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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
No Oops, You Won’t Do It Again: Mechanisms for Self-correction in Crowdsourcing | https://proceedings.mlr.press/v48/shaha16.html | [
"Nihar Shah",
"Dengyong Zhou"
] | null | null | Crowdsourcing is a very popular means of obtaining the large amounts of labeled data that modern machine learning methods require. Although cheap and fast to obtain, crowdsourced labels suffer from significant amounts of error, thereby degrading the performance of downstream machine learning tasks. With the goal of imp... | [] | null | 1 | null | null | [
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Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues | https://proceedings.mlr.press/v48/shahb16.html | [
"Nihar Shah",
"Sivaraman Balakrishnan",
"Aditya Guntuboyina",
"Martin Wainwright"
] | null | null | There are various parametric models for analyzing pairwise comparison data, including the Bradley-Terry-Luce (BTL) and Thurstone models, but their reliance on strong parametric assumptions is limiting. In this work, we study a flexible model for pairwise comparisons, under which the probabilities of outcomes are requir... | [] | null | 2 | 1510.05610 | title_snapshot | [
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Uprooting and Rerooting Graphical Models | https://proceedings.mlr.press/v48/weller16.html | [
"Adrian Weller"
] | null | null | We show how any binary pairwise model may be “uprooted” to a fully symmetric model, wherein original singleton potentials are transformed to potentials on edges to an added variable, and then “rerooted” to a new model on the original number of variables. The new model is essentially equivalent to the original model, wi... | [] | null | 3 | null | null | [
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A Deep Learning Approach to Unsupervised Ensemble Learning | https://proceedings.mlr.press/v48/shaham16.html | [
"Uri Shaham",
"Xiuyuan Cheng",
"Omer Dror",
"Ariel Jaffe",
"Boaz Nadler",
"Joseph Chang",
"Yuval Kluger"
] | null | null | We show how deep learning methods can be applied in the context of crowdsourcing and unsupervised ensemble learning. First, we prove that the popular model of Dawid and Skene, which assumes that all classifiers are conditionally independent, is \em equivalent to a Restricted Boltzmann Machine (RBM) with a single hidden... | [] | null | 4 | 1602.02285 | title_snapshot | [
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Revisiting Semi-Supervised Learning with Graph Embeddings | https://proceedings.mlr.press/v48/yanga16.html | [
"Zhilin Yang",
"William Cohen",
"Ruslan Salakhudinov"
] | null | null | We present a semi-supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant... | [] | null | 5 | 1603.08861 | title_snapshot | [
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Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization | https://proceedings.mlr.press/v48/finn16.html | [
"Chelsea Finn",
"Sergey Levine",
"Pieter Abbeel"
] | null | null | Reinforcement learning can acquire complex behaviors from high-level specifications. However, defining a cost function that can be optimized effectively and encodes the correct task is challenging in practice. We explore how inverse optimal control (IOC) can be used to learn behaviors from demonstrations, with applicat... | [] | null | 6 | 1603.00448 | title_snapshot | [
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Diversity-Promoting Bayesian Learning of Latent Variable Models | https://proceedings.mlr.press/v48/xiea16.html | [
"Pengtao Xie",
"Jun Zhu",
"Eric Xing"
] | null | null | In learning latent variable models (LVMs), it is important to effectively capture infrequent patterns and shrink model size without sacrificing modeling power. Various studies have been done to “diversify” a LVM, which aim to learn a diverse set of latent components in LVMs. Most existing studies fall into a frequentis... | [] | null | 7 | 1711.08770 | title_snapshot | [
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Additive Approximations in High Dimensional Nonparametric Regression via the SALSA | https://proceedings.mlr.press/v48/kandasamy16.html | [
"Kirthevasan Kandasamy",
"Yaoliang Yu"
] | null | null | High dimensional nonparametric regression is an inherently difficult problem with known lower bounds depending exponentially in dimension. A popular strategy to alleviate this curse of dimensionality has been to use additive models of \emphfirst order, which model the regression function as a sum of independent functio... | [] | null | 8 | 1602.00287 | title_snapshot | [
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Hawkes Processes with Stochastic Excitations | https://proceedings.mlr.press/v48/leea16.html | [
"Young Lee",
"Kar Wai Lim",
"Cheng Soon Ong"
] | null | null | We propose an extension to Hawkes processes by treating the levels of self-excitation as a stochastic differential equation. Our new point process allows better approximation in application domains where events and intensities accelerate each other with correlated levels of contagion. We generalize a recent algorithm f... | [] | null | 9 | 1609.06831 | title_snapshot | [
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Data-driven Rank Breaking for Efficient Rank Aggregation | https://proceedings.mlr.press/v48/khetan16.html | [
"Ashish Khetan",
"Sewoong Oh"
] | null | null | Rank aggregation systems collect ordinal preferences from individuals to produce a global ranking that represents the social preference. To reduce the computational complexity of learning the global ranking, a common practice is to use rank-breaking. Individuals’ preferences are broken into pairwise comparisons and the... | [] | null | 10 | 1601.05495 | title_snapshot | [
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Dropout distillation | https://proceedings.mlr.press/v48/bulo16.html | [
"Samuel Rota Bulò",
"Lorenzo Porzi",
"Peter Kontschieder"
] | null | null | Dropout is a popular stochastic regularization technique for deep neural networks that works by randomly dropping (i.e. zeroing) units from the network during training. This randomization process allows to implicitly train an ensemble of exponentially many networks sharing the same parametrization, which should be aver... | [] | null | 11 | null | null | [
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Metadata-conscious anonymous messaging | https://proceedings.mlr.press/v48/fanti16.html | [
"Giulia Fanti",
"Peter Kairouz",
"Sewoong Oh",
"Kannan Ramchandran",
"Pramod Viswanath"
] | null | null | Anonymous messaging platforms like Whisper and Yik Yak allow users to spread messages over a network (e.g., a social network) without revealing message authorship to other users. The spread of messages on these platforms can be modeled by a diffusion process over a graph. Recent advances in network analysis have reveal... | [] | null | 12 | null | null | [
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The Teaching Dimension of Linear Learners | https://proceedings.mlr.press/v48/liua16.html | [
