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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
A Discriminative Latent Variable Model for Online Clustering | https://proceedings.mlr.press/v32/samdani14.html | [
"Rajhans Samdani",
"Kai-Wei Chang",
"Dan Roth"
] | null | null | This paper presents a latent variable structured prediction model for discriminative supervised clustering of items called the Latent Left-linking Model (L3M). We present an online clustering algorithm for L3M based on a feature-based item similarity function. We provide a learning framework for estimating the similari... | [] | null | 1 | null | null | [
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Kernel Mean Estimation and Stein Effect | https://proceedings.mlr.press/v32/muandet14.html | [
"Krikamol Muandet",
"Kenji Fukumizu",
"Bharath Sriperumbudur",
"Arthur Gretton",
"Bernhard Schoelkopf"
] | null | null | A mean function in reproducing kernel Hilbert space (RKHS), or a kernel mean, is an important part of many algorithms ranging from kernel principal component analysis to Hilbert-space embedding of distributions. Given a finite sample, an empirical average is the standard estimate for the true kernel mean. We show that ... | [] | null | 2 | null | null | [
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Demystifying Information-Theoretic Clustering | https://proceedings.mlr.press/v32/steeg14.html | [
"Greg Ver Steeg",
"Aram Galstyan",
"Fei Sha",
"Simon DeDeo"
] | null | null | We propose a novel method for clustering data which is grounded in information-theoretic principles and requires no parametric assumptions. Previous attempts to use information theory to define clusters in an assumption-free way are based on maximizing mutual information between data and cluster labels. We demonstrate... | [] | null | 3 | 1310.4210 | title_snapshot | [
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Covering Number for Efficient Heuristic-based POMDP Planning | https://proceedings.mlr.press/v32/zhanga14.html | [
"Zongzhang Zhang",
"David Hsu",
"Wee Sun Lee"
] | null | null | The difficulty of POMDP planning depends on the size of the search space involved. Heuristics are often used to reduce the search space size and improve computational efficiency; however, there are few theoretical bounds on their effectiveness. In this paper, we use the covering number to characterize the size of the ... | [] | null | 4 | null | null | [
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The Coherent Loss Function for Classification | https://proceedings.mlr.press/v32/yanga14.html | [
"Wenzhuo Yang",
"Melvyn Sim",
"Huan Xu"
] | null | null | A prediction rule in binary classification that aims to achieve the lowest probability of misclassification involves minimizing over a non-convex, 0-1 loss function, which is typically a computationally intractable optimization problem. To address the intractability, previous methods consider minimizing the cumulative ... | [] | null | 5 | null | null | [
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Fast Stochastic Alternating Direction Method of Multipliers | https://proceedings.mlr.press/v32/zhong14.html | [
"Wenliang Zhong",
"James Kwok"
] | null | null | We propose a new stochastic alternating direction method of multipliers (ADMM) algorithm, which incrementally approximates the full gradient in the linearized ADMM formulation. Besides having a low per-iteration complexity as existing stochastic ADMM algorithms, it improves the convergence rate on convex problems from... | [] | null | 6 | 1308.3558 | title_snapshot | [
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Active Detection via Adaptive Submodularity | https://proceedings.mlr.press/v32/chena14.html | [
"Yuxin Chen",
"Hiroaki Shioi",
"Cesar Fuentes Montesinos",
"Lian Pin Koh",
"Serge Wich",
"Andreas Krause"
] | null | null | Efficient detection of multiple object instances is one of the fundamental challenges in computer vision. For certain object categories, even the best automatic systems are yet unable to produce high-quality detection results, and fully manual annotation would be an expensive process. How can detection algorithms inter... | [] | null | 7 | null | null | [
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Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization | https://proceedings.mlr.press/v32/shalev-shwartz14.html | [
"Shai Shalev-Shwartz",
"Tong Zhang"
] | null | null | We introduce a proximal version of the stochastic dual coordinate ascent method and show how to accelerate the method using an inner-outer iteration procedure. We analyze the runtime of the framework and obtain rates that improve state-of-the-art results for various key machine learning optimization problems including ... | [] | null | 8 | 1309.2375 | title_snapshot | [
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An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization | https://proceedings.mlr.press/v32/lin14.html | [
"Qihang Lin",
"Lin Xiao"
] | null | null | We first propose an adaptive accelerated proximal gradient(APG) method for minimizing strongly convex composite functions with unknown convexity parameters. This method incorporates a restarting scheme to automatically estimate the strong convexity parameter and achieves a nearly optimal iteration complexity. Then we c... | [] | null | 9 | null | null | [
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Recurrent Convolutional Neural Networks for Scene Labeling | https://proceedings.mlr.press/v32/pinheiro14.html | [
"Pedro Pinheiro",
"Ronan Collobert"
] | null | null | The goal of the scene labeling task is to assign a class label to each pixel in an image. To ensure a good visual coherence and a high class accuracy, it is essential for a model to capture long range pixel) label dependencies in images. In a feed-forward architecture, this can be achieved simply by considering a suf... | [] | null | 10 | null | null | [
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A Statistical Perspective on Algorithmic Leveraging | https://proceedings.mlr.press/v32/ma14.html | [
"Ping Ma",
"Michael Mahoney",
"Bin Yu"
] | null | null | One popular method for dealing with large-scale data sets is sampling. Using the empirical statistical leverage scores as an importance sampling distribution, the method of algorithmic leveraging samples and rescales rows/columns of data matrices to reduce the data size before performing computations on the subproblem.... | [] | null | 11 | 1306.5362 | title_snapshot | [
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Thompson Sampling for Complex Online Problems | https://proceedings.mlr.press/v32/gopalan14.html | [
"Aditya Gopalan",
"Shie Mannor",
"Yishay Mansour"
] | null | null | We consider stochastic multi-armed bandit problems with complex actions over a set of basic arms, where the decision maker plays a complex action rather than a basic arm in each round. The reward of the complex action is some function of the basic arms’ rewards, and the feedback observed may not necessarily be the rewa... | [] | null | 12 | null | null | [
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Boosting multi-step autoregressive forecasts | https://proceedings.mlr.press/v32/taieb14.html | [
"Souhaib Ben Taieb",
"Rob Hyndman"
] | null | null | Multi-step forecasts can be produced recursively by iterating a one-step model, or directly using a specific model for each horizon. Choosing between these two strategies is not an easy task since it involves a trade-off between bias and estimation variance over the forecast horizon. Using a nonlinear machine learning ... | [] | null | 13 | null | null | [
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A Statistical Convergence Perspective of Algorithms for Rank Aggregation from Pairwise Data | https://proceedings.mlr.press/v32/rajkumar14.html | [
"Arun Rajkumar",
"Shivani Agarwal"
] | null | null | There has been much interest recently in the problem of rank aggregation from pairwise data. A natural question that arises is: under what sorts of statistical assumptions do various rank aggregation algorithms converge to an ‘optimal’ ranking? In this paper, we consider this question in a natural setting where pairwis... | [] | null | 14 | null | null | [
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Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations | https://proceedings.mlr.press/v32/mann14.html | [
"Timothy Mann",
"Shie Mannor"
] | null | null | We show how options, a class of control structures encompassing primitive and temporally extended actions, can play a valuable role in planning in MDPs with continuous state-spaces. Analyzing the convergence rate of Approximate Value Iteration with options reveals that for pessimistic initial value function estimates, ... | [] | null | 15 | null | null | [
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Latent Bandits. | https://proceedings.mlr.press/v32/maillard14.html | [
"Odalric-Ambrym Maillard",
"Shie Mannor"
] | null | null | We consider a multi-armed bandit problem where the reward distributions are indexed by two sets –one for arms, one for type– and can be partitioned into a small number of clusters according to the type. First, we consider the setting where all reward distributions are known and all types have the same underlying cluste... | [] | null | 16 | null | null | [
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Fast Allocation of Gaussian Process Experts | https://proceedings.mlr.press/v32/nguyena14.html | [
"Trung Nguyen",
"Edwin Bonilla"
] | null | null | We propose a scalable nonparametric Bayesian regression model based on a mixture of Gaussian process (GP) experts and the inducing points formalism underpinning sparse GP approximations. Each expert is augmented with a set of inducing points, and the allocation of data points to experts is defined probabilistically ba... | [] | null | 17 | null | null | [
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Von Mises-Fisher Clustering Models | https://proceedings.mlr.press/v32/gopal14.html | [
"Siddharth Gopal",
"Yiming Yang"
] | null | null | This paper proposes a suite of models for clustering high-dimensional data on a unit sphere based on Von Mises-Fisher (vMF) distribution and for discovering more intuitive clusters than existing approaches. The proposed models include a) A Bayesian formulation of vMF mixture that enables information sharing among clus... | [] | null | 18 | null | null | [
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Convergence rates for persistence diagram estimation in Topological Data Analysis | https://proceedings.mlr.press/v32/chazal14.html | [
"Frédéric Chazal",
"Marc Glisse",
"Catherine Labruère",
"Bertrand Michel"
] | null | null | Computational topology has recently seen an important development toward data analysis, giving birth to Topological Data Analysis. Persistent homology appears as a fundamental tool in this field. We show that the use of persistent homology can be naturally considered in general statistical frameworks. We establish co... | [] | null | 19 | null | null | [
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Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs | https://proceedings.mlr.press/v32/gieseke14.html | [
"Fabian Gieseke",
"Justin Heinermann",
"Cosmin Oancea",
"Christian Igel"
] | null | null | We present a new approach for combining k-d trees and graphics processing units for nearest neighbor search. It is well known that a direct combination of these tools leads to a non-satisfying performance due to conditional computations and suboptimal memory accesses. To alleviate these problems, we propose a variant o... | [] | null | 20 | null | null | [
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Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget | https://proceedings.mlr.press/v32/korattikara14.html | [
"Anoop Korattikara",
"Yutian Chen",
"Max Welling"
] | null | null | Can we make Bayesian posterior MCMC sampling more efficient when faced with very large datasets? We argue that computing the likelihood for N datapoints in the Metropolis-Hastings (MH) test to reach a single binary decision is computationally inefficient. We introduce an approximate MH rule based on a sequential hypoth... | [] | null | 21 | 1304.5299 | title_snapshot | [
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Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis | https://proceedings.mlr.press/v32/tang14.html | [
"Jian Tang",
"Zhaoshi Meng",
"Xuanlong Nguyen",
"Qiaozhu Mei",
"Ming Zhang"
] | null | null | Topic models such as the latent Dirichlet allocation (LDA) have become a standard staple in the modeling toolbox of machine learning. They have been applied to a vast variety of data sets, contexts, and tasks to varying degrees of success. However, to date there is almost no formal theory explicating the LDA’s behavior... | [] | null | 22 | null | null | [
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The Inverse Regression Topic Model | https://proceedings.mlr.press/v32/rabinovich14.html | [
"Maxim Rabinovich",
"David Blei"
] | null | null | \citettaddy13mnir proposed multinomial inverse regression (MNIR) as a new model of annotated text based on the influence of metadata and response variables on the distribution of words in a document. While effective, MNIR has no way to exploit structure in the corpus to improve its predictions or facilitate exploratory... | [] | null | 23 | null | null | [
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A Consistent Histogram Estimator for Exchangeable Graph Models | https://proceedings.mlr.press/v32/chan14.html | [
"Stanley Chan",
"Edoardo Airoldi"
] | null | null | Exchangeable graph models (ExGM) subsume a number of popular network models. The mathematical object that characterizes an ExGM is termed a graphon. Finding scalable estimators of graphons, provably consistent, remains an open issue. In this paper, we propose a histogram estimator of a graphon that is provably consist... | [] | null | 24 | 1402.1888 | title_snapshot | [
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Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data | https://proceedings.mlr.press/v32/letham14.html | [
"Benjamin Letham",
"Wei Sun",
"Anshul Sheopuri"
] | null | null | Bundle discounts are used by retailers in many industries. Optimal bundle pricing requires learning the joint distribution of consumer valuations for the items in the bundle, that is, how much they are willing to pay for each of the items. We suppose that a retailer has sales transaction data, and the corresponding con... | [] | null | 25 | null | null | [
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Towards Minimax Online Learning with Unknown Time Horizon | https://proceedings.mlr.press/v32/luo14.html | [
"Haipeng Luo",
"Robert Schapire"
] | null | null | We consider online learning when the time horizon is unknown. We apply a minimax analysis, beginning with the fixed horizon case, and then moving on to two unknown-horizon settings, one that assumes the horizon is chosen randomly according to some distribution, and the other which allows the adversary full control over... | [] | null | 26 | 1307.8187 | title_snapshot | [
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Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball | https://proceedings.mlr.press/v32/miller14.html | [
"Andrew Miller",
"Luke Bornn",
"Ryan Adams",
"Kirk Goldsberry"
] | null | null | We develop a machine learning approach to represent and analyze the underlying spatial structure that governs shot selection among professional basketball players in the NBA. Typically, NBA players are discussed and compared in an heuristic, imprecise manner that relies on unmeasured intuitions about player behavior. ... | [] | null | 27 | 1401.0942 | title_snapshot | [
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Margins, Kernels and Non-linear Smoothed Perceptrons | https://proceedings.mlr.press/v32/ramdas14.html | [
"Aaditya Ramdas",
"Javier Peña"
] | null | null | We focus on the problem of finding a non-linear classification function that lies in a Reproducing Kernel Hilbert Space (RKHS) both from the primal point of view (finding a perfect separator when one exists) and the dual point of view (giving a certificate of non-existence), with special focus on generalizations of two... | [] | null | 28 | 1505.04123 | title_snapshot | [
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Robust RegBayes: Selectively Incorporating First-Order Logic Domain Knowledge into Bayesian Models | https://proceedings.mlr.press/v32/mei14.html | [
"Shike Mei",
"Jun Zhu",
"Jerry Zhu"
] | null | null | Much research in Bayesian modeling has been done to elicit a prior distribution that incorporates domain knowledge. We present a novel and more direct approach by imposing First-Order Logic (FOL) rules on the posterior distribution. Our approach unifies FOL and Bayesian modeling under the regularized Bayesian framework... | [] | null | 29 | null | null | [
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Learning Theory and Algorithms for revenue optimization in second price auctions with reserve | https://proceedings.mlr.press/v32/mohri14.html | [
"Mehryar Mohri",
"Andres Munoz Medina"
] | null | null | Second-price auctions with reserve play a critical role for modern search engine and popular online sites since the revenue of these companies often directly depends on the outcome of such auctions. The choice of the reserve price is the main mechanism through which the auction revenue can be influenced in the... | [] | null | 30 | 1310.5665 | title_snapshot | [
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Low-density Parity Constraints for Hashing-Based Discrete Integration | https://proceedings.mlr.press/v32/ermon14.html | [
"Stefano Ermon",
"Carla Gomes",
"Ashish Sabharwal",
"Bart Selman"
] | null | null | In recent years, a number of probabilistic inference and counting techniques have been proposed that exploit pairwise independent hash functions to infer properties of succinctly defined high-dimensional sets. While providing desirable statistical guarantees, typical constructions of such hash functions are themselves ... | [] | null | 31 | null | null | [
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Prediction with Limited Advice and Multiarmed Bandits with Paid Observations | https://proceedings.mlr.press/v32/seldin14.html | [
"Yevgeny Seldin",
"Peter Bartlett",
"Koby Crammer",
"Yasin Abbasi-Yadkori"
] | null | null | We study two problems of online learning under restricted information access. In the first problem, \emphprediction with limited advice, we consider a game of prediction with expert advice, where on each round of the game we query the advice of a subset of M out of N experts. We present an algorithm that achieves O(\sq... | [] | null | 32 | null | null | [
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Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts | https://proceedings.mlr.press/v32/nguyenb14.html | [
"Tien Vu Nguyen",
"Dinh Phung",
"Xuanlong Nguyen",
"Swetha Venkatesh",
"Hung Bui"
] | null | null | We present a Bayesian nonparametric framework for multilevel clustering which utilizes group-level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters. Using the Dirichlet process as the building block, our model constructs a product base-m... | [] | null | 33 | 1401.1974 | title_snapshot | [
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Large-Margin Metric Learning for Constrained Partitioning Problems | https://proceedings.mlr.press/v32/lajugie14.html | [
"Rémi Lajugie",
"Francis Bach",
"Sylvain Arlot"
] | null | null | We consider unsupervised partitioning problems based explicitly or implicitly on the minimization of Euclidean distortions, such as clustering, image or video segmentation, and other change-point detection problems. We emphasize on cases with specific structure, which include many practical situations ranging from mean... | [] | null | 34 | null | null | [
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Wasserstein Propagation for Semi-Supervised Learning | https://proceedings.mlr.press/v32/solomon14.html | [
"Justin Solomon",
"Raif Rustamov",
"Leonidas Guibas",
"Adrian Butscher"
] | null | null | Probability distributions and histograms are natural representations for product ratings, traffic measurements, and other data considered in many machine learning applications. Thus, this paper introduces a technique for graph-based semi-supervised learning of histograms, derived from the theory of optimal transportat... | [] | null | 35 | null | null | [
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Max-Margin Infinite Hidden Markov Models | https://proceedings.mlr.press/v32/zhangb14.html | [
"Aonan Zhang",
"Jun Zhu",
"Bo Zhang"
] | null | null | Infinite hidden Markov models (iHMMs) are nonparametric Bayesian extensions of hidden Markov models (HMMs) with an infinite number of states. Though flexible in describing sequential data, the generative formulation of iHMMs could limit their discriminative ability in sequential prediction tasks. Our paper introduces m... | [] | null | 36 | null | null | [
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Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence Function | https://proceedings.mlr.press/v32/liua14.html | [
"Yong Liu",
"Shali Jiang",
"Shizhong Liao"
] | null | null | Model selection is one of the key issues both in recent research and application of kernel methods. Cross-validation is a commonly employed and widely accepted model selection criterion. However, it requires multiple times of training the algorithm under consideration, which is computationally intensive. In this paper,... | [] | null | 37 | null | null | [
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Generalized Exponential Concentration Inequality for Renyi Divergence Estimation | https://proceedings.mlr.press/v32/singh14.html | [
"Shashank Singh",
"Barnabas Poczos"
] | null | null | Estimating divergences between probability distributions in a consistent way is of great importance in many machine learning tasks. Although this is a fundamental problem in nonparametric statistics, to the best of our knowledge there has been no finite sample exponential inequality convergence bound derived for any di... | [] | null | 38 | 1603.08589 | title_snapshot | [
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Boosting with Online Binary Learners for the Multiclass Bandit Problem | https://proceedings.mlr.press/v32/chenb14.html | [
"Shang-Tse Chen",
"Hsuan-Tien Lin",
"Chi-Jen Lu"
] | null | null | We consider the problem of online multiclass prediction in the bandit setting. Compared with the full-information setting, in which the learner can receive the true label as feedback after making each prediction, the bandit setting assumes that the learner can only know the correctness of the predicted label. Because t... | [] | null | 39 | null | null | [
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Optimal Budget Allocation: Theoretical Guarantee and Efficient Algorithm | https://proceedings.mlr.press/v32/soma14.html | [
"Tasuku Soma",
"Naonori Kakimura",
"Kazuhiro Inaba",
"Ken-ichi Kawarabayashi"
] | null | null | We consider the budget allocation problem over bipartite influence model proposed by Alon et al. This problem can be viewed as the well-known influence maximization problem with budget constraints. We first show that this problem and its much more general form fall into a general setting; namely the monotone submo... | [] | null | 40 | null | null | [
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Computing Parametric Ranking Models via Rank-Breaking | https://proceedings.mlr.press/v32/soufiani14.html | [
"Hossein Azari Soufiani",
"David Parkes",
"Lirong Xia"
] | null | null | Rank breaking is a methodology introduced by Azari Soufiani et al. (2013a) for applying a Generalized Method of Moments (GMM) algorithm to the estimation of parametric ranking models. Breaking takes full rankings and breaks, or splits them up, into counts for pairs of alternatives that occur in particular positions (e.... | [] | null | 41 | null | null | [
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Tracking Adversarial Targets | https://proceedings.mlr.press/v32/abbasi-yadkori14.html | [
"Yasin Abbasi-Yadkori",
"Peter Bartlett",
"Varun Kanade"
] | null | null | We study linear control problems with quadratic losses and adversarially chosen tracking targets. We present an efficient algorithm for this problem and show that, under standard conditions on the linear system, its regret with respect to an optimal linear policy grows as O(\log^2 T), where T is the number of rounds of... | [] | null | 42 | null | null | [
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Online Bayesian Passive-Aggressive Learning | https://proceedings.mlr.press/v32/shi14.html | [
"Tianlin Shi",
"Jun Zhu"
] | null | null | Online Passive-Aggressive (PA) learning is an effective framework for performing max-margin online learning. But the deterministic formulation and estimated single large-margin model could limit its capability in discovering descriptive structures underlying complex data. This paper presents online Bayesian Passive-Agg... | [] | null | 43 | 1312.3388 | title_snapshot | [
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Deterministic Policy Gradient Algorithms | https://proceedings.mlr.press/v32/silver14.html | [
"David Silver",
"Guy Lever",
"Nicolas Heess",
"Thomas Degris",
"Daan Wierstra",
"Martin Riedmiller"
] | null | null | In this paper we consider deterministic policy gradient algorithms for reinforcement learning with continuous actions. The deterministic policy gradient has a particularly appealing form: it is the expected gradient of the action-value function. This simple form means that the deterministic policy gradient can be estim... | [] | null | 44 | null | null | [
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Modeling Correlated Arrival Events with Latent Semi-Markov Processes | https://proceedings.mlr.press/v32/lian14.html | [
"Wenzhao Lian",
"Vinayak Rao",
"Brian Eriksson",
"Lawrence Carin"
] | null | null | The analysis and characterization of correlated point process data has wide applications, ranging from biomedical research to network analysis. In this work, we model such data as generated by a latent collection of continuous-time binary semi-Markov processes, corresponding to external events appearing and disappearin... | [] | null | 45 | null | null | [
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Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach | https://proceedings.mlr.press/v32/bardenet14.html | [
"Rémi Bardenet",
"Arnaud Doucet",
"Chris Holmes"
] | null | null | Markov chain Monte Carlo (MCMC) methods are often deemed far too computationally intensive to be of any practical use for large datasets. This paper describes a methodology that aims to scale up the Metropolis-Hastings (MH) algorithm in this context. We propose an approximate implementation of the accept/reject step of... | [] | null | 46 | null | null | [
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Diagnosis determination: decision trees optimizing simultaneously worst and expected testing cost | https://proceedings.mlr.press/v32/cicalese14.html | [
"Ferdinando Cicalese",
"Eduardo Laber",
"Aline Medeiros Saettler"
] | null | null | In several applications of automatic diagnosis and active learning a central problem is the evaluation of a discrete function by adaptively querying the values of its variables until the values read uniquely determine the value of the function. In general reading the value of a variable is done at the expense of som... | [] | null | 47 | 1309.2796 | title_judge | [
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Condensed Filter Tree for Cost-Sensitive Multi-Label Classification | https://proceedings.mlr.press/v32/lia14.html | [
"Chun-Liang Li",
"Hsuan-Tien Lin"
] | null | null | Different real-world applications of multi-label classification often demand different evaluation criteria. We formalize this demand with a general setup, cost-sensitive multi-label classification (CSMLC), which takes the evaluation criteria into account during learning. Nevertheless, most existing algorithms can only... | [] | null | 48 | null | null | [
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On Measure Concentration of Random Maximum A-Posteriori Perturbations | https://proceedings.mlr.press/v32/orabona14.html | [
"Francesco Orabona",
"Tamir Hazan",
"Anand Sarwate",
"Tommi Jaakkola"
] | null | null | The maximum a-posteriori (MAP) perturbation framework has emerged as a useful approach for inference and learning in high dimensional complex models. By maximizing a randomly perturbed potential function, MAP perturbations generate unbiased samples from the Gibbs distribution. Unfortunately, the computational cost of... | [] | null | 49 | 1310.4227 | title_snapshot | [
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Bias in Natural Actor-Critic Algorithms | https://proceedings.mlr.press/v32/thomas14.html | [
"Philip Thomas"
] | null | null | We show that several popular discounted reward natural actor-critics, including the popular NAC-LSTD and eNAC algorithms, do not generate unbiased estimates of the natural policy gradient as claimed. We derive the first unbiased discounted reward natural actor-critics using batch and iterative approaches to gradient es... | [] | null | 50 | null | null | [
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Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning | https://proceedings.mlr.press/v32/denis14.html | [
"François Denis",
"Mattias Gybels",
"Amaury Habrard"
] | null | null | Learning probabilistic models over strings is an important issue for many applications. Spectral methods propose elegant solutions to the problem of inferring weighted automata from finite samples of variable-length strings drawn from an unknown target distribution. These methods rely on a singular value decomposition ... | [] | null | 51 | 1312.6282 | title_snapshot | [
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0.0... |
On Modelling Non-linear Topical Dependencies | https://proceedings.mlr.press/v32/lib14.html | [
"Zhixing Li",
"Siqiang Wen",
"Juanzi Li",
"Peng Zhang",
"Jie Tang"
] | null | null | Probabilistic topic models such as Latent Dirichlet Allocation (LDA) discover latent topics from large corpora by exploiting words’ co-occurring relation. By observing the topical similarity between words, we find that some other relations, such as semantic or syntax relation between words, lead to strong dependence be... | [] | null | 52 | null | null | [
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A Deep and Tractable Density Estimator | https://proceedings.mlr.press/v32/uria14.html | [
"Benigno Uria",
"Iain Murray",
"Hugo Larochelle"
] | null | null | The Neural Autoregressive Distribution Estimator (NADE) and its real-valued version RNADE are competitive density models of multidimensional data across a variety of domains. These models use a fixed, arbitrary ordering of the data dimensions. One can easily condition on variables at the beginning of the ordering, and... | [] | null | 53 | 1310.1757 | title_snapshot | [
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(Near) Dimension Independent Risk Bounds for Differentially Private Learning | https://proceedings.mlr.press/v32/jain14.html | [
"Prateek Jain",
"Abhradeep Guha Thakurta"
] | null | null | In this paper, we study the problem of differentially private risk minimization where the goal is to provide differentially private algorithms that have small excess risk. In particular we address the following open problem: \emphIs it possible to design computationally efficient differentially private risk minimizers ... | [] | null | 54 | null | null | [
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Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels | https://proceedings.mlr.press/v32/yangb14.html | [
"Jiyan Yang",
"Vikas Sindhwani",
"Haim Avron",
"Michael Mahoney"
] | null | null | We consider the problem of improving the efficiency of randomized Fourier feature maps to accelerate training and testing speed of kernel methods on large datasets. These approximate feature maps arise as Monte Carlo approximations to integral representations of shift-invariant kernel functions (e.g., Gaussian kernel).... | [] | null | 55 | 1412.8293 | title_snapshot | [
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Discriminative Features via Generalized Eigenvectors | https://proceedings.mlr.press/v32/karampatziakis14.html | [
"Nikos Karampatziakis",
"Paul Mineiro"
] | null | null | Representing examples in a way that is compatible with the underlying classifier can greatly enhance the performance of a learning system. In this paper we investigate scalable techniques for inducing discriminative features by taking advantage of simple second order structure in the data. We focus on multiclass classi... | [] | null | 56 | 1310.1934 | title_snapshot | [
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Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint | https://proceedings.mlr.press/v32/liub14.html | [
"Ji Liu",
"Jieping Ye",
"Ryohei Fujimaki"
] | null | null | We consider forward-backward greedy algorithms for solving sparse feature selection problems with general convex smooth functions. A state-of-the-art greedy method, the Forward-Backward greedy algorithm (FoBa-obj) requires to solve a large number of optimization problems, thus it is not scalable for large-size problems... | [] | null | 57 | 1401.0086 | title_snapshot | [
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Online Learning in Markov Decision Processes with Changing Cost Sequences | https://proceedings.mlr.press/v32/dick14.html | [
"Travis Dick",
"Andras Gyorgy",
"Csaba Szepesvari"
] | null | null | In this paper we consider online learning in finite Markov decision processes (MDPs) with changing cost sequences under full and bandit-information. We propose to view this problem as an instance of online linear optimization. We propose two methods for this problem: MD^2 (mirror descent with approximate projections)... | [] | null | 58 | null | null | [
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Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms | https://proceedings.mlr.press/v32/combes14.html | [
"Richard Combes",
"Alexandre Proutiere"
] | null | null | We consider stochastic multi-armed bandits where the expected reward is a unimodal function over partially ordered arms. This important class of problems has been recently investigated in (Cope 2009, Yu 2011). The set of arms is either discrete, in which case arms correspond to the vertices of a finite graph whose stru... | [] | null | 59 | 1405.5096 | title_snapshot | [
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Maximum Mean Discrepancy for Class Ratio Estimation: Convergence Bounds and Kernel Selection | https://proceedings.mlr.press/v32/iyer14.html | [
"Arun Iyer",
"Saketha Nath",
"Sunita Sarawagi"
] | null | null | In recent times, many real world applications have emerged that require estimates of class ratios in an unlabeled instance collection as opposed to labels of individual instances in the collection. In this paper we investigate the use of maximum mean discrepancy (MMD) in a reproducing kernel Hilbert space (RKHS) for e... | [] | null | 60 | null | null | [
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Asymptotically consistent estimation of the number of change points in highly dependent time series | https://proceedings.mlr.press/v32/khaleghi14.html | [
"Azadeh Khaleghi",
"Daniil Ryabko"
] | null | null | The problem of change point estimation is considered in a general framework where the data are generated by arbitrary unknown stationary ergodic process distributions. This means that the data may have long-range dependencies of an arbitrary form. In this context the consistent estimation of the number of change poin... | [] | null | 61 | 1302.3407 | title_judge | [
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Coordinate-descent for learning orthogonal matrices through Givens rotations | https://proceedings.mlr.press/v32/shalit14.html | [
"Uri Shalit",
"Gal Chechik"
] | null | null | Optimizing over the set of orthogonal matrices is a central component in problems like sparse-PCA or tensor decomposition. Unfortunately, such optimization is hard since simple operations on orthogonal matrices easily break orthogonality, and correcting orthogonality usually costs a large amount of computation. Here w... | [] | null | 62 | 1312.0624 | title_judge | [
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Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search | https://proceedings.mlr.press/v32/shrivastava14.html | [
"Anshumali Shrivastava",
"Ping Li"
] | null | null | The query complexity of \em locality sensitive hashing (LSH) based similarity search is dominated by the number of hash evaluations, and this number grows with the data size \citeProc:Indyk_STOC98. In industrial applications such as search where the data are often high-dimensional and binary (e.g., text n-grams), \em ... | [] | null | 63 | null | null | [
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A Divide-and-Conquer Solver for Kernel Support Vector Machines | https://proceedings.mlr.press/v32/hsieha14.html | [
"Cho-Jui Hsieh",
"Si Si",
"Inderjit Dhillon"
] | null | null | The kernel support vector machine (SVM) is one of the most widely used classification methods; however, the amount of computation required becomes the bottleneck when facing millions of samples. In this paper, we propose and analyze a novel divide-and-conquer solver for kernel SVMs (DC-SVM). In the division step, we pa... | [] | null | 64 | 1311.0914 | title_snapshot | [
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Nuclear Norm Minimization via Active Subspace Selection | https://proceedings.mlr.press/v32/hsiehb14.html | [
"Cho-Jui Hsieh",
"Peder Olsen"
] | null | null | We describe a novel approach to optimizing matrix problems involving nuclear norm regularization and apply it to the matrix completion problem. We combine methods from non-smooth and smooth optimization. At each step we use the proximal gradient to select an active subspace. We then find a smooth, convex relaxation of ... | [] | null | 65 | null | null | [
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Provable Bounds for Learning Some Deep Representations | https://proceedings.mlr.press/v32/arora14.html | [
"Sanjeev Arora",
"Aditya Bhaskara",
"Rong Ge",
"Tengyu Ma"
] | null | null | We give algorithms with provable guarantees that learn a class of deep nets in the generative model view popularized by Hinton and others. Our generative model is an n node multilayer neural net that has degree at most n^γ for some γ< 1 and each edge has a random edge weight in [-1,1]. Our algorithm learns almost all... | [] | null | 66 | 1310.6343 | title_snapshot | [
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Large-scale Multi-label Learning with Missing Labels | https://proceedings.mlr.press/v32/yu14.html | [
"Hsiang-Fu Yu",
"Prateek Jain",
"Purushottam Kar",
"Inderjit Dhillon"
] | null | null | The multi-label classification problem has generated significant interest in recent years. However, existing approaches do not adequately address two key challenges: (a) scaling up to problems with a large number (say millions) of labels, and (b) handling data with missing labels. In this paper, we directly address bot... | [] | null | 67 | 1307.5101 | title_snapshot | [
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Learning Graphs with a Few Hubs | https://proceedings.mlr.press/v32/tandon14.html | [
"Rashish Tandon",
"Pradeep Ravikumar"
] | null | null | We consider the problem of recovering the graph structure of a “hub-networked” Ising model given iid samples, under high-dimensional settings, where number of nodes p could be potentially larger than the number of samples n. By a “hub-networked” graph, we mean a graph with a few “hub nodes” with very large degrees. Sta... | [] | null | 68 | null | null | [
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Agnostic Bayesian Learning of Ensembles | https://proceedings.mlr.press/v32/lacoste14.html | [
"Alexandre Lacoste",
"Mario Marchand",
"François Laviolette",
"Hugo Larochelle"
] | null | null | We propose a method for producing ensembles of predictors based on holdout estimations of their generalization performances. This approach uses a prior directly on the performance of predictors taken from a finite set of candidates and attempts to infer which one is best. Using Bayesian inference, we can thus obtain a ... | [] | null | 69 | null | null | [
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Towards an optimal stochastic alternating direction method of multipliers | https://proceedings.mlr.press/v32/azadi14.html | [
"Samaneh Azadi",
"Suvrit Sra"
] | null | null | We study regularized stochastic convex optimization subject to linear equality constraints. This class of problems was recently also studied by Ouyang et al. (2013) and Suzuki (2013); both introduced similar stochastic alternating direction method of multipliers (SADMM) algorithms. However, the analysis of both papers ... | [] | null | 70 | null | null | [
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Spherical Hamiltonian Monte Carlo for Constrained Target Distributions | https://proceedings.mlr.press/v32/lan14.html | [
"Shiwei Lan",
"Bo Zhou",
"Babak Shahbaba"
] | null | null | Statistical models with constrained probability distributions are abundant in machine learning. Some examples include regression models with norm constraints (e.g., Lasso), probit models, many copula models, and Latent Dirichlet Allocation (LDA) models. Bayesian inference involving probability distributions confined to... | [] | null | 71 | 1309.4289 | title_snapshot | [
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Efficient Continuous-Time Markov Chain Estimation | https://proceedings.mlr.press/v32/hajiaghayi14.html | [
"Monir Hajiaghayi",
"Bonnie Kirkpatrick",
"Liangliang Wang",
"Alexandre Bouchard-Côté"
] | null | null | Many problems of practical interest rely on Continuous-time Markov chains (CTMCs) defined over combinatorial state spaces, rendering the computation of transition probabilities, and hence probabilistic inference, difficult or impossible with existing methods. For problems with countably infinite states, where classica... | [] | null | 72 | 1309.3250 | title_snapshot | [
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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition | https://proceedings.mlr.press/v32/donahue14.html | [
"Jeff Donahue",
"Yangqing Jia",
"Oriol Vinyals",
"Judy Hoffman",
"Ning Zhang",
"Eric Tzeng",
"Trevor Darrell"
] | null | null | We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be ins... | [] | null | 73 | 1310.1531 | title_snapshot | [
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Making the Most of Bag of Words: Sentence Regularization with Alternating Direction Method of Multipliers | https://proceedings.mlr.press/v32/yogatama14.html | [
"Dani Yogatama",
"Noah Smith"
] | null | null | In many high-dimensional learning problems, only some parts of an observation are important to the prediction task; for example, the cues to correctly categorizing a document may lie in a handful of its sentences. We introduce a learning algorithm that exploits this intuition by encoding it in a regularizer. Specifica... | [] | null | 74 | null | null | [
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Narrowing the Gap: Random Forests In Theory and In Practice | https://proceedings.mlr.press/v32/denil14.html | [
"Misha Denil",
"David Matheson",
"Nando De Freitas"
] | null | null | Despite widespread interest and practical use, the theoretical properties of random forests are still not well understood. In this paper we contribute to this understanding in two ways. We present a new theoreti- cally tractable variant of random regression forests and prove that our algorithm is con- sistent. We also ... | [] | null | 75 | 1310.1415 | title_snapshot | [
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Coherent Matrix Completion | https://proceedings.mlr.press/v32/chenc14.html | [
"Yudong Chen",
"Srinadh Bhojanapalli",
"Sujay Sanghavi",
"Rachel Ward"
] | null | null | Matrix completion concerns the recovery of a low-rank matrix from a subset of its revealed entries, and nuclear norm minimization has emerged as an effective surrogate for this combinatorial problem. Here, we show that nuclear norm minimization can recover an arbitrary n \times n matrix of rank r from O(nr log^2(n)) r... | [] | null | 76 | null | null | [
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Admixture of Poisson MRFs: A Topic Model with Word Dependencies | https://proceedings.mlr.press/v32/inouye14.html | [
"David Inouye",
"Pradeep Ravikumar",
"Inderjit Dhillon"
] | null | null | This paper introduces a new topic model based on an admixture of Poisson Markov Random Fields (APM), which can model dependencies between words as opposed to previous independent topic models such as PLSA (Hofmann, 1999), LDA (Blei et al., 2003) or SAM (Reisinger et al., 2010). We propose a class of admixture models th... | [] | null | 77 | null | null | [
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... |
True Online TD(lambda) | https://proceedings.mlr.press/v32/seijen14.html | [
"Harm Seijen",
"Rich Sutton"
] | null | null | TD(lambda) is a core algorithm of modern reinforcement learning. Its appeal comes from its equivalence to a clear and conceptually simple forward view, and the fact that it can be implemented online in an inexpensive manner. However, the equivalence between TD(lambda) and the forward view is exact only for the off-line... | [] | null | 78 | null | null | [
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Memory Efficient Kernel Approximation | https://proceedings.mlr.press/v32/si14.html | [
"Si Si",
"Cho-Jui Hsieh",
"Inderjit Dhillon"
] | null | null | The scalability of kernel machines is a big challenge when facing millions of samples due to storage and computation issues for large kernel matrices, that are usually dense. Recently, many papers have suggested tackling this problem by using a low rank approximation of the kernel matrix. In this paper, we first make t... | [] | null | 79 | null | null | [
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Learning Sum-Product Networks with Direct and Indirect Variable Interactions | https://proceedings.mlr.press/v32/rooshenas14.html | [
"Amirmohammad Rooshenas",
"Daniel Lowd"
] | null | null | Sum-product networks (SPNs) are a deep probabilistic representation that allows for efficient, exact inference. SPNs generalize many other tractable models, including thin junction trees, latent tree models, and many types of mixtures. Previous work on learning SPN structure has mainly focused on using top-down or bo... | [] | null | 80 | null | null | [
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Hamiltonian Monte Carlo Without Detailed Balance | https://proceedings.mlr.press/v32/sohl-dickstein14.html | [
"Jascha Sohl-Dickstein",
"Mayur Mudigonda",
"Michael DeWeese"
] | null | null | We present a method for performing Hamiltonian Monte Carlo that largely eliminates sample rejection. In situations that would normally lead to rejection, instead a longer trajectory is computed until a new state is reached that can be accepted. This is achieved using Markov chain transitions that satisfy the fixed po... | [] | null | 81 | 1409.5191 | title_snapshot | [
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Filtering with Abstract Particles | https://proceedings.mlr.press/v32/steinhardt14.html | [
"Jacob Steinhardt",
"Percy Liang"
] | null | null | Using particles, beam search and sequential Monte Carlo can approximate distributions in an extremely flexible manner. However, they can suffer from sparsity and inadequate coverage on large state spaces. We present a new filtering method that addresses this issue by using “abstract particles” that each represent an e... | [] | null | 82 | null | null | [
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Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers | https://proceedings.mlr.press/v32/suzuki14.html | [
"Taiji Suzuki"
] | null | null | We propose a new stochastic dual coordinate ascent technique that can be applied to a wide range of regularized learning problems. Our method is based on alternating direction method of multipliers (ADMM) to deal with complex regularization functions such as structured regularizations. Although the original ADMM is a... | [] | null | 83 | 1311.0622 | title_judge | [
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... |
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction | https://proceedings.mlr.press/v32/zhou14.html | [
"Jian Zhou",
"Olga Troyanskaya"
] | null | null | Predicting protein secondary structure is a fundamental problem in protein structure prediction. Here we present a new supervised generative stochastic network (GSN) based method to predict local secondary structure with deep hierarchical representations. GSN is a recently proposed deep learning technique (Bengio & Thi... | [] | null | 84 | 1403.1347 | title_snapshot | [
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0.03... |
An Efficient Approach for Assessing Hyperparameter Importance | https://proceedings.mlr.press/v32/hutter14.html | [
"Frank Hutter",
"Holger Hoos",
"Kevin Leyton-Brown"
] | null | null | The performance of many machine learning methods depends critically on hyperparameter settings. Sophisticated Bayesian optimization methods have recently achieved considerable successes in optimizing these hyperparameters, in several cases surpassing the performance of human experts. However, blind reliance on such met... | [] | null | 85 | null | null | [
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... |
An Information Geometry of Statistical Manifold Learning | https://proceedings.mlr.press/v32/suna14.html | [
"Ke Sun",
"Stéphane Marchand-Maillet"
] | null | null | Manifold learning seeks low-dimensional representations of high-dimensional data. The main tactics have been exploring the geometry in an input data space and an output embedding space. We develop a manifold learning theory in a hypothesis space consisting of models. A model means a specific instance of a collection of... | [] | null | 86 | null | null | [
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Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem | https://proceedings.mlr.press/v32/zoghi14.html | [
"Masrour Zoghi",
"Shimon Whiteson",
"Remi Munos",
"Maarten Rijke"
] | null | null | This paper proposes a new method for the K-armed dueling bandit problem, a variation on the regular K-armed bandit problem that offers only relative feedback about pairs of arms. Our approach extends the Upper Confidence Bound algorithm to the relative setting by using estimates of the pairwise probabilities to select ... | [] | null | 87 | 1312.3393 | title_snapshot | [
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... |
Compact Random Feature Maps | https://proceedings.mlr.press/v32/hamid14.html | [
"Raffay Hamid",
"Ying Xiao",
"Alex Gittens",
"Dennis Decoste"
] | null | null | Kernel approximation using randomized feature maps has recently gained a lot of interest. In this work, we identify that previous approaches for polynomial kernel approximation create maps that are rank deficient, and therefore do not utilize the capacity of the projected feature space effectively. To address this chal... | [] | null | 88 | 1312.4626 | title_snapshot | [
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0.0... |
Concentration in unbounded metric spaces and algorithmic stability | https://proceedings.mlr.press/v32/kontorovicha14.html | [
"Aryeh Kontorovich"
] | null | null | We prove an extension of McDiarmid’s inequality for metric spaces with unbounded diameter. To this end, we introduce the notion of the \em subgaussian diameter, which is a distribution-dependent refinement of the metric diameter. Our technique provides an alternative approach to that of Kutin and Niyogi’s method o... | [] | null | 89 | 1309.1007 | title_snapshot | [
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0... |
Heavy-tailed regression with a generalized median-of-means | https://proceedings.mlr.press/v32/hsu14.html | [
"Daniel Hsu",
"Sivan Sabato"
] | null | null | This work proposes a simple and computationally efficient estimator for linear regression, and other smooth and strongly convex loss minimization problems. We prove loss approximation guarantees that hold for general distributions, including those with heavy tails. All prior results only hold for estimators which ... | [] | null | 90 | null | null | [
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... |
Spectral Bandits for Smooth Graph Functions | https://proceedings.mlr.press/v32/valko14.html | [
"Michal Valko",
"Remi Munos",
"Branislav Kveton",
"Tomáš Kocák"
] | null | null | Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework is suitable for solving online learning problems that involve graphs, such as content-based recommendation. In this problem... | [] | null | 91 | 2604.18420 | title_snapshot | [
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Robust Principal Component Analysis with Complex Noise | https://proceedings.mlr.press/v32/zhao14.html | [
"Qian Zhao",
"Deyu Meng",
"Zongben Xu",
"Wangmeng Zuo",
"Lei Zhang"
] | null | null | The research on robust principal component analysis (RPCA) has been attracting much attention recently. The original RPCA model assumes sparse noise, and use the L_1-norm to characterize the error term. In practice, however, the noise is much more complex and it is not appropriate to simply use a certain L_p-norm for n... | [] | null | 92 | null | null | [
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0.011... |
Scalable Semidefinite Relaxation for Maximum A Posterior Estimation | https://proceedings.mlr.press/v32/huang14.html | [
"Qixing Huang",
"Yuxin Chen",
"Leonidas Guibas"
] | null | null | Maximum a posteriori (MAP) inference over discrete Markov random fields is a central task spanning a wide spectrum of real-world applications but known to be NP-hard for general graphs. In this paper, we propose a novel semidefinite relaxation formulation (referred to as SDR) to estimate the MAP assignment. Algorithmic... | [] | null | 93 | 1405.4807 | title_snapshot | [
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0.... |
Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery | https://proceedings.mlr.press/v32/mu14.html | [
"Cun Mu",
"Bo Huang",
"John Wright",
"Donald Goldfarb"
] | null | null | Recovering a low-rank tensor from incomplete information is a recurring problem in signal processing and machine learning. The most popular convex relaxation of this problem minimizes the sum of the nuclear norms (SNN) of the unfolding matrices of the tensor. We show that this approach can be substantially suboptimal: ... | [] | null | 94 | 1307.5870 | title_snapshot | [
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-... |
Automated inference of point of view from user interactions in collective intelligence venues | https://proceedings.mlr.press/v32/das14.html | [
"Sanmay Das",
"Allen Lavoie"
] | null | null | Empirical evaluation of trust and manipulation in large-scale collective intelligence processes is challenging. The datasets involved are too large for thorough manual study, and current automated options are limited. We introduce a statistical framework which classifies point of view based on user interactions. The fr... | [] | null | 95 | null | null | [
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... |
Rank-One Matrix Pursuit for Matrix Completion | https://proceedings.mlr.press/v32/wanga14.html | [
"Zheng Wang",
"Ming-Jun Lai",
"Zhaosong Lu",
"Wei Fan",
"Hasan Davulcu",
"Jieping Ye"
] | null | null | Low rank matrix completion has been applied successfully in a wide range of machine learning applications, such as collaborative filtering, image inpainting and Microarray data imputation. However, many existing algorithms are not scalable to large-scale problems, as they involve computing singular value decomposition.... | [] | null | 96 | 1404.1377 | title_judge | [
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Near-Optimal Joint Object Matching via Convex Relaxation | https://proceedings.mlr.press/v32/chend14.html | [
"Yuxin Chen",
"Leonidas Guibas",
"Qixing Huang"
] | null | null | Joint object matching aims at aggregating information from a large collection of similar instances (e.g. images, graphs, shapes) to improve the correspondences computed between pairs of objects, typically by exploiting global map compatibility. Despite some practical advances on this problem, from the theoretical point... | [] | null | 97 | 1402.1473 | title_snapshot | [
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-0.02285248227417469,
-0.08008375763893127,
-0.02... |
Convex Total Least Squares | https://proceedings.mlr.press/v32/malioutov14.html | [
"Dmitry Malioutov",
"Nikolai Slavov"
] | null | null | We study the total least squares (TLS) problem that generalizes least squares regression by allowing measurement errors in both dependent and independent variables. TLS is widely used in applied fields including computer vision, system identification and econometrics. The special case when all dependent and independen... | [] | null | 98 | 1406.0189 | title_snapshot | [
0.009058119729161263,
0.009209752082824707,
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-0.02765708602964878,
-0.00869676936417818,
-0.029861995950341225,
-0.05491770803928375,
0.0029... |
On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection | https://proceedings.mlr.press/v32/jawanpuria14.html | [
"Pratik Jawanpuria",
"Manik Varma",
"Saketha Nath"
] | null | null | Our objective is to develop formulations and algorithms for efficiently computing the feature selection path – i.e. the variation in classification accuracy as the fraction of selected features is varied from null to unity. Multiple Kernel Learning subject to l_p\geq1 regularization (l_p-MKL) has been demonstrated to b... | [] | null | 99 | null | null | [
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0.03204794228076935,
-0.05464544892311096,
-0.00... |
Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization | https://proceedings.mlr.press/v32/yuan14.html | [
"Xiaotong Yuan",
"Ping Li",
"Tong Zhang"
] | null | null | Hard Thresholding Pursuit (HTP) is an iterative greedy selection procedure for finding sparse solutions of underdetermined linear systems. This method has been shown to have strong theoretical guarantees and impressive numerical performance. In this paper, we generalize HTP from compressed sensing to a generic problem ... | [] | null | 100 | 1311.5750 | title_snapshot | [
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-0.05551567301154137,
-0.01202809065580368,
0.00007071933941915631,
-0.052354130893945694,
-0... |
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