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Toward Optimal Stratification for Stratified Monte-Carlo Integration | https://proceedings.mlr.press/v28/carpentier13.html | [
"Alexandra Carpentier",
"Rémi Munos"
] | null | null | We consider the problem of adaptive stratified sampling for Monte Carlo integration of a function, given a finite number of function evaluations perturbed by noise. Here we address the problem of adapting simultaneously the number of samples into each stratum and the stratification itself. We show a tradeoff in the siz... | [] | null | 78 | 1303.2892 | title_snapshot | [
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... |
A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems | https://proceedings.mlr.press/v28/gong13a.html | [
"Pinghua Gong",
"Changshui Zhang",
"Zhaosong Lu",
"Jianhua Huang",
"Jieping Ye"
] | null | null | Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterparts in several sparse learning settings. However, solving the non-convex optimization problems associated with non-conv... | [] | null | 79 | 1303.4434 | title_snapshot | [
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Thurstonian Boltzmann Machines: Learning from Multiple Inequalities | https://proceedings.mlr.press/v28/tran13.html | [
"Truyen Tran",
"Dinh Phung",
"Svetha Venkatesh"
] | null | null | We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture that can naturally incorporate a wide range of data inputs at the same time. Our motivation rests in the Thurstonian view that many discrete data types can be considered as being generated from a subset of underlying latent continuous variables, ... | [] | null | 80 | 1408.0055 | title_snapshot | [
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A Variational Approximation for Topic Modeling of Hierarchical Corpora | https://proceedings.mlr.press/v28/kim13.html | [
"Do-kyum Kim",
"Geoffrey Voelker",
"Lawrence Saul"
] | null | null | We study the problem of topic modeling in corpora whose documents are organized in a multi-level hierarchy. We explore a parametric approach to this problem, assuming that the number of topics is known or can be estimated by cross-validation. The models we consider can be viewed as special (finite-dimensional) instan... | [] | null | 81 | null | null | [
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... |
Forecastable Component Analysis | https://proceedings.mlr.press/v28/goerg13.html | [
"Georg Goerg"
] | null | null | I introduce Forecastable Component Analysis (ForeCA), a novel dimension reduction technique for temporally dependent signals. Based on a new forecastability measure, ForeCA finds an optimal transformation to separate a multivariate time series into a forecastable and an orthogonal white noise space. I present a converg... | [] | null | 82 | null | null | [
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-0.04058651626110077,
0.012... |
Ellipsoidal Multiple Instance Learning | https://proceedings.mlr.press/v28/krummenacher13.html | [
"Gabriel Krummenacher",
"Cheng Soon Ong",
"Joachim Buhmann"
] | null | null | We propose a large margin method for asymmetric learning with ellipsoids, called eMIL, suited to multiple instance learning (MIL). We derive the distance between ellipsoids and the hyperplane, generalising the standard support vector machine. Negative bags in MIL contain only negative instances, and we treat them akin ... | [] | null | 83 | null | null | [
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Local Low-Rank Matrix Approximation | https://proceedings.mlr.press/v28/lee13.html | [
"Joonseok Lee",
"Seungyeon Kim",
"Guy Lebanon",
"Yoram Singer"
] | null | null | Matrix approximation is a common tool in recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model where we assume instead that the matrix is locally of low-rank... | [] | null | 84 | null | null | [
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0.0060961488634347916,
-0.068542897701263... |
Generic Exploration and K-armed Voting Bandits | https://proceedings.mlr.press/v28/urvoy13.html | [
"Tanguy Urvoy",
"Fabrice Clerot",
"Raphael Féraud",
"Sami Naamane"
] | null | null | We study a stochastic online learning scheme with partial feedback where the utility of decisions is only observable through an estimation of the environment parameters. We propose a generic pure-exploration algorithm, able to cope with various utility functions from multi-armed bandits settings to dueling bandits. The... | [] | null | 85 | null | null | [
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A unifying framework for vector-valued manifold regularization and multi-view learning | https://proceedings.mlr.press/v28/haquang13.html | [
"Minh Hà Quang",
"Loris Bazzani",
"Vittorio Murino"
] | null | null | This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) formulation for the problem of learning an unknown functional dependency between a structured input space and a structured output space, in the Semi-Supervised Learning setting. Our formulation includes as special cases Vector-valued ... | [] | null | 86 | 1401.8066 | title_judge | [
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-0.05657079443335533,
-0.026862220838665962,
0.02621549554169178,
-0.08170103281736374,
0.01... |
Learning Connections in Financial Time Series | https://proceedings.mlr.press/v28/ganeshapillai13.html | [
"Gartheeban Ganeshapillai",
"John Guttag",
"Andrew Lo"
] | null | null | To reduce risk, investors seek assets that have high expected return and are unlikely to move in tandem. Correlation measures are generally used to quantify the connections between equities. The 2008 financial crisis, and its aftermath, demonstrated the need for a better way to quantify these connections. We present a ... | [] | null | 87 | null | null | [
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0.00282878614962101,
0.032393679022789,
-0.06829799711704254,
-0.0003... |
Fast dropout training | https://proceedings.mlr.press/v28/wang13a.html | [
"Sida Wang",
"Christopher Manning"
] | null | null | Preventing feature co-adaptation by encouraging independent contributions from different features often improves classification and regression performance. Dropout training (Hinton et al., 2012) does this by randomly dropping out (zeroing) hidden units and input features during training of neural networks. However, re... | [] | null | 88 | null | null | [
0.0021706183906644583,
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... |
Scalable Optimization of Neighbor Embedding for Visualization | https://proceedings.mlr.press/v28/yang13b.html | [
"Zhirong Yang",
"Jaakko Peltonen",
"Samuel Kaski"
] | null | null | Neighbor embedding (NE) methods have found their use in data visualization but are limited in big data analysis tasks due to their O(n^2) complexity for n data samples. We demonstrate that the obvious approach of subsampling produces inferior results and propose a generic approximated optimization technique that reduce... | [] | null | 89 | null | null | [
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0... |
Precision-recall space to correct external indices for biclustering | https://proceedings.mlr.press/v28/hanczar13.html | [
"Blaise Hanczar",
"Mohamed Nadif"
] | null | null | Biclustering is a major tool of data mining in many domains and many algorithms have emerged in recent years. All these algorithms aim to obtain coherent biclusters and it is crucial to have a reliable procedure for their validation. We point out the problem of size bias in biclustering evaluation and show how it can l... | [] | null | 90 | null | null | [
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0.00426197936758399,
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-0.04886813089251518,
0.01759255677461624,
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-0.05628956854343414,
0.00431... |
Monochromatic Bi-Clustering | https://proceedings.mlr.press/v28/wulff13.html | [
"Sharon Wulff",
"Ruth Urner",
"Shai Ben-David"
] | null | null | We propose a natural cost function for the bi-clustering task, the monochromatic cost. This cost function is suitable for detecting meaningful homogeneous bi-clusters based on categorical valued input matrices. Such tasks arise in many applications, such as the analysis of social networks and in systems-biology where ... | [] | null | 91 | null | null | [
-0.009434178471565247,
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-0.02081017941236496,
0.044736556708812714,
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0.0050191087648272514,
-0.020614301785826683,
-0.040793146938085556,
-0.00003680068766698241,
-0.010601772926747799,
-0.07447361201047897... |
Gated Autoencoders with Tied Input Weights | https://proceedings.mlr.press/v28/alain13.html | [
"Droniou Alain",
"Sigaud Olivier"
] | null | null | The semantic interpretation of images is one of the core applications of deep learning. Several techniques have been recently proposed to model the relation between two images, with application to pose estimation, action recognition or invariant object recognition. Among these techniques, higher-order Boltzmann machine... | [] | null | 92 | null | null | [
0.016061535105109215,
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-0.008084584027528763,
0.04472576826810837,
0.023054728284478188,
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0.016814015805721283,
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-0.039281655102968216,
-0.014397673308849335,
-0.00556239765137434,
-0.07760696113109589,
-0... |
Strict Monotonicity of Sum of Squares Error and Normalized Cut in the Lattice of Clusterings | https://proceedings.mlr.press/v28/rebagliati13.html | [
"Nicola Rebagliati"
] | null | null | Sum of Squares Error and Normalized Cut are two widely used clustering functional. It is known their minimum values are monotone with respect to the input number of clusters and this monotonicity does not allow for a simple automatic selection of a correct number of clusters. Here we study monotonicity not just on the ... | [] | null | 93 | null | null | [
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0.0021181663032621145,
0.005196890328079462,
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-0.05290818586945534,
... |
Transition Matrix Estimation in High Dimensional Time Series | https://proceedings.mlr.press/v28/han13a.html | [
"Fang Han",
"Han Liu"
] | null | null | In this paper, we propose a new method in estimating transition matrices of high dimensional vector autoregressive (VAR) models. Here the data are assumed to come from a stationary Gaussian VAR time series. By formulating the problem as a linear program, we provide a new approach to conduct inference on such models. In... | [] | null | 94 | null | null | [
-0.02161613665521145,
-0.03788226470351219,
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0.01451394334435463,
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0.... |
Label Partitioning For Sublinear Ranking | https://proceedings.mlr.press/v28/weston13.html | [
"Jason Weston",
"Ameesh Makadia",
"Hector Yee"
] | null | null | We consider the case of ranking a very large set of labels, items, or documents, which is common to information retrieval, recommendation, and large-scale annotation tasks. We present a general approach for converting an algorithm which has linear time in the size of the set to a sublinear one via label partitioning. O... | [] | null | 95 | null | null | [
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-0.04492057114839554,
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0.022432418540120125,
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-0.02066430076956749,
0.011278180405497551,
-0.06303565204143524,... |
Subproblem-Tree Calibration: A Unified Approach to Max-Product Message Passing | https://proceedings.mlr.press/v28/wang13b.html | [
"Huayan Wang",
"Koller Daphne"
] | null | null | Max-product (max-sum) message passing algorithms are widely used for MAP inference in MRFs. It has many variants sharing a common flavor of passing "messages" over some graph-object. Recent advances revealed that its convergent versions (such as MPLP, MSD, TRW-S) can be viewed as performing block coordinate descent (BC... | [] | null | 96 | null | null | [
-0.026705622673034668,
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0.0349055640399456,
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-0.04563404247164726,
0.012169701978564262,
-0.01635221764445305,
-0.0741945281624794,
0.009... |
Collaborative hyperparameter tuning | https://proceedings.mlr.press/v28/bardenet13.html | [
"Rémi Bardenet",
"Mátyás Brendel",
"Balázs Kégl",
"Michèle Sebag"
] | null | null | Hyperparameter learning has traditionally been a manual task because of the limited number of trials. Today’s computing infrastructures allow bigger evaluation budgets, thus opening the way for algorithmic approaches. Recently, surrogate-based optimization was successfully applied to hyperparameter learning for deep be... | [] | null | 97 | null | null | [
-0.000075713345722761,
-0.03832869604229927,
-0.013189062476158142,
0.039326686412096024,
0.014194684103131294,
0.03834269940853119,
0.06853342801332474,
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0.01798289828002453,
-0.05317485332489014,
0.009100525639951229,
0.0009260636288672686,
-0.04275527223944664,
-0.0... |
SADA: A General Framework to Support Robust Causation Discovery | https://proceedings.mlr.press/v28/cai13.html | [
"Ruichu Cai",
"Zhenjie Zhang",
"Zhifeng Hao"
] | null | null | Causality discovery without manipulation is considered a crucial problem to a variety of applications, such as genetic therapy. The state-of-the-art solutions, e.g. LiNGAM, return accurate results when the number of labeled samples is larger than the number of variables. These approaches are thus applicable only when l... | [] | null | 98 | null | null | [
0.004369412083178759,
-0.01984107866883278,
-0.037902865558862686,
0.029949529096484184,
0.044759418815374374,
0.04292618855834007,
0.04424115642905235,
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-0.04204750061035156,
0.029093973338603973,
0.0019770690705627203,
-0.06206655874848366,
0... |
Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines | https://proceedings.mlr.press/v28/sohn13.html | [
"Kihyuk Sohn",
"Guanyu Zhou",
"Chansoo Lee",
"Honglak Lee"
] | null | null | Unsupervised feature learning has emerged as a promising tool in learning representations from unlabeled data. However, it is still challenging to learn useful high-level features when the data contains a significant amount of irrelevant patterns. Although feature selection can be used for such complex data, it may fai... | [] | null | 99 | null | null | [
0.000030027007596800104,
-0.019219936802983284,
0.0020189438946545124,
0.01925213634967804,
0.026447227224707603,
0.015074191614985466,
0.01694810576736927,
-0.0256227795034647,
-0.0025112167932093143,
-0.032258741557598114,
-0.013738890178501606,
-0.008845881558954716,
-0.06332361698150635,... |
Sequential Bayesian Search | https://proceedings.mlr.press/v28/wen13.html | [
"Zheng Wen",
"Branislav Kveton",
"Brian Eriksson",
"Sandilya Bhamidipati"
] | null | null | Millions of people search daily for movies, music, and books on the Internet. Unfortunately, non-personalized exploration of items can result in an infeasible number of costly interaction steps. We study the problem of efficient, repeated interactive search. In this problem, the user is navigated to the items of intere... | [] | null | 100 | null | null | [
-0.017155364155769348,
0.003914672415703535,
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0.014148915186524391,
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-0.029999185353517532,
-0.03500989452004433,
0.037841252982616425,
-0.04395272955298424,
-0... |
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