"Ji Liu",
"Xiaojin Zhu",
"Hrag Ohannessian"
] | null | null | Teaching dimension is a learning theoretic quantity that specifies the minimum training set size to teach a target model to a learner. Previous studies on teaching dimension focused on version-space learners which maintain all hypotheses consistent with the training data, and cannot be applied to modern machine learner... | [] | null | 13 | 1512.02181 | title_snapshot | [
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Truthful Univariate Estimators | https://proceedings.mlr.press/v48/caragiannis16.html | [
"Ioannis Caragiannis",
"Ariel Procaccia",
"Nisarg Shah"
] | null | null | We revisit the classic problem of estimating the population mean of an unknown single-dimensional distribution from samples, taking a game-theoretic viewpoint. In our setting, samples are supplied by strategic agents, who wish to pull the estimate as close as possible to their own value. In this setting, the sample mea... | [] | null | 14 | null | null | [
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Why Regularized Auto-Encoders learn Sparse Representation? | https://proceedings.mlr.press/v48/arpita16.html | [
"Devansh Arpit",
"Yingbo Zhou",
"Hung Ngo",
"Venu Govindaraju"
] | null | null | Sparse distributed representation is the key to learning useful features in deep learning algorithms, because not only it is an efficient mode of data representation, but also – more importantly – it captures the generation process of most real world data. While a number of regularized auto-encoders (AE) enforce sparsi... | [] | null | 15 | 1505.05561 | title_snapshot | [
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k-variates++: more pluses in the k-means++ | https://proceedings.mlr.press/v48/nock16.html | [
"Richard Nock",
"Raphael Canyasse",
"Roksana Boreli",
"Frank Nielsen"
] | null | null | k-means++ seeding has become a de facto standard for hard clustering algorithms. In this paper, our first contribution is a two-way generalisation of this seeding, k-variates++, that includes the sampling of general densities rather than just a discrete set of Dirac densities anchored at the point locations, *and* a ge... | [] | null | 16 | 1602.01198 | title_snapshot | [
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Multi-Player Bandits – a Musical Chairs Approach | https://proceedings.mlr.press/v48/rosenski16.html | [
"Jonathan Rosenski",
"Ohad Shamir",
"Liran Szlak"
] | null | null | We consider a variant of the stochastic multi-armed bandit problem, where multiple players simultaneously choose from the same set of arms and may collide, receiving no reward. This setting has been motivated by problems arising in cognitive radio networks, and is especially challenging under the realistic assumption t... | [] | null | 17 | 1512.02866 | title_snapshot | [
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The Information Sieve | https://proceedings.mlr.press/v48/steeg16.html | [
"Greg Ver Steeg",
"Aram Galstyan"
] | null | null | We introduce a new framework for unsupervised learning of representations based on a novel hierarchical decomposition of information. Intuitively, data is passed through a series of progressively fine-grained sieves. Each layer of the sieve recovers a single latent factor that is maximally informative about multivariat... | [] | null | 18 | 1507.02284 | title_snapshot | [
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Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin | https://proceedings.mlr.press/v48/amodei16.html | [
"Dario Amodei",
"Sundaram Ananthanarayanan",
"Rishita Anubhai",
"Jingliang Bai",
"Eric Battenberg",
"Carl Case",
"Jared Casper",
"Bryan Catanzaro",
"Qiang Cheng",
"Guoliang Chen",
"Jie Chen",
"Jingdong Chen",
"Zhijie Chen",
"Mike Chrzanowski",
"Adam Coates",
"Greg Diamos",
"Ke Ding",... | null | null | We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech–two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including nois... | [] | null | 19 | 1512.02595 | title_snapshot | [
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On the Consistency of Feature Selection With Lasso for Non-linear Targets | https://proceedings.mlr.press/v48/zhanga16.html | [
"Yue Zhang",
"Weihong Guo",
"Soumya Ray"
] | null | null | An important question in feature selection is whether a selection strategy recovers the “true” set of features, given enough data. We study this question in the context of the popular Least Absolute Shrinkage and Selection Operator (Lasso) feature selection strategy. In particular, we consider the scenario when the mod... | [] | null | 20 | null | null | [
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Minimum Regret Search for Single- and Multi-Task Optimization | https://proceedings.mlr.press/v48/metzen16.html | [
"Jan Hendrik Metzen"
] | null | null | We propose minimum regret search (MRS), a novel acquisition function for Bayesian optimization. MRS bears similarities with information-theoretic approaches such as entropy search (ES). However, while ES aims in each query at maximizing the information gain with respect to the global maximum, MRS aims at minimizing the... | [] | null | 21 | 1602.01064 | title_snapshot | [
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CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy | https://proceedings.mlr.press/v48/gilad-bachrach16.html | [
"Ran Gilad-Bachrach",
"Nathan Dowlin",
"Kim Laine",
"Kristin Lauter",
"Michael Naehrig",
"John Wernsing"
] | null | null | Applying machine learning to a problem which involves medical, financial, or other types of sensitive data, not only requires accurate predictions but also careful attention to maintaining data privacy and security. Legal and ethical requirements may prevent the use of cloud-based machine learning solutions for such ta... | [] | null | 22 | null | null | [
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The Variational Nystrom method for large-scale spectral problems | https://proceedings.mlr.press/v48/vladymyrov16.html | [
"Max Vladymyrov",
"Miguel Carreira-Perpinan"
] | null | null | Spectral methods for dimensionality reduction and clustering require solving an eigenproblem defined by a sparse affinity matrix. When this matrix is large, one seeks an approximate solution. The standard way to do this is the Nystrom method, which first solves a small eigenproblem considering only a subset of landmark... | [] | null | 23 | null | null | [
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Multi-Bias Non-linear Activation in Deep Neural Networks | https://proceedings.mlr.press/v48/lia16.html | [
"Hongyang Li",
"Wanli Ouyang",
"Xiaogang Wang"
] | null | null | As a widely used non-linear activation, Rectified Linear Unit (ReLU) separates noise and signal in a feature map by learning a threshold or bias. However, we argue that the classification of noise and signal not only depends on the magnitude of responses, but also the context of how the feature responses would be used ... | [] | null | 24 | 1604.00676 | title_snapshot | [
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Asymmetric Multi-task Learning Based on Task Relatedness and Loss | https://proceedings.mlr.press/v48/leeb16.html | [
"Giwoong Lee",
"Eunho Yang",
"Sung Hwang"
] | null | null | We propose a novel multi-task learning method that can minimize the effect of negative transfer by allowing asymmetric transfer between the tasks based on task relatedness as well as the amount of individual task losses, which we refer to as Asymmetric Multi-task Learning (AMTL). To tackle this problem, we couple multi... | [] | null | 25 | null | null | [
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Accurate Robust and Efficient Error Estimation for Decision Trees | https://proceedings.mlr.press/v48/fan16.html | [
"Lixin Fan"
] | null | null | This paper illustrates a novel approach to the estimation of generalization error of decision tree classifiers. We set out the study of decision tree errors in the context of consistency analysis theory, which proved that the Bayes error can be achieved only if when the number of data samples thrown into each leaf node... | [] | null | 26 | null | null | [
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Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity | https://proceedings.mlr.press/v48/shamira16.html | [
"Ohad Shamir"
] | null | null | We study the convergence properties of the VR-PCA algorithm introduced by (Shamir, 2015) for fast computation of leading singular vectors. We prove several new results, including a formal analysis of a block version of the algorithm, and convergence from random initialization. We also make a few observations of indepen... | [] | null | 27 | 1507.08788 | title_snapshot | [
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Convergence of Stochastic Gradient Descent for PCA | https://proceedings.mlr.press/v48/shamirb16.html | [
"Ohad Shamir"
] | null | null | We consider the problem of principal component analysis (PCA) in a streaming stochastic setting, where our goal is to find a direction of approximate maximal variance, based on a stream of i.i.d. data points in R^d. A simple and computationally cheap algorithm for this is stochastic gradient descent (SGD), which increm... | [] | null | 28 | 1509.09002 | title_snapshot | [
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Dealbreaker: A Nonlinear Latent Variable Model for Educational Data | https://proceedings.mlr.press/v48/lan16.html | [
"Andrew Lan",
"Tom Goldstein",
"Richard Baraniuk",
"Christoph Studer"
] | null | null | Statistical models of student responses on assessment questions, such as those in homeworks and exams, enable educators and computer-based personalized learning systems to gain insights into students’ knowledge using machine learning. Popular student-response models, including the Rasch model and item response theory m... | [] | null | 29 | null | null | [
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A Kernelized Stein Discrepancy for Goodness-of-fit Tests | https://proceedings.mlr.press/v48/liub16.html | [
"Qiang Liu",
"Jason Lee",
"Michael Jordan"
] | null | null | We derive a new discrepancy statistic for measuring differences between two probability distributions based on combining Stein’s identity and the reproducing kernel Hilbert space theory. We apply our result to test how well a probabilistic model fits a set of observations, and derive a new class of powerful goodness-of... | [] | null | 30 | 1602.03253 | title_judge | [
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Variable Elimination in the Fourier Domain | https://proceedings.mlr.press/v48/xue16.html | [
"Yexiang Xue",
"Stefano Ermon",
"Ronan Le Bras",
"Carla",
"Bart Selman"
] | null | null | The ability to represent complex high dimensional probability distributions in a compact form is one of the key insights in the field of graphical models. Factored representations are ubiquitous in machine learning and lead to major computational advantages. We explore a different type of compact representation based o... | [] | null | 31 | 1508.04032 | title_snapshot | [
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Low-Rank Matrix Approximation with Stability | https://proceedings.mlr.press/v48/lib16.html | [
"Dongsheng Li",
"Chao Chen",
"Qin Lv",
"Junchi Yan",
"Li Shang",
"Stephen Chu"
] | null | null | Low-rank matrix approximation has been widely adopted in machine learning applications with sparse data, such as recommender systems. However, the sparsity of the data, incomplete and noisy, introduces challenges to the algorithm stability – small changes in the training data may significantly change the models. As a r... | [] | null | 32 | null | null | [
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Linking losses for density ratio and class-probability estimation | https://proceedings.mlr.press/v48/menon16.html | [
"Aditya Menon",
"Cheng Soon Ong"
] | null | null | Given samples from two densities p and q, density ratio estimation (DRE) is the problem of estimating the ratio p/q. Two popular discriminative approaches to DRE are KL importance estimation (KLIEP), and least squares importance fitting (LSIF). In this paper, we show that KLIEP and LSIF both employ class-probability es... | [] | null | 33 | null | null | [
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Stochastic Variance Reduction for Nonconvex Optimization | https://proceedings.mlr.press/v48/reddi16.html | [
"Sashank J. Reddi",
"Ahmed Hefny",
"Suvrit Sra",
"Barnabas Poczos",
"Alex Smola"
] | null | null | We study nonconvex finite-sum problems and analyze stochastic variance reduced gradient (SVRG) methods for them. SVRG and related methods have recently surged into prominence for convex optimization given their edge over stochastic gradient descent (SGD); but their theoretical analysis almost exclusively assumes convex... | [] | null | 34 | 1603.06160 | title_snapshot | [
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Hierarchical Variational Models | https://proceedings.mlr.press/v48/ranganath16.html | [
"Rajesh Ranganath",
"Dustin Tran",
"David Blei"
] | null | null | Black box variational inference allows researchers to easily prototype and evaluate an array of models. Recent advances allow such algorithms to scale to high dimensions. However, a central question remains: How to specify an expressive variational distribution that maintains efficient computation? To address this, we ... | [] | null | 35 | 1511.02386 | title_snapshot | [
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Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams | https://proceedings.mlr.press/v48/adams16.html | [
"Roy Adams",
"Nazir Saleheen",
"Edison Thomaz",
"Abhinav Parate",
"Santosh Kumar",
"Benjamin Marlin"
] | null | null | The field of mobile health (mHealth) has the potential to yield new insights into health and behavior through the analysis of continuously recorded data from wearable health and activity sensors. In this paper, we present a hierarchical span-based conditional random field model for the key problem of jointly detecting ... | [] | null | 36 | null | null | [
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Binary embeddings with structured hashed projections | https://proceedings.mlr.press/v48/choromanska16.html | [
"Anna Choromanska",
"Krzysztof Choromanski",
"Mariusz Bojarski",
"Tony Jebara",
"Sanjiv Kumar",
"Yann LeCun"
] | null | null | We consider the hashing mechanism for constructing binary embeddings, that involves pseudo-random projections followed by nonlinear (sign function) mappings. The pseudo-random projection is described by a matrix, where not all entries are independent random variables but instead a fixed “budget of randomness” is distri... | [] | null | 37 | 1511.05212 | title_snapshot | [
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A Variational Analysis of Stochastic Gradient Algorithms | https://proceedings.mlr.press/v48/mandt16.html | [
"Stephan Mandt",
"Matthew Hoffman",
"David Blei"
] | null | null | Stochastic Gradient Descent (SGD) is an important algorithm in machine learning. With constant learning rates, it is a stochastic process that, after an initial phase of convergence, generates samples from a stationary distribution. We show that SGD with constant rates can be effectively used as an approximate posterio... | [] | null | 38 | 1602.02666 | title_snapshot | [
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Adaptive Sampling for SGD by Exploiting Side Information | https://proceedings.mlr.press/v48/gopal16.html | [
"Siddharth Gopal"
] | null | null | This paper proposes a new mechanism for sampling training instances for stochastic gradient descent (SGD) methods by exploiting any side-information associated with the instances (for e.g. class-labels) to improve convergence. Previous methods have either relied on sampling from a distribution defined over training ins... | [] | null | 39 | null | null | [
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Learning from Multiway Data: Simple and Efficient Tensor Regression | https://proceedings.mlr.press/v48/yu16.html | [
"Rose Yu",
"Yan Liu"
] | null | null | Tensor regression has shown to be advantageous in learning tasks with multi-directional relatedness. Given massive multiway data, traditional methods are often too slow to operate on or suffer from memory bottleneck. In this paper, we introduce subsampled tensor projected gradient to solve the problem. Our algorithm is... | [] | null | 40 | 1607.02535 | title_snapshot | [
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A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models | https://proceedings.mlr.press/v48/hoang16.html | [
"Trong Nghia Hoang",
"Quang Minh Hoang",
"Bryan Kian Hsiang Low"
] | null | null | This paper presents a novel distributed variational inference framework that unifies many parallel sparse Gaussian process regression (SGPR) models for scalable hyperparameter learning with big data. To achieve this, our framework exploits a structure of correlated noise process model that represents the observation no... | [] | null | 41 | null | null | [
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Online Stochastic Linear Optimization under One-bit Feedback | https://proceedings.mlr.press/v48/zhangb16.html | [
"Lijun Zhang",
"Tianbao Yang",
"Rong Jin",
"Yichi Xiao",
"Zhi-hua Zhou"
] | null | null | In this paper, we study a special bandit setting of online stochastic linear optimization, where only one-bit of information is revealed to the learner at each round. This problem has found many applications including online advertisement and online recommendation. We assume the binary feedback is a random variable gen... | [] | null | 42 | 1509.07728 | title_snapshot | [
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Adaptive Algorithms for Online Convex Optimization with Long-term Constraints | https://proceedings.mlr.press/v48/jenatton16.html | [
"Rodolphe Jenatton",
"Jim Huang",
"Cedric Archambeau"
] | null | null | We present an adaptive online gradient descent algorithm to solve online convex optimization problems with long-term constraints, which are constraints that need to be satisfied when accumulated over a finite number of rounds T, but can be violated in intermediate rounds. For some user-defined trade-off parameter βin (... | [] | null | 43 | 1512.07422 | title_snapshot | [
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Actively Learning Hemimetrics with Applications to Eliciting User Preferences | https://proceedings.mlr.press/v48/singla16.html | [
"Adish Singla",
"Sebastian Tschiatschek",
"Andreas Krause"
] | null | null | Motivated by an application of eliciting users’ preferences, we investigate the problem of learning hemimetrics, i.e., pairwise distances among a set of n items that satisfy triangle inequalities and non-negativity constraints. In our application, the (asymmetric) distances quantify private costs a user incurs when sub... | [] | null | 44 | 1605.07144 | title_snapshot | [
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Learning Simple Algorithms from Examples | https://proceedings.mlr.press/v48/zaremba16.html | [
"Wojciech Zaremba",
"Tomas Mikolov",
"Armand Joulin",
"Rob Fergus"
] | null | null | We present an approach for learning simple algorithms such as copying, multi-digit addition and single digit multiplication directly from examples. Our framework consists of a set of interfaces, accessed by a controller. Typical interfaces are 1-D tapes or 2-D grids that hold the input and output data. For the controll... | [] | null | 45 | 1511.07275 | title_snapshot | [
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Learning Physical Intuition of Block Towers by Example | https://proceedings.mlr.press/v48/lerer16.html | [
"Adam Lerer",
"Sam Gross",
"Rob Fergus"
] | null | null | Wooden blocks are a common toy for infants, allowing them to develop motor skills and gain intuition about the physical behavior of the world. In this paper, we explore the ability of deep feed-forward models to learn such intuitive physics. Using a 3D game engine, we create small towers of wooden blocks whose stabilit... | [] | null | 46 | 1603.01312 | title_snapshot | [
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Structure Learning of Partitioned Markov Networks | https://proceedings.mlr.press/v48/liuc16.html | [
"Song Liu",
"Taiji Suzuki",
"Masashi Sugiyama",
"Kenji Fukumizu"
] | null | null | We learn the structure of a Markov Network between two groups of random variables from joint observations. Since modelling and learning the full MN structure may be hard, learning the links between two groups directly may be a preferable option. We introduce a novel concept called the \emphpartitioned ratio whose facto... | [] | null | 47 | 1504.00624 | title_snapshot | [
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Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient | https://proceedings.mlr.press/v48/yangb16.html | [
"Tianbao Yang",
"Lijun Zhang",
"Rong Jin",
"Jinfeng Yi"
] | null | null | This work focuses on dynamic regret of online convex optimization that compares the performance of online learning to a clairvoyant who knows the sequence of loss functions in advance and hence selects the minimizer of the loss function at each step. By assuming that the clairvoyant moves slowly (i.e., the minimizers c... | [] | null | 48 | 1605.04638 | title_snapshot | [
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Beyond CCA: Moment Matching for Multi-View Models | https://proceedings.mlr.press/v48/podosinnikova16.html | [
"Anastasia Podosinnikova",
"Francis Bach",
"Simon Lacoste-Julien"
] | null | null | We introduce three novel semi-parametric extensions of probabilistic canonical correlation analysis with identifiability guarantees. We consider moment matching techniques for estimation in these models. For that, by drawing explicit links between the new models and a discrete version of independent component analysis ... | [] | null | 49 | 1602.09013 | title_snapshot | [
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Fast methods for estimating the Numerical rank of large matrices | https://proceedings.mlr.press/v48/ubaru16.html | [
"Shashanka Ubaru",
"Yousef Saad"
] | null | null | We present two computationally inexpensive techniques for estimating the numerical rank of a matrix, combining powerful tools from computational linear algebra. These techniques exploit three key ingredients. The first is to approximate the projector on the non-null invariant subspace of the matrix by using a polynomia... | [] | null | 50 | null | null | [
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Unsupervised Deep Embedding for Clustering Analysis | https://proceedings.mlr.press/v48/xieb16.html | [
"Junyuan Xie",
"Ross Girshick",
"Ali Farhadi"
] | null | null | Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning representations for clustering. In this paper, we propose Deep Embedded Clustering (DEC), a method that simultaneously lea... | [] | null | 51 | 1511.06335 | title_snapshot | [
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Efficient Private Empirical Risk Minimization for High-dimensional Learning | https://proceedings.mlr.press/v48/kasiviswanathan16.html | [
"Shiva Prasad Kasiviswanathan",
"Hongxia Jin"
] | null | null | Dimensionality reduction is a popular approach for dealing with high dimensional data that leads to substantial computational savings. Random projections are a simple and effective method for universal dimensionality reduction with rigorous theoretical guarantees. In this paper, we theoretically study the problem of di... | [] | null | 52 | null | null | [
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Parameter Estimation for Generalized Thurstone Choice Models | https://proceedings.mlr.press/v48/vojnovic16.html | [
"Milan Vojnovic",
"Seyoung Yun"
] | null | null | We consider the maximum likelihood parameter estimation problem for a generalized Thurstone choice model, where choices are from comparison sets of two or more items. We provide tight characterizations of the mean square error, as well as necessary and sufficient conditions for correct classification when each item bel... | [] | null | 53 | null | null | [
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Large-Margin Softmax Loss for Convolutional Neural Networks | https://proceedings.mlr.press/v48/liud16.html | [
"Weiyang Liu",
"Yandong Wen",
"Zhiding Yu",
"Meng Yang"
] | null | null | Cross-entropy loss together with softmax is arguably one of the most common used supervision components in convolutional neural networks (CNNs). Despite its simplicity, popularity and excellent performance, the component does not explicitly encourage discriminative learning of features. In this paper, we propose a gene... | [] | null | 54 | 1612.02295 | title_snapshot | [
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A Random Matrix Approach to Echo-State Neural Networks | https://proceedings.mlr.press/v48/couillet16.html | [
"Romain Couillet",
"Gilles Wainrib",
"Hafiz Tiomoko Ali",
"Harry Sevi"
] | null | null | Recurrent neural networks, especially in their linear version, have provided many qualitative insights on their performance under different configurations. This article provides, through a novel random matrix framework, the quantitative counterpart of these performance results, specifically in the case of echo-state ne... | [] | null | 55 | null | null | [
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Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings | https://proceedings.mlr.press/v48/johnson16.html | [
"Rie Johnson",
"Tong Zhang"
] | null | null | One-hot CNN (convolutional neural network) has been shown to be effective for text categorization (Johnson & Zhang, 2015). We view it as a special case of a general framework which jointly trains a linear model with a non-linear feature generator consisting of ‘text region embedding + pooling’. Under this framework, we... | [] | null | 56 | 1602.02373 | title_snapshot | [
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Optimality of Belief Propagation for Crowdsourced Classification | https://proceedings.mlr.press/v48/ok16.html | [
"Jungseul Ok",
"Sewoong Oh",
"Jinwoo Shin",
"Yung Yi"
] | null | null | Crowdsourcing systems are popular for solving large-scale labelling tasks with low-paid (or even non-paid) workers. We study the problem of recovering the true labels from noisy crowdsourced labels under the popular Dawid-Skene model. To address this inference problem, several algorithms have recently been proposed, bu... | [] | null | 57 | null | null | [
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Stability of Controllers for Gaussian Process Forward Models | https://proceedings.mlr.press/v48/vinogradska16.html | [
"Julia Vinogradska",
"Bastian Bischoff",
"Duy Nguyen-Tuong",
"Anne Romer",
"Henner Schmidt",
"Jan Peters"
] | null | null | Learning control has become an appealing alternative to the derivation of control laws based on classic control theory. However, a major shortcoming of learning control is the lack of performance guarantees which prevents its application in many real-world scenarios. As a step in this direction, we provide a stability ... | [] | null | 58 | null | null | [
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Learning privately from multiparty data | https://proceedings.mlr.press/v48/hamm16.html | [
"Jihun Hamm",
"Yingjun Cao",
"Mikhail Belkin"
] | null | null | Learning a classifier from private data distributed across multiple parties is an important problem that has many potential applications. How can we build an accurate and differentially private global classifier by combining locally-trained classifiers from different parties, without access to any party’s private data?... | [] | null | 59 | 1602.03552 | title_snapshot | [
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Network Morphism | https://proceedings.mlr.press/v48/wei16.html | [
"Tao Wei",
"Changhu Wang",
"Yong Rui",
"Chang Wen Chen"
] | null | null | We present a systematic study on how to morph a well-trained neural network to a new one so that its network function can be completely preserved. We define this as network morphism in this research. After morphing a parent network, the child network is expected to inherit the knowledge from its parent network and also... | [] | null | 60 | 1603.01670 | title_snapshot | [
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A Kronecker-factored approximate Fisher matrix for convolution layers | https://proceedings.mlr.press/v48/grosse16.html | [
"Roger Grosse",
"James Martens"
] | null | null | Second-order optimization methods such as natural gradient descent have the potential to speed up training of neural networks by correcting for the curvature of the loss function. Unfortunately, the exact natural gradient is impractical to compute for large models, and most approximations either require an expensive it... | [] | null | 61 | 1602.01407 | title_snapshot | [
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Experimental Design on a Budget for Sparse Linear Models and Applications | https://proceedings.mlr.press/v48/ravi16.html | [
"Sathya Narayanan Ravi",
"Vamsi Ithapu",
"Sterling Johnson",
"Vikas Singh"
] | null | null | Budget constrained optimal design of experiments is a classical problem in statistics. Although the optimal design literature is very mature, few efficient strategies are available when these design problems appear in the context of sparse linear models commonly encountered in high dimensional machine learning and stat... | [] | null | 62 | null | null | [
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Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs | https://proceedings.mlr.press/v48/osokin16.html | [
"Anton Osokin",
"Jean-Baptiste Alayrac",
"Isabella Lukasewitz",
"Puneet Dokania",
"Simon Lacoste-Julien"
] | null | null | In this paper, we propose several improvements on the block-coordinate Frank-Wolfe (BCFW) algorithm from Lacoste-Julien et al. (2013) recently used to optimize the structured support vector machine (SSVM) objective in the context of structured prediction, though it has wider applications. The key intuition behind our i... | [] | null | 63 | 1605.09346 | title_snapshot | [
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Exact Exponent in Optimal Rates for Crowdsourcing | https://proceedings.mlr.press/v48/gaoa16.html | [
"Chao Gao",
"Yu Lu",
"Dengyong Zhou"
] | null | null | Crowdsourcing has become a popular tool for labeling large datasets. This paper studies the optimal error rate for aggregating crowdsourced labels provided by a collection of amateur workers. Under the Dawid-Skene probabilistic model, we establish matching upper and lower bounds with an exact exponent mI(\pi), where m ... | [] | null | 64 | 1605.07696 | title_snapshot | [
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Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification | https://proceedings.mlr.press/v48/zhangc16.html | [
"Yuting Zhang",
"Kibok Lee",
"Honglak Lee"
] | null | null | Unsupervised learning and supervised learning are key research topics in deep learning. However, as high-capacity supervised neural networks trained with a large amount of labels have achieved remarkable success in many computer vision tasks, the availability of large-scale labeled images reduced the significance of un... | [] | null | 65 | 1606.06582 | title_snapshot | [
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Online Low-Rank Subspace Clustering by Basis Dictionary Pursuit | https://proceedings.mlr.press/v48/shen16.html | [
"Jie Shen",
"Ping Li",
"Huan Xu"
] | null | null | Low-Rank Representation (LRR) has been a significant method for segmenting data that are generated from a union of subspaces. It is also known that solving LRR is challenging in terms of time complexity and memory footprint, in that the size of the nuclear norm regularized matrix is n-by-n (where n is the number of sam... | [] | null | 66 | 1503.08356 | title_judge | [
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A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization | https://proceedings.mlr.press/v48/curtis16.html | [
"Frank Curtis"
] | null | null | An algorithm for stochastic (convex or nonconvex) optimization is presented. The algorithm is variable-metric in the sense that, in each iteration, the step is computed through the product of a symmetric positive definite scaling matrix and a stochastic (mini-batch) gradient of the objective function, where the sequenc... | [] | null | 67 | null | null | [
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Stochastic Quasi-Newton Langevin Monte Carlo | https://proceedings.mlr.press/v48/simsekli16.html | [
"Umut Simsekli",
"Roland Badeau",
"Taylan Cemgil",
"Gaël Richard"
] | null | null | Recently, Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) methods have been proposed for scaling up Monte Carlo computations to large data problems. Whilst these approaches have proven useful in many applications, vanilla SG-MCMC might suffer from poor mixing rates when random variables exhibit strong couplings ... | [] | null | 68 | 1602.03442 | title_snapshot | [
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Doubly Robust Off-policy Value Evaluation for Reinforcement Learning | https://proceedings.mlr.press/v48/jiang16.html | [
"Nan Jiang",
"Lihong Li"
] | null | null | We study the problem of off-policy value evaluation in reinforcement learning (RL), where one aims to estimate the value of a new policy based on data collected by a different policy. This problem is often a critical step when applying RL to real-world problems. Despite its importance, existing general methods either h... | [] | null | 69 | 1511.03722 | title_snapshot | [
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Fast Rate Analysis of Some Stochastic Optimization Algorithms | https://proceedings.mlr.press/v48/qua16.html | [
"Chao Qu",
"Huan Xu",
"Chong Ong"
] | null | null | In this paper, we revisit three fundamental and popular stochastic optimization algorithms (namely, Online Proximal Gradient, Regularized Dual Averaging method and ADMM with online proximal gradient) and analyze their convergence speed under conditions weaker than those in literature. In particular, previous works show... | [] | null | 70 | null | null | [
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Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing | https://proceedings.mlr.press/v48/lic16.html | [
"Ke Li",
"Jitendra Malik"
] | null | null | Existing methods for retrieving k-nearest neighbours suffer from the curse of dimensionality. We argue this is caused in part by inherent deficiencies of space partitioning, which is the underlying strategy used by most existing methods. We devise a new strategy that avoids partitioning the vector space and present a n... | [] | null | 71 | 1512.00442 | title_snapshot | [
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Smooth Imitation Learning for Online Sequence Prediction | https://proceedings.mlr.press/v48/le16.html | [
"Hoang Le",
"Andrew Kang",
"Yisong Yue",
"Peter Carr"
] | null | null | We study the problem of smooth imitation learning for online sequence prediction, where the goal is to train a policy that can smoothly imitate demonstrated behavior in a dynamic and continuous environment in response to online, sequential context input. Since the mapping from context to behavior is often complex, we t... | [] | null | 72 | 1606.00968 | title_snapshot | [
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Community Recovery in Graphs with Locality | https://proceedings.mlr.press/v48/chena16.html | [
"Yuxin Chen",
"Govinda Kamath",
"Changho Suh",
"David Tse"
] | null | null | Motivated by applications in domains such as social networks and computational biology, we study the problem of community recovery in graphs with locality. In this problem, pairwise noisy measurements of whether two nodes are in the same community or different communities come mainly or exclusively from nearby nodes ra... | [] | null | 73 | 1602.03828 | title_snapshot | [
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Variance Reduction for Faster Non-Convex Optimization | https://proceedings.mlr.press/v48/allen-zhua16.html | [
"Zeyuan Allen-Zhu",
"Elad Hazan"
] | null | null | We consider the fundamental problem in non-convex optimization of efficiently reaching a stationary point. In contrast to the convex case, in the long history of this basic problem, the only known theoretical results on first-order non-convex optimization remain to be full gradient descent that converges in O(1/\vareps... | [] | null | 74 | 1603.05643 | title_snapshot | [
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Loss factorization, weakly supervised learning and label noise robustness | https://proceedings.mlr.press/v48/patrini16.html | [
"Giorgio Patrini",
"Frank Nielsen",
"Richard Nock",
"Marcello Carioni"
] | null | null | We prove that the empirical risk of most well-known loss functions factors into a linear term aggregating all labels with a term that is label free, and can further be expressed by sums of the same loss. This holds true even for non-smooth, non-convex losses and in any RKHS. The first term is a (kernel) mean operator —... | [] | null | 75 | 1602.02450 | title_snapshot | [
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Analysis of Deep Neural Networks with Extended Data Jacobian Matrix | https://proceedings.mlr.press/v48/wanga16.html | [
"Shengjie Wang",
"Abdel-rahman Mohamed",
"Rich Caruana",
"Jeff Bilmes",
"Matthai Plilipose",
"Matthew Richardson",
"Krzysztof Geras",
"Gregor Urban",
"Ozlem Aslan"
] | null | null | Deep neural networks have achieved great successes on various machine learning tasks, however, there are many open fundamental questions to be answered. In this paper, we tackle the problem of quantifying the quality of learned wights of different networks with possibly different architectures, going beyond considering... | [] | null | 76 | null | null | [
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Doubly Decomposing Nonparametric Tensor Regression | https://proceedings.mlr.press/v48/imaizumi16.html | [
"Masaaki Imaizumi",
"Kohei Hayashi"
] | null | null | Nonparametric extension of tensor regression is proposed. Nonlinearity in a high-dimensional tensor space is broken into simple local functions by incorporating low-rank tensor decomposition. Compared to naive nonparametric approaches, our formulation considerably improves the convergence rate of estimation while maint... | [] | null | 77 | 1506.05967 | title_snapshot | [
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Hyperparameter optimization with approximate gradient | https://proceedings.mlr.press/v48/pedregosa16.html | [
"Fabian Pedregosa"
] | null | null | Most models in machine learning contain at least one hyperparameter to control for model complexity. Choosing an appropriate set of hyperparameters is both crucial in terms of model accuracy and computationally challenging. In this work we propose an algorithm for the optimization of continuous hyperparameters using in... | [] | null | 78 | 1602.02355 | title_snapshot | [
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SDCA without Duality, Regularization, and Individual Convexity | https://proceedings.mlr.press/v48/shalev-shwartza16.html | [
"Shai Shalev-Shwartz"
] | null | null | Stochastic Dual Coordinate Ascent is a popular method for solving regularized loss minimization for the case of convex losses. We describe variants of SDCA that do not require explicit regularization and do not rely on duality. We prove linear convergence rates even if individual loss functions are non-convex, as long ... | [] | null | 79 | 1602.01582 | title_snapshot | [
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Heteroscedastic Sequences: Beyond Gaussianity | https://proceedings.mlr.press/v48/anava16.html | [
"Oren Anava",
"Shie Mannor"
] | null | null | We address the problem of sequential prediction in the heteroscedastic setting, when both the signal and its variance are assumed to depend on explanatory variables. By applying regret minimization techniques, we devise an efficient online learning algorithm for the problem, without assuming that the error terms comply... | [] | null | 80 | null | null | [
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0.012... |
A Neural Autoregressive Approach to Collaborative Filtering | https://proceedings.mlr.press/v48/zheng16.html | [
"Yin Zheng",
"Bangsheng Tang",
"Wenkui Ding",
"Hanning Zhou"
] | null | null | This paper proposes CF-NADE, a neural autoregressive architecture for collaborative filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF model and the Neural Autoregressive Distribution Estimator (NADE). We first describe the basic CF-NADE model for CF tasks. Then we propose to imp... | [] | null | 81 | 1605.09477 | title_snapshot | [
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On the Quality of the Initial Basin in Overspecified Neural Networks | https://proceedings.mlr.press/v48/safran16.html | [
"Itay Safran",
"Ohad Shamir"
] | null | null | Deep learning, in the form of artificial neural networks, has achieved remarkable practical success in recent years, for a variety of difficult machine learning applications. However, a theoretical explanation for this remains a major open problem, since training neural networks involves optimizing a highly non-convex ... | [] | null | 82 | 1511.04210 | title_snapshot | [
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... |
Primal-Dual Rates and Certificates | https://proceedings.mlr.press/v48/dunner16.html | [
"Celestine Dünner",
"Simone Forte",
"Martin Takac",
"Martin Jaggi"
] | null | null | We propose an algorithm-independent framework to equip existing optimization methods with primal-dual certificates. Such certificates and corresponding rate of convergence guarantees are important for practitioners to diagnose progress, in particular in machine learning applications. We obtain new primal-dual convergen... | [] | null | 83 | 1602.05205 | title_snapshot | [
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... |
Minimizing the Maximal Loss: How and Why | https://proceedings.mlr.press/v48/shalev-shwartzb16.html | [
"Shai Shalev-Shwartz",
"Yonatan Wexler"
] | null | null | A commonly used learning rule is to approximately minimize the \emphaverage loss over the training set. Other learning algorithms, such as AdaBoost and hard-SVM, aim at minimizing the \emphmaximal loss over the training set. The average loss is more popular, particularly in deep learning, due to three main reasons. Fir... | [] | null | 84 | 1602.01690 | title_snapshot | [
-0.021990224719047546,
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-0.05624077096581459,
-0.031603459268808365,
0.0005175537080504,
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-... |
The Information-Theoretic Requirements of Subspace Clustering with Missing Data | https://proceedings.mlr.press/v48/pimentel-alarcon16.html | [
"Daniel Pimentel-Alarcon",
"Robert Nowak"
] | null | null | Subspace clustering with missing data (SCMD) is a useful tool for analyzing incomplete datasets. Let d be the ambient dimension, and r the dimension of the subspaces. Existing theory shows that Nk = O(r d) columns per subspace are necessary for SCMD, and Nk =O(min d^(log d), d^(r+1) ) are sufficient. We close this gap,... | [] | null | 85 | null | null | [
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0.0025257214438170195,
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-0.02653205767273903,
-0.03256555274128914,
0.008364219218492508,
-0.052436769008636475,
0... |
Online Learning with Feedback Graphs Without the Graphs | https://proceedings.mlr.press/v48/cohena16.html | [
"Alon Cohen",
"Tamir Hazan",
"Tomer Koren"
] | null | null | We study an online learning framework introduced by Mannor and Shamir (2011) in which the feedback is specified by a graph, in a setting where the graph may vary from round to round and is \emphnever fully revealed to the learner. We show a large gap between the adversarial and the stochastic cases. In the adversarial ... | [] | null | 86 | 1605.07018 | title_snapshot | [
-0.000604204717092216,
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-0.033746395260095596,
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-0.0760127454996109,
... |
PAC learning of Probabilistic Automaton based on the Method of Moments | https://proceedings.mlr.press/v48/glaude16.html | [
"Hadrien Glaude",
"Olivier Pietquin"
] | null | null | Probabilitic Finite Automata (PFA) are generative graphical models that define distributions with latent variables over finite sequences of symbols, a.k.a. stochastic languages. Traditionally, unsupervised learning of PFA is performed through algorithms that iteratively improves the likelihood like the Expectation-Maxi... | [] | null | 87 | null | null | [
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-0.03444648161530495,
0.013963945209980011,
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-0.07746686041355133,
-0... |
Estimating Structured Vector Autoregressive Models | https://proceedings.mlr.press/v48/melnyk16.html | [
"Igor Melnyk",
"Arindam Banerjee"
] | null | null | While considerable advances have been made in estimating high-dimensional structured models from independent data using Lasso-type models, limited progress has been made for settings when the samples are dependent. We consider estimating structured VAR (vector auto-regressive model), where the structure can be captured... | [] | null | 88 | 1602.06606 | title_judge | [
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Mixing Rates for the Alternating Gibbs Sampler over Restricted Boltzmann Machines and Friends | https://proceedings.mlr.press/v48/tosh16.html | [
"Christopher Tosh"
] | null | null | Alternating Gibbs sampling is a modification of classical Gibbs sampling where several variables are simultaneously sampled from their joint conditional distribution. In this work, we investigate the mixing rate of alternating Gibbs sampling with a particular emphasis on Restricted Boltzmann Machines (RBMs) and variant... | [] | null | 89 | null | null | [
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-... |
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms | https://proceedings.mlr.press/v48/blondel16.html | [
"Mathieu Blondel",
"Masakazu Ishihata",
"Akinori Fujino",
"Naonori Ueda"
] | null | null | Polynomial networks and factorization machines are two recently-proposed models that can efficiently use feature interactions in classification and regression tasks. In this paper, we revisit both models from a unified perspective. Based on this new view, we study the properties of both models and propose new efficient... | [] | null | 90 | 1607.08810 | title_snapshot | [
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0... |
A New PAC-Bayesian Perspective on Domain Adaptation | https://proceedings.mlr.press/v48/germain16.html | [
"Pascal Germain",
"Amaury Habrard",
"François Laviolette",
"Emilie Morvant"
] | null | null | We study the issue of PAC-Bayesian domain adaptation: We want to learn, from a source domain, a majority vote model dedicated to a target one. Our theoretical contribution brings a new perspective by deriving an upper-bound on the target risk where the distributions’ divergence - expressed as a ratio - controls the tra... | [] | null | 91 | 1506.04573 | title_snapshot | [
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0.... |
Correlation Clustering and Biclustering with Locally Bounded Errors | https://proceedings.mlr.press/v48/puleo16.html | [
"Gregory Puleo",
"Olgica Milenkovic"
] | null | null | We consider a generalized version of the correlation clustering problem, defined as follows. Given a complete graph G whose edges are labeled with + or -, we wish to partition the graph into clusters while trying to avoid errors: + edges between clusters or - edges within clusters. Classically, one seeks to minimize th... | [] | null | 92 | 1506.08189 | title_snapshot | [
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0.... |
PAC Lower Bounds and Efficient Algorithms for The Max K-Armed Bandit Problem | https://proceedings.mlr.press/v48/david16.html | [
"Yahel David",
"Nahum Shimkin"
] | null | null | We consider the Max K-Armed Bandit problem, where a learning agent is faced with several stochastic arms, each a source of i.i.d. rewards of unknown distribution. At each time step the agent chooses an arm, and observes the reward of the obtained sample. Each sample is considered here as a separate item with the reward... | [] | null | 93 | 1512.07650 | title_judge | [
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-0.03634483739733696,
-0.014617708511650562,
-0.006908652372658253,
-0.04926853999495506,
-0... |
A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose Estimation | https://proceedings.mlr.press/v48/elhoseiny16.html | [
"Mohamed Elhoseiny",
"Tarek El-Gaaly",
"Amr Bakry",
"Ahmed Elgammal"
] | null | null | In the Object Recognition task, there exists a dichotomy between the categorization of objects and estimating object pose, where the former necessitates a view-invariant representation, while the latter requires a representation capable of capturing pose information over different categories of objects. With the rise o... | [] | null | 94 | 1511.05175 | title_judge | [
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-0.027039891108870506,
-0.013238769955933094,
-0.08462442457675934,
... |
BASC: Applying Bayesian Optimization to the Search for Global Minima on Potential Energy Surfaces | https://proceedings.mlr.press/v48/carr16.html | [
"Shane Carