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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
Stochastic Optimization with Importance Sampling for Regularized Loss Minimization | https://proceedings.mlr.press/v37/zhaoa15.html | [
"Peilin Zhao",
"Tong Zhang"
] | null | null | Uniform sampling of training data has been commonly used in traditional stochastic optimization algorithms such as Proximal Stochastic Mirror Descent (prox-SMD) and Proximal Stochastic Dual Coordinate Ascent (prox-SDCA). Although uniform sampling can guarantee that the sampled stochastic quantity is an unbiased estimat... | [] | null | 1 | null | null | [
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Approval Voting and Incentives in Crowdsourcing | https://proceedings.mlr.press/v37/shaha15.html | [
"Nihar Shah",
"Dengyong Zhou",
"Yuval Peres"
] | null | null | The growing need for labeled training data has made crowdsourcing an important part of machine learning. The quality of crowdsourced labels is, however, adversely affected by three factors: (1) the workers are not experts; (2) the incentives of the workers are not aligned with those of the requesters; and (3) the inter... | [] | null | 2 | 1502.05696 | title_snapshot | [
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A low variance consistent test of relative dependency | https://proceedings.mlr.press/v37/bounliphone15.html | [
"Wacha Bounliphone",
"Arthur Gretton",
"Arthur Tenenhaus",
"Matthew Blaschko"
] | null | null | We describe a novel non-parametric statistical hypothesis test of relative dependence between a source variable and two candidate target variables. Such a test enables us to determine whether one source variable is significantly more dependent on a first target variable or a second. Dependence is measured via the Hilbe... | [] | null | 3 | 1406.3852 | title_snapshot | [
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An Aligned Subtree Kernel for Weighted Graphs | https://proceedings.mlr.press/v37/bai15.html | [
"Lu Bai",
"Luca Rossi",
"Zhihong Zhang",
"Edwin Hancock"
] | null | null | In this paper, we develop a new entropic matching kernel for weighted graphs by aligning depth-based representations. We demonstrate that this kernel can be seen as an \textbfaligned subtree kernel that incorporates explicit subtree correspondences, and thus addresses the drawback of neglecting the relative locations b... | [] | null | 4 | null | null | [
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Spectral Clustering via the Power Method - Provably | https://proceedings.mlr.press/v37/boutsidis15.html | [
"Christos Boutsidis",
"Prabhanjan Kambadur",
"Alex Gittens"
] | null | null | Spectral clustering is one of the most important algorithms in data mining and machine intelligence; however, its computational complexity limits its application to truly large scale data analysis. The computational bottleneck in spectral clustering is computing a few of the top eigenvectors of the (normalized) Laplaci... | [] | null | 5 | 1311.2854 | title_snapshot | [
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Information Geometry and Minimum Description Length Networks | https://proceedings.mlr.press/v37/suna15.html | [
"Ke Sun",
"Jun Wang",
"Alexandros Kalousis",
"Stephan Marchand-Maillet"
] | null | null | We study parametric unsupervised mixture learning. We measure the loss of intrinsic information from the observations to complex mixture models, and then to simple mixture models. We present a geometric picture, where all these representations are regarded as free points in the space of probability distributions. Based... | [] | null | 6 | null | null | [
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Efficient Training of LDA on a GPU by Mean-for-Mode Estimation | https://proceedings.mlr.press/v37/tristan15.html | [
"Jean-Baptiste Tristan",
"Joseph Tassarotti",
"Guy Steele"
] | null | null | We introduce Mean-for-Mode estimation, a variant of an uncollapsed Gibbs sampler that we use to train LDA on a GPU. The algorithm combines benefits of both uncollapsed and collapsed Gibbs samplers. Like a collapsed Gibbs sampler — and unlike an uncollapsed Gibbs sampler — it has good statistical performance, and can us... | [] | null | 7 | null | null | [
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Adaptive Stochastic Alternating Direction Method of Multipliers | https://proceedings.mlr.press/v37/zhaob15.html | [
"Peilin Zhao",
"Jinwei Yang",
"Tong Zhang",
"Ping Li"
] | null | null | The Alternating Direction Method of Multipliers (ADMM) has been studied for years. Traditional ADMM algorithms need to compute, at each iteration, an (empirical) expected loss function on all training examples, resulting in a computational complexity proportional to the number of training examples. To reduce the comple... | [] | null | 8 | 1312.4564 | title_snapshot | [
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A Lower Bound for the Optimization of Finite Sums | https://proceedings.mlr.press/v37/agarwal15.html | [
"Alekh Agarwal",
"Leon Bottou"
] | null | null | This paper presents a lower bound for optimizing a finite sum of n functions, where each function is L-smooth and the sum is μ-strongly convex. We show that no algorithm can reach an error εin minimizing all functions from this class in fewer than Ω(n + \sqrtn(κ-1)\log(1/ε)) iterations, where κ=L/μis a surrogate condit... | [] | null | 9 | 1410.0723 | title_snapshot | [
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Learning Word Representations with Hierarchical Sparse Coding | https://proceedings.mlr.press/v37/yogatama15.html | [
"Dani Yogatama",
"Manaal Faruqui",
"Chris Dyer",
"Noah Smith"
] | null | null | We propose a new method for learning word representations using hierarchical regularization in sparse coding inspired by the linguistic study of word meanings. We show an efficient learning algorithm based on stochastic proximal methods that is significantly faster than previous approaches, making it possible to perfor... | [] | null | 10 | 1406.2035 | title_snapshot | [
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Learning Transferable Features with Deep Adaptation Networks | https://proceedings.mlr.press/v37/long15.html | [
"Mingsheng Long",
"Yue Cao",
"Jianmin Wang",
"Michael Jordan"
] | null | null | Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain... | [] | null | 11 | 1502.02791 | title_snapshot | [
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Robust partially observable Markov decision process | https://proceedings.mlr.press/v37/osogami15.html | [
"Takayuki Osogami"
] | null | null | We seek to find the robust policy that maximizes the expected cumulative reward for the worst case when a partially observable Markov decision process (POMDP) has uncertain parameters whose values are only known to be in a given region. We prove that the robust value function, which represents the expected cumulative r... | [] | null | 12 | null | null | [
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On the Relationship between Sum-Product Networks and Bayesian Networks | https://proceedings.mlr.press/v37/zhaoc15.html | [
"Han Zhao",
"Mazen Melibari",
"Pascal Poupart"
] | null | null | In this paper, we establish some theoretical connections between Sum-Product Networks (SPNs) and Bayesian Networks (BNs). We prove that every SPN can be converted into a BN in linear time and space in terms of the network size. The key insight is to use Algebraic Decision Diagrams (ADDs) to compactly represent the loca... | [] | null | 13 | 1501.01239 | title_snapshot | [
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Learning from Corrupted Binary Labels via Class-Probability Estimation | https://proceedings.mlr.press/v37/menon15.html | [
"Aditya Menon",
"Brendan Van Rooyen",
"Cheng Soon Ong",
"Bob Williamson"
] | null | null | Many supervised learning problems involve learning from samples whose labels are corrupted in some way. For example, each sample may have some constant probability of being incorrectly labelled (learning with label noise), or one may have a pool of unlabelled samples in lieu of negative samples (learning from positive ... | [] | null | 14 | null | null | [
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An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection | https://proceedings.mlr.press/v37/yanga15.html | [
"Tianbao Yang",
"Lijun Zhang",
"Rong Jin",
"Shenghuo Zhu"
] | null | null | In this paper, we consider the problem of column subset selection. We present a novel analysis of the spectral norm reconstruction for a simple randomized algorithm and establish a new bound that depends explicitly on the sampling probabilities. The sampling dependent error bound (i) allows us to better understand the ... | [] | null | 15 | 1505.00526 | title_snapshot | [
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A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate | https://proceedings.mlr.press/v37/shamir15.html | [
"Ohad Shamir"
] | null | null | We describe and analyze a simple algorithm for principal component analysis and singular value decomposition, VR-PCA, which uses computationally cheap stochastic iterations, yet converges exponentially fast to the optimal solution. In contrast, existing algorithms suffer either from slow convergence, or computationally... | [] | null | 16 | 1409.2848 | title_snapshot | [
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Attribute Efficient Linear Regression with Distribution-Dependent Sampling | https://proceedings.mlr.press/v37/kukliansky15.html | [
"Doron Kukliansky",
"Ohad Shamir"
] | null | null | We consider a budgeted learning setting, where the learner can only choose and observe a small subset of the attributes of each training example. We develop efficient algorithms for Ridge and Lasso linear regression, which utilize the geometry of the data by a novel distribution-dependent sampling scheme, and have exce... | [] | null | 17 | 1410.6382 | title_judge | [
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Learning Local Invariant Mahalanobis Distances | https://proceedings.mlr.press/v37/fetaya15.html | [
"Ethan Fetaya",
"Shimon Ullman"
] | null | null | For many tasks and data types, there are natural transformations to which the data should be invariant or insensitive. For instance, in visual recognition, natural images should be insensitive to rotation and translation. This requirement and its implications have been important in many machine learning applications, a... | [] | null | 18 | 1502.01176 | title_snapshot | [
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Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis | https://proceedings.mlr.press/v37/maa15.html | [
"Zhuang Ma",
"Yichao Lu",
"Dean Foster"
] | null | null | Canonical Correlation Analysis (CCA) is a widely used spectral technique for finding correlation structures in multi-view datasets. In this paper, we tackle the problem of large scale CCA, where classical algorithms, usually requiring computing the product of two huge matrices and huge matrix decomposition, are computa... | [] | null | 19 | 1506.08170 | title_snapshot | [
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Abstraction Selection in Model-based Reinforcement Learning | https://proceedings.mlr.press/v37/jiang15.html | [
"Nan Jiang",
"Alex Kulesza",
"Satinder Singh"
] | null | null | State abstractions are often used to reduce the complexity of model-based reinforcement learning when only limited quantities of data are available. However, choosing the appropriate level of abstraction is an important problem in practice. Existing approaches have theoretical guarantees only under strong assumptions o... | [] | null | 20 | null | null | [
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Surrogate Functions for Maximizing Precision at the Top | https://proceedings.mlr.press/v37/kar15.html | [
"Purushottam Kar",
"Harikrishna Narasimhan",
"Prateek Jain"
] | null | null | The problem of maximizing precision at the top of a ranked list, often dubbed Precision@k (prec@k), finds relevance in myriad learning applications such as ranking, multi-label classification, and learning with severe label imbalance. However, despite its popularity, there exist significant gaps in our understanding of... | [] | null | 21 | 1505.06813 | title_snapshot | [
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Optimizing Non-decomposable Performance Measures: A Tale of Two Classes | https://proceedings.mlr.press/v37/narasimhana15.html | [
"Harikrishna Narasimhan",
"Purushottam Kar",
"Prateek Jain"
] | null | null | Modern classification problems frequently present mild to severe label imbalance as well as specific requirements on classification characteristics, and require optimizing performance measures that are non-decomposable over the dataset, such as F-measure. Such measures have spurred much interest and pose specific chall... | [] | null | 22 | 1505.06812 | title_snapshot | [
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Coresets for Nonparametric Estimation - the Case of DP-Means | https://proceedings.mlr.press/v37/bachem15.html | [
"Olivier Bachem",
"Mario Lucic",
"Andreas Krause"
] | null | null | Scalable training of Bayesian nonparametric models is a notoriously difficult challenge. We explore the use of coresets - a data summarization technique originating from computational geometry - for this task. Coresets are weighted subsets of the data such that models trained on these coresets are provably competitive ... | [] | null | 23 | null | null | [
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A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits | https://proceedings.mlr.press/v37/gajane15.html | [
"Pratik Gajane",
"Tanguy Urvoy",
"Fabrice Clérot"
] | null | null | We study the K-armed dueling bandit problem which is a variation of the classical Multi-Armed Bandit (MAB) problem in which the learner receives only relative feedback about the selected pairs of arms. We propose a new algorithm called Relative Exponential-weight algorithm for Exploration and Exploitation (REX3) to han... | [] | null | 24 | 1601.03855 | title_snapshot | [
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Functional Subspace Clustering with Application to Time Series | https://proceedings.mlr.press/v37/bahadori15.html | [
"Mohammad Taha Bahadori",
"David Kale",
"Yingying Fan",
"Yan Liu"
] | null | null | Functional data, where samples are random functions, are increasingly common and important in a variety of applications, such as health care and traffic analysis. They are naturally high dimensional and lie along complex manifolds. These properties warrant use of the subspace assumption, but most state-of-the-art subsp... | [] | null | 25 | null | null | [
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Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams | https://proceedings.mlr.press/v37/yua15.html | [
"Rose Yu",
"Dehua Cheng",
"Yan Liu"
] | null | null | Low-rank tensor learning has many applications in machine learning. A series of batch learning algorithms have achieved great successes. However, in many emerging applications, such as climate data analysis, we are confronted with large-scale tensor streams, which poses significant challenges to existing solution in te... | [] | null | 26 | null | null | [
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Atomic Spatial Processes | https://proceedings.mlr.press/v37/jewell15.html | [
"Sean Jewell",
"Neil Spencer",
"Alexandre Bouchard-Côté"
] | null | null | The emergence of compact GPS systems and the establishment of open data initiatives has resulted in widespread availability of spatial data for many urban centres. These data can be leveraged to develop data-driven intelligent resource allocation systems for urban issues such as policing, sanitation, and transportation... | [] | null | 27 | null | null | [
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Classification with Low Rank and Missing Data | https://proceedings.mlr.press/v37/hazan15.html | [
"Elad Hazan",
"Roi Livni",
"Yishay Mansour"
] | null | null | We consider classification and regression tasks where we have missing data and assume that the (clean) data resides in a low rank subspace. Finding a hidden subspace is known to be computationally hard. Nevertheless, using a non-proper formulation we give an efficient agnostic algorithm that classifies as good as the b... | [] | null | 28 | 1501.03273 | title_snapshot | [
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Dynamic Sensing: Better Classification under Acquisition Constraints | https://proceedings.mlr.press/v37/richman15.html | [
"Oran Richman",
"Shie Mannor"
] | null | null | In many machine learning applications the quality of the data is limited by resource constraints (may it be power, bandwidth, memory, ...). In such cases, the constraints are on the average resources allocated, therefore there is some control over each sample’s quality. In most cases this option remains unused and the ... | [] | null | 29 | null | null | [
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A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis | https://proceedings.mlr.press/v37/gonga15.html | [
"Pinghua Gong",
"Jieping Ye"
] | null | null | The Orthant-Wise Limited memory Quasi-Newton (OWL-QN) method has been demonstrated to be very effective in solving the \ell_1-regularized sparse learning problem. OWL-QN extends the L-BFGS from solving unconstrained smooth optimization problems to \ell_1-regularized (non-smooth) sparse learning problems. At each iterat... | [] | null | 30 | null | null | [
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Telling cause from effect in deterministic linear dynamical systems | https://proceedings.mlr.press/v37/shajarisales15.html | [
"Naji Shajarisales",
"Dominik Janzing",
"Bernhard Schoelkopf",
"Michel Besserve"
] | null | null | Telling a cause from its effect using observed time series data is a major challenge in natural and social sciences. Assuming the effect is generated by the cause through a linear system, we propose a new approach based on the hypothesis that nature chooses the “cause” and the “mechanism generating the effect from the ... | [] | null | 31 | 1503.01299 | title_snapshot | [
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High Dimensional Bayesian Optimisation and Bandits via Additive Models | https://proceedings.mlr.press/v37/kandasamy15.html | [
"Kirthevasan Kandasamy",
"Jeff Schneider",
"Barnabas Poczos"
] | null | null | Bayesian Optimisation (BO) is a technique used in optimising a D-dimensional function which is typically expensive to evaluate. While there have been many successes for BO in low dimensions, scaling it to high dimensions has been notoriously difficult. Existing literature on the topic are under very restrictive setting... | [] | null | 32 | 1503.01673 | title_snapshot | [
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Theory of Dual-sparse Regularized Randomized Reduction | https://proceedings.mlr.press/v37/yangb15.html | [
"Tianbao Yang",
"Lijun Zhang",
"Rong Jin",
"Shenghuo Zhu"
] | null | null | In this paper, we study randomized reduction methods, which reduce high-dimensional features into low-dimensional space by randomized methods (e.g., random projection, random hashing), for large-scale high-dimensional classification. Previous theoretical results on randomized reduction methods hinge on strong assumptio... | [] | null | 33 | 1504.03991 | title_snapshot | [
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Generalization error bounds for learning to rank: Does the length of document lists matter? | https://proceedings.mlr.press/v37/tewari15.html | [
"Ambuj Tewari",
"Sougata Chaudhuri"
] | null | null | We consider the generalization ability of algorithms for learning to rank at a query level, a problem also called subset ranking. Existing generalization error bounds necessarily degrade as the size of the document list associated with a query increases. We show that such a degradation is not intrinsic to the problem. ... | [] | null | 34 | 1603.01860 | title_snapshot | [
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PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data | https://proceedings.mlr.press/v37/hocking15.html | [
"Toby Hocking",
"Guillem Rigaill",
"Guillaume Bourque"
] | null | null | Peak detection is a central problem in genomic data analysis, and current algorithms for this task are unsupervised and mostly effective for a single data type and pattern (e.g. H3K4me3 data with a sharp peak pattern). We propose PeakSeg, a new constrained maximum likelihood segmentation model for peak detection with a... | [] | null | 35 | null | null | [
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Mind the duality gap: safer rules for the Lasso | https://proceedings.mlr.press/v37/fercoq15.html | [
"Olivier Fercoq",
"Alexandre Gramfort",
"Joseph Salmon"
] | null | null | Screening rules allow to early discard irrelevant variables from the optimization in Lasso problems, or its derivatives, making solvers faster. In this paper, we propose new versions of the so-called \textitsafe rules for the Lasso. Based on duality gap considerations, our new rules create safe test regions whose diame... | [] | null | 36 | 1505.03410 | title_snapshot | [
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A General Analysis of the Convergence of ADMM | https://proceedings.mlr.press/v37/nishihara15.html | [
"Robert Nishihara",
"Laurent Lessard",
"Ben Recht",
"Andrew Packard",
"Michael Jordan"
] | null | null | We provide a new proof of the linear convergence of the alternating direction method of multipliers (ADMM) when one of the objective terms is strongly convex. Our proof is based on a framework for analyzing optimization algorithms introduced in Lessard et al. (2014), reducing algorithm convergence to verifying the stab... | [] | null | 37 | 1502.02009 | title_snapshot | [
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Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization | https://proceedings.mlr.press/v37/zhanga15.html | [
"Yuchen Zhang",
"Xiao Lin"
] | null | null | We consider a generic convex optimization problem associated with regularized empirical risk minimization of linear predictors. The problem structure allows us to reformulate it as a convex-concave saddle point problem. We propose a stochastic primal-dual coordinate method, which alternates between maximizing over one ... | [] | null | 38 | 1409.3257 | title_snapshot | [
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DiSCO: Distributed Optimization for Self-Concordant Empirical Loss | https://proceedings.mlr.press/v37/zhangb15.html | [
"Yuchen Zhang",
"Xiao Lin"
] | null | null | We propose a new distributed algorithm for empirical risk minimization in machine learning. The algorithm is based on an inexact damped Newton method, where the inexact Newton steps are computed by a distributed preconditioned conjugate gradient method. We analyze its iteration complexity and communication efficiency f... | [] | null | 39 | 1501.00263 | title_judge | [
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Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons | https://proceedings.mlr.press/v37/chena15.html | [
"Yuxin Chen",
"Changho Suh"
] | null | null | This paper explores the preference-based top-K rank aggregation problem. Suppose that a collection of items is repeatedly compared in pairs, and one wishes to recover a consistent ordering that emphasizes the top-K ranked items, based on partially revealed preferences. We focus on the Bradley-Terry-Luce (BTL) model tha... | [] | null | 40 | 1504.07218 | title_snapshot | [
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Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs | https://proceedings.mlr.press/v37/bach15.html | [
"Stephen Bach",
"Bert Huang",
"Jordan Boyd-Graber",
"Lise Getoor"
] | null | null | Latent variables allow probabilistic graphical models to capture nuance and structure in important domains such as network science, natural language processing, and computer vision. Naive approaches to learning such complex models can be prohibitively expensive—because they require repeated inferences to update beliefs... | [] | null | 41 | null | null | [
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Structural Maxent Models | https://proceedings.mlr.press/v37/cortes15.html | [
"Corinna Cortes",
"Vitaly Kuznetsov",
"Mehryar Mohri",
"Umar Syed"
] | null | null | We present a new class of density estimation models, Structural Maxent models, with feature functions selected from possibly very complex families. The design of our models is motivated by data-dependent convergence bounds and benefits from new data-dependent learning bounds expressed in terms of the Rademacher complex... | [] | null | 42 | null | null | [
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A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning | https://proceedings.mlr.press/v37/ghoshdastidar15.html | [
"Debarghya Ghoshdastidar",
"Ambedkar Dukkipati"
] | null | null | Matrix spectral methods play an important role in statistics and machine learning, and most often the word ‘matrix’ is dropped as, by default, one assumes that similarities or affinities are measured between two points, thereby resulting in similarity matrices. However, recent challenges in computer vision and text min... | [] | null | 43 | null | null | [
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The Benefits of Learning with Strongly Convex Approximate Inference | https://proceedings.mlr.press/v37/london15.html | [
"Ben London",
"Bert Huang",
"Lise Getoor"
] | null | null | We explore the benefits of strongly convex free energies in variational inference, providing both theoretical motivation and a new meta-algorithm. Using the duality between strong convexity and stability, we prove a high-probability bound on the error of learned marginals that is inversely proportional to the modulus o... | [] | null | 44 | null | null | [
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Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA | https://proceedings.mlr.press/v37/xin15.html | [
"Bo Xin",
"David Wipf"
] | null | null | Many applications require recovering a matrix of minimal rank within an affine constraint set, with matrix completion a notable special case. Because the problem is NP-hard in general, it is common to replace the matrix rank with the nuclear norm, which acts as a convenient convex surrogate. While elegant theoretical c... | [] | null | 45 | 1406.2504 | title_judge | [
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Budget Allocation Problem with Multiple Advertisers: A Game Theoretic View | https://proceedings.mlr.press/v37/maehara15.html | [
"Takanori Maehara",
"Akihiro Yabe",
"Ken-ichi Kawarabayashi"
] | null | null | In marketing planning, advertisers seek to maximize the number of customers by allocating given budgets to each media channel effectively. The budget allocation problem with a bipartite influence model captures this scenario; however, the model is problematic because it assumes there is only one advertiser in the marke... | [] | null | 46 | null | null | [
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Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains | https://proceedings.mlr.press/v37/blechschmidt15.html | [
"Katharina Blechschmidt",
"Joachim Giesen",
"Soeren Laue"
] | null | null | Many machine learning methods are given as parameterized optimization problems. Important examples of such parameters are regularization- and kernel hyperparameters. These parameters have to be tuned carefully since the choice of their values can have a significant impact on the statistical performance of the learning ... | [] | null | 47 | null | null | [
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift | https://proceedings.mlr.press/v37/ioffe15.html | [
"Sergey Ioffe",
"Christian Szegedy"
] | null | null | Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train mode... | [] | null | 48 | 1502.03167 | title_snapshot | [
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Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds | https://proceedings.mlr.press/v37/zhangc15.html | [
"Yuchen Zhang",
"Martin Wainwright",
"Michael Jordan"
] | null | null | We study the following generalized matrix rank estimation problem: given an n-by-n matrix and a constant c > 0, estimate the number of eigenvalues that are greater than c. In the distributed setting, the matrix of interest is the sum of m matrices held by separate machines. We show that any deterministic algorithm solv... | [] | null | 49 | 1502.01403 | title_snapshot | [
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Landmarking Manifolds with Gaussian Processes | https://proceedings.mlr.press/v37/liang15.html | [
"Dawen Liang",
"John Paisley"
] | null | null | We present an algorithm for finding landmarks along a manifold. These landmarks provide a small set of locations spaced out along the manifold such that they capture the low-dimensional non-linear structure of the data embedded in the high-dimensional space. The approach does not select points directly from the dataset... | [] | null | 50 | null | null | [
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Markov Mixed Membership Models | https://proceedings.mlr.press/v37/zhangd15.html | [
"Aonan Zhang",
"John Paisley"
] | null | null | We present a Markov mixed membership model (Markov M3) for grouped data that learns a fully connected graph structure among mixing components. A key feature of Markov M3 is that it interprets the mixed membership assignment as a Markov random walk over this graph of nodes. This is in contrast to tree-structured models ... | [] | null | 51 | null | null | [
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A Unified Framework for Outlier-Robust PCA-like Algorithms | https://proceedings.mlr.press/v37/yangc15.html | [
"Wenzhuo Yang",
"Huan Xu"
] | null | null | We propose a unified framework for making a wide range of PCA-like algorithms – including the standard PCA, sparse PCA and non-negative sparse PCA, etc. – robust when facing a constant fraction of arbitrarily corrupted outliers. Our theoretic analysis establishes solid performance guarantees of the proposed framework: ... | [] | null | 52 | null | null | [
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Streaming Sparse Principal Component Analysis | https://proceedings.mlr.press/v37/yangd15.html | [
"Wenzhuo Yang",
"Huan Xu"
] | null | null | This paper considers estimating the leading k principal components with at most s non-zero attributes from p-dimensional samples collected sequentially in memory limited environments. We develop and analyze two memory and computational efficient algorithms called streaming sparse PCA and streaming sparse ECA for analyz... | [] | null | 53 | null | null | [
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A Divide and Conquer Framework for Distributed Graph Clustering | https://proceedings.mlr.press/v37/yange15.html | [
"Wenzhuo Yang",
"Huan Xu"
] | null | null | Graph clustering is about identifying clusters of closely connected nodes, and is a fundamental technique of data analysis with many applications including community detection, VLSI network partitioning, collaborative filtering, and many others. In order to improve the scalability of existing graph clustering algorithm... | [] | null | 54 | null | null | [
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How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances? | https://proceedings.mlr.press/v37/an15.html | [
"Senjian An",
"Farid Boussaid",
"Mohammed Bennamoun"
] | null | null | This paper investigates how hidden layers of deep rectifier networks are capable of transforming two or more pattern sets to be linearly separable while preserving the distances with a guaranteed degree, and proves the universal classification power of such distance preserving rectifier networks. Through the nearly iso... | [] | null | 55 | null | null | [
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Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning | https://proceedings.mlr.press/v37/lakshmanan15.html | [
"K. Lakshmanan",
"Ronald Ortner",
"Daniil Ryabko"
] | null | null | We consider the problem of undiscounted reinforcement learning in continuous state space. Regret bounds in this setting usually hold under various assumptions on the structure of the reward and transition function. Under the assumption that the rewards and transition probabilities are Lipschitz, for 1-dimensional state... | [] | null | 56 | null | null | [
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The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling | https://proceedings.mlr.press/v37/betancourt15.html | [
"Michael Betancourt"
] | null | null | Leveraging the coherent exploration of Hamiltonian flow, Hamiltonian Monte Carlo produces computationally efficient Monte Carlo estimators, even with respect to complex and high-dimensional target distributions. When confronted with data-intensive applications, however, the algorithm may be too expensive to implement, ... | [] | null | 57 | 1502.01510 | title_judge | [
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Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets | https://proceedings.mlr.press/v37/garbera15.html | [
"Dan Garber",
"Elad Hazan"
] | null | null | The Frank-Wolfe method (a.k.a. conditional gradient algorithm) for smooth optimization has regained much interest in recent years in the context of large scale optimization and machine learning. A key advantage of the method is that it avoids projections - the computational bottleneck in many applications - replacing i... | [] | null | 58 | 1406.1305 | title_snapshot | [
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Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models | https://proceedings.mlr.press/v37/das15.html | [
"Mrinal Das",
"Trapit Bansal",
"Chiranjib Bhattacharyya"
] | null | null | This paper introduces ordered stick-breaking process (OSBP), where the atoms in a stick-breaking process (SBP) appear in order. The choice of weights on the atoms of OSBP ensure that; (1) probability of adding new atoms exponentially decrease, and (2) OSBP, though non-exchangeable, admit predictive probability function... | [] | null | 59 | null | null | [
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Online Learning of Eigenvectors | https://proceedings.mlr.press/v37/garberb15.html | [
"Dan Garber",
"Elad Hazan",
"Tengyu Ma"
] | null | null | Computing the leading eigenvector of a symmetric real matrix is a fundamental primitive of numerical linear algebra with numerous applications. We consider a natural online extension of the leading eigenvector problem: a sequence of matrices is presented and the goal is to predict for each matrix a unit vector, with th... | [] | null | 60 | null | null | [
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A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data | https://proceedings.mlr.press/v37/hoang15.html | [
"Trong Nghia Hoang",
"Quang Minh Hoang",
"Bryan Kian Hsiang Low"
] | null | null | This paper presents a novel unifying framework of anytime sparse Gaussian process regression (SGPR) models that can produce good predictive performance fast and improve their predictive performance over time. Our proposed unifying framework reverses the variational inference procedure to theoretically construct a non-t... | [] | null | 61 | null | null | [
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Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup | https://proceedings.mlr.press/v37/ding15.html | [
"Yufei Ding",
"Yue Zhao",
"Xipeng Shen",
"Madanlal Musuvathi",
"Todd Mytkowicz"
] | null | null | This paper presents Yinyang K-means, a new algorithm for K-means clustering. By clustering the centers in the initial stage, and leveraging efficiently maintained lower and upper bounds between a point and centers, it more effectively avoids unnecessary distance calculations than prior algorithms. It significantly outp... | [] | null | 62 | null | null | [
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Ordinal Mixed Membership Models | https://proceedings.mlr.press/v37/virtanen15.html | [
"Seppo Virtanen",
"Mark Girolami"
] | null | null | We present a novel class of mixed membership models for joint distributions of groups of observations that co-occur with ordinal response variables for each group for learning statistical associations between the ordinal response variables and the observation groups. The class of proposed models addresses a requirement... | [] | null | 63 | null | null | [
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Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network | https://proceedings.mlr.press/v37/hong15.html | [
"Seunghoon Hong",
"Tackgeun You",
"Suha Kwak",
"Bohyung Han"
] | null | null | We propose an online visual tracking algorithm by learning discriminative saliency map using Convolutional Neural Network (CNN). Given a CNN pre-trained on a large-scale image repository in offline, our algorithm takes outputs from hidden layers of the network as feature descriptors since they show excellent representa... | [] | null | 64 | 1502.06796 | title_snapshot | [
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Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods | https://proceedings.mlr.press/v37/flaxman15.html | [
"Seth Flaxman",
"Andrew Wilson",
"Daniel Neill",
"Hannes Nickisch",
"Alex Smola"
] | null | null | Gaussian processes (GPs) are a flexible class of methods with state of the art performance on spatial statistics applications. However, GPs require O(n^3) computations and O(n^2) storage, and popular GP kernels are typically limited to smoothing and interpolation. To address these difficulties, Kronecker methods have b... | [] | null | 65 | null | null | [
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Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares | https://proceedings.mlr.press/v37/raskutti15.html | [
"Garvesh Raskutti",
"Michael Mahoney"
] | null | null | We consider statistical and algorithmic aspects of solving large-scale least-squares (LS) problems using randomized sketching algorithms. Prior results show that, from an \emphalgorithmic perspective, when using sketching matrices constructed from random projections and leverage-score sampling, if the number of samples... | [] | null | 66 | 1505.06659 | title_judge | [
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On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence | https://proceedings.mlr.press/v37/korda15.html | [
"Nathaniel Korda",
"Prashanth La"
] | null | null | We provide non-asymptotic bounds for the well-known temporal difference learning algorithm TD(0) with linear function approximators. These include high-probability bounds as well as bounds in expectation. Our analysis suggests that a step-size inversely proportional to the number of iterations cannot guarantee optimal ... | [] | null | 67 | 1411.3224 | title_snapshot | [
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Learning Parametric-Output HMMs with Two Aliased States | https://proceedings.mlr.press/v37/weiss15.html | [
"Roi Weiss",
"Boaz Nadler"
] | null | null | In various applications involving hidden Markov models (HMMs), some of the hidden states are aliased, having identical output distributions. The minimality, identifiability and learnability of such aliased HMMs have been long standing problems, with only partial solutions provided thus far. In this paper we focus on pa... | [] | null | 68 | 1502.02158 | title_snapshot | [
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Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data | https://proceedings.mlr.press/v37/gala15.html | [
"Yarin Gal",
"Yutian Chen",
"Zoubin Ghahramani"
] | null | null | Multivariate categorical data occur in many applications of machine learning. One of the main difficulties with these vectors of categorical variables is sparsity. The number of possible observations grows exponentially with vector length, but dataset diversity might be poor in comparison. Recent models have gained sig... | [] | null | 69 | 1503.02182 | title_snapshot | [
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Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs | https://proceedings.mlr.press/v37/galb15.html | [
"Yarin Gal",
"Richard Turner"
] | null | null | Standard sparse pseudo-input approximations to the Gaussian process (GP) cannot handle complex functions well. Sparse spectrum alternatives attempt to answer this but are known to over-fit. We suggest the use of variational inference for the sparse spectrum approximation to avoid both issues. We model the covariance fu... | [] | null | 70 | 1503.02424 | title_snapshot | [
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Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top | https://proceedings.mlr.press/v37/rajkumar15.html | [
"Arun Rajkumar",
"Suprovat Ghoshal",
"Lek-Heng Lim",
"Shivani Agarwal"
] | null | null | We consider the problem of ranking n items from stochastically sampled pairwise preferences. It was shown recently that when the underlying pairwise preferences are acyclic, several algorithms including the Rank Centrality algorithm, the Matrix Borda algorithm, and the SVM-RankAggregation algorithm succeed in recoverin... | [] | null | 71 | null | null | [
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Stochastic Dual Coordinate Ascent with Adaptive Probabilities | https://proceedings.mlr.press/v37/csiba15.html | [
"Dominik Csiba",
"Zheng Qu",
"Peter Richtarik"
] | null | null | This paper introduces AdaSDCA: an adaptive variant of stochastic dual coordinate ascent (SDCA) for solving the regularized empirical risk minimization problems. Our modification consists in allowing the method adaptively change the probability distribution over the dual variables throughout the iterative process. AdaSD... | [] | null | 72 | 1502.08053 | title_snapshot | [
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Vector-Space Markov Random Fields via Exponential Families | https://proceedings.mlr.press/v37/tansey15.html | [
"Wesley Tansey",
"Oscar Hernan Madrid Padilla",
"Arun Sai Suggala",
"Pradeep Ravikumar"
] | null | null | We present Vector-Space Markov Random Fields (VS-MRFs), a novel class of undirected graphical models where each variable can belong to an arbitrary vector space. VS-MRFs generalize a recent line of work on scalar-valued, uni-parameter exponential family and mixed graphical models, thereby greatly broadening the class o... | [] | null | 73 | 1505.05117 | title_snapshot | [
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JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes | https://proceedings.mlr.press/v37/hugginsa15.html | [
"Jonathan Huggins",
"Karthik Narasimhan",
"Ardavan Saeedi",
"Vikash Mansinghka"
] | null | null | Markov jump processes (MJPs) are used to model a wide range of phenomenon from disease progression to RNA path folding. However, existing methods suffer from a number of shortcomings: degenerate trajectories in the case of ML estimation of parametric models and poor inferential performance in the case of nonparametric ... | [] | null | 74 | 1503.00332 | title_snapshot | [
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Low Rank Approximation using Error Correcting Coding Matrices | https://proceedings.mlr.press/v37/ubaru15.html | [
"Shashanka Ubaru",
"Arya Mazumdar",
"Yousef Saad"
] | null | null | Low-rank matrix approximation is an integral component of tools such as principal component analysis (PCA), as well as is an important instrument used in applications like web search models, text mining and computer vision, e.g., face recognition. Recently, randomized algorithms were proposed to effectively construct l... | [] | null | 75 | null | null | [
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Off-policy Model-based Learning under Unknown Factored Dynamics | https://proceedings.mlr.press/v37/hallak15.html | [
"Assaf Hallak",
"Francois Schnitzler",
"Timothy Mann",
"Shie Mannor"
] | null | null | Off-policy learning in dynamic decision problems is essential for providing strong evidence that a new policy is better than the one in use. But how can we prove superiority without testing the new policy? To answer this question, we introduce the G-SCOPE algorithm that evaluates a new policy based on data generated by... | [] | null | 76 | null | null | [
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Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification | https://proceedings.mlr.press/v37/huanga15.html | [
"Zhiwu Huang",
"Ruiping Wang",
"Shiguang Shan",
"Xianqiu Li",
"Xilin Chen"
] | null | null | The manifold of Symmetric Positive Definite (SPD) matrices has been successfully used for data representation in image set classification. By endowing the SPD manifold with Log-Euclidean Metric, existing methods typically work on vector-forms of SPD matrix logarithms. This however not only inevitably distorts the geome... | [] | null | 77 | null | null | [
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Asymmetric Transfer Learning with Deep Gaussian Processes | https://proceedings.mlr.press/v37/kandemir15.html | [
"Melih Kandemir"
] | null | null | We introduce a novel Gaussian process based Bayesian model for asymmetric transfer learning. We adopt a two-layer feed-forward deep Gaussian process as the task learner of source and target domains. The first layer projects the data onto a separate non-linear manifold for each task. We perform knowledge transfer by pro... | [] | null | 78 | null | null | [
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Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing | https://proceedings.mlr.press/v37/zhua15.html | [
"Rongda Zhu",
"Quanquan Gu"
] | null | null | In this paper, we propose a novel algorithm based on nonconvex sparsity-inducing penalty for one-bit compressed sensing. We prove that our algorithm has a sample complexity of O(s/ε^2) for strong signals, and O(s\log d/ε^2) for weak signals, where s is the number of nonzero entries in the signal vector, d is the signal... | [] | null | 79 | null | null | [
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BilBOWA: Fast Bilingual Distributed Representations without Word Alignments | https://proceedings.mlr.press/v37/gouws15.html | [
"Stephan Gouws",
"Yoshua Bengio",
"Greg Corrado"
] | null | null | We introduce BilBOWA (Bilingual Bag-of-Words without Alignments), a simple and computationally-efficient model for learning bilingual distributed representations of words which can scale to large monolingual datasets and does not require word-aligned parallel training data. Instead it trains directly on monolingual dat... | [] | null | 80 | 1410.2455 | title_snapshot | [
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Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization | https://proceedings.mlr.press/v37/sunb15.html | [
"Jiangwen Sun",
"Jin Lu",
"Tingyang Xu",
"Jinbo Bi"
] | null | null | When multiple views of data are available for a set of subjects, co-clustering aims to identify subject clusters that agree across the different views. We explore the problem of co-clustering when the underlying clusters exist in different subspaces of each view. We propose a proximal alternating linearized minimizatio... | [] | null | 81 | null | null | [
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Cascading Bandits: Learning to Rank in the Cascade Model | https://proceedings.mlr.press/v37/kveton15.html | [
"Branislav Kveton",
"Csaba Szepesvari",
"Zheng Wen",
"Azin Ashkan"
] | null | null | A search engine usually outputs a list of K web pages. The user examines this list, from the first web page to the last, and chooses the first attractive page. This model of user behavior is known as the cascade model. In this paper, we propose cascading bandits, a learning variant of the cascade model where the object... | [] | null | 82 | 1502.02763 | title_snapshot | [
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... |
Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models | https://proceedings.mlr.press/v37/foulds15.html | [
"James Foulds",
"Shachi Kumar",
"Lise Getoor"
] | null | null | Topic models have become increasingly prominent text-analytic machine learning tools for research in the social sciences and the humanities. In particular, custom topic models can be developed to answer specific research questions. The design of these models requires a non-trivial amount of effort and expertise, motiva... | [] | null | 83 | null | null | [
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Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions | https://proceedings.mlr.press/v37/ene15.html | [
"Alina Ene",
"Huy Nguyen"
] | null | null | Submodular function minimization is a fundamental optimization problem that arises in several applications in machine learning and computer vision. The problem is known to be solvable in polynomial time, but general purpose algorithms have high running times and are unsuitable for large-scale problems. Recent work have... | [] | null | 84 | 1502.02643 | title_snapshot | [
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0... |
Alpha-Beta Divergences Discover Micro and Macro Structures in Data | https://proceedings.mlr.press/v37/narayan15.html | [
"Karthik Narayan",
"Ali Punjani",
"Pieter Abbeel"
] | null | null | Although recent work in non-linear dimensionality reduction investigates multiple choices of divergence measure during optimization \citeyang2013icml,bunte2012neuro, little work discusses the direct effects that divergence measures have on visualization. We study this relationship, theoretically and through an empirica... | [] | null | 85 | null | null | [
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... |
Fictitious Self-Play in Extensive-Form Games | https://proceedings.mlr.press/v37/heinrich15.html | [
"Johannes Heinrich",
"Marc Lanctot",
"David Silver"
] | null | null | Fictitious play is a popular game-theoretic model of learning in games. However, it has received little attention in practical applications to large problems. This paper introduces two variants of fictitious play that are implemented in behavioural strategies of an extensive-form game. The first variant is a full-width... | [] | null | 86 | null | null | [
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0.... |
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback | https://proceedings.mlr.press/v37/swaminathan15.html | [
"Adith Swaminathan",
"Thorsten Joachims"
] | null | null | We develop a learning principle and an efficient algorithm for batch learning from logged bandit feedback. This learning setting is ubiquitous in online systems (e.g., ad placement, web search, recommendation), where an algorithm makes a prediction (e.g., ad ranking) for a given input (e.g., query) and observes bandit ... | [] | null | 87 | 1502.02362 | title_snapshot | [
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The Hedge Algorithm on a Continuum | https://proceedings.mlr.press/v37/krichene15.html | [
"Walid Krichene",
"Maximilian Balandat",
"Claire Tomlin",
"Alexandre Bayen"
] | null | null | We consider an online optimization problem on a subset S of R^n (not necessarily convex), in which a decision maker chooses, at each iteration t, a probability distribution x^(t) over S, and seeks to minimize a cumulative expected loss, where each loss is a Lipschitz function revealed at the end of iteration t. Buildin... | [] | null | 88 | null | null | [
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A Linear Dynamical System Model for Text | https://proceedings.mlr.press/v37/belanger15.html | [
"David Belanger",
"Sham Kakade"
] | null | null | Low dimensional representations of words allow accurate NLP models to be trained on limited annotated data. While most representations ignore words’ local context, a natural way to induce context-dependent representations is to perform inference in a probabilistic latent-variable sequence model. Given the recent succes... | [] | null | 89 | 1502.04081 | title_snapshot | [
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Unsupervised Learning of Video Representations using LSTMs | https://proceedings.mlr.press/v37/srivastava15.html | [
"Nitish Srivastava",
"Elman Mansimov",
"Ruslan Salakhudinov"
] | null | null | We use Long Short Term Memory (LSTM) networks to learn representations of video sequences. Our model uses an encoder LSTM to map an input sequence into a fixed length representation. This representation is decoded using single or multiple decoder LSTMs to perform different tasks, such as reconstructing the input sequen... | [] | null | 90 | 1502.04681 | title_snapshot | [
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0.0... |
Message Passing for Collective Graphical Models | https://proceedings.mlr.press/v37/sunc15.html | [
"Tao Sun",
"Dan Sheldon",
"Akshat Kumar"
] | null | null | Collective graphical models (CGMs) are a formalism for inference and learning about a population of independent and identically distributed individuals when only noisy aggregate data are available. We highlight a close connection between approximate MAP inference in CGMs and marginal inference in standard graphical mod... | [] | null | 91 | null | null | [
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0.... |
DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics | https://proceedings.mlr.press/v37/wanga15.html | [
"Yining Wang",
"Jun Zhu"
] | null | null | Subspace clustering separates data points approximately lying on union of affine subspaces into several clusters. This paper presents a novel nonparametric Bayesian subspace clustering model that infers both the number of subspaces and the dimension of each subspace from the observed data. Though the posterior inferenc... | [] | null | 92 | null | null | [
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... |
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades | https://proceedings.mlr.press/v37/he15.html | [
"Xinran He",
"Theodoros Rekatsinas",
"James Foulds",
"Lise Getoor",
"Yan Liu"
] | null | null | Understanding the diffusion of information in social network and social media requires modeling the text diffusion process. In this work, we develop the HawkesTopic model (HTM) for analyzing text-based cascades, such as "retweeting a post" or "publishing a follow-up blog post". HTM combines Hawkes processes and topic m... | [] | null | 93 | null | null | [
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0.018235426396131516,
-0.026445189490914345,
... |
MADE: Masked Autoencoder for Distribution Estimation | https://proceedings.mlr.press/v37/germain15.html | [
"Mathieu Germain",
"Karol Gregor",
"Iain Murray",
"Hugo Larochelle"
] | null | null | There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder’s parameters to respect autoregressive constraints: ea... | [] | null | 94 | 1502.03509 | title_snapshot | [
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0... |
An Online Learning Algorithm for Bilinear Models | https://proceedings.mlr.press/v37/wua15.html | [
"Yuanbin Wu",
"Shiliang Sun"
] | null | null | We investigate the bilinear model, which is a matrix form linear model with the rank 1 constraint. A new online learning algorithm is proposed to train the model parameters. Our algorithm runs in the manner of online mirror descent, and gradients are computed by the power iteration. To analyze it, we give a new second ... | [] | null | 95 | null | null | [
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0.0067977593280375,
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-0.0... |
Adaptive Belief Propagation | https://proceedings.mlr.press/v37/papachristoudis15.html | [
"Georgios Papachristoudis",
"John Fisher"
] | null | null | Graphical models are widely used in inference problems. In practice, one may construct a single large-scale model to explain a phenomenon of interest, which may be utilized in a variety of settings. The latent variables of interest, which can differ in each setting, may only represent a small subset of all variables. T... | [] | null | 96 | null | null | [
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-0.061453625559806824,
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0.027526281774044037,
-0.07438292354345322,
0.00... |
Large-scale log-determinant computation through stochastic Chebyshev expansions | https://proceedings.mlr.press/v37/hana15.html | [
"Insu Han",
"Dmitry Malioutov",
"Jinwoo Shin"
] | null | null | Logarithms of determinants of large positive definite matrices appear ubiquitously in machine learning applications including Gaussian graphical and Gaussian process models, partition functions of discrete graphical models, minimum-volume ellipsoids and metric and kernel learning. Log-determinant computation involves t... | [] | null | 97 | 1503.06394 | title_snapshot | [
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-0.02113465592265129,
-0.00015194856678135693,
-0.019888179376721382,
-0.08121654391288757,
... |
Differentially Private Bayesian Optimization | https://proceedings.mlr.press/v37/kusnera15.html | [
"Matt Kusner",
"Jacob Gardner",
"Roman Garnett",
"Kilian Weinberger"
] | null | null | Bayesian optimization is a powerful tool for fine-tuning the hyper-parameters of a wide variety of machine learning models. The success of machine learning has led practitioners in diverse real-world settings to learn classifiers for practical problems. As machine learning becomes commonplace, Bayesian optimization bec... | [] | null | 98 | 1501.04080 | title_snapshot | [
-0.011665621772408485,
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0.00014426409325096756,
-0.026046650484204292,
-0.0071332077495753765,
0.018593573942780495,
-0.038998402655124664,
... |
A Nearly-Linear Time Framework for Graph-Structured Sparsity | https://proceedings.mlr.press/v37/hegde15.html | [
"Chinmay Hegde",
"Piotr Indyk",
"Ludwig Schmidt"
] | null | null | We introduce a framework for sparsity structures defined via graphs. Our approach is flexible and generalizes several previously studied sparsity models. Moreover, we provide efficient projection algorithms for our sparsity model that run in nearly-linear time. In the context of sparse recovery, we show that our framew... | [] | null | 99 | null | null | [
0.00922954361885786,
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0.03344929590821266,
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0.028608767315745354,
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-0.05842633917927742,
-0.0015... |
Support Matrix Machines | https://proceedings.mlr.press/v37/luo15.html | [
"Luo Luo",
"Yubo Xie",
"Zhihua Zhang",
"Wu-Jun Li"
] | null | null | In many classification problems such as electroencephalogram (EEG) classification and image classification, the input features are naturally represented as matrices rather than vectors or scalars. In general, the structure information of the original feature matrix is useful and informative for data analysis tasks such... | [] | null | 100 | null | null | [
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-0.04283478111028671,
-0.007084163837134838,
0.012278069742023945,
-0.07473567128181458,
0.0... |
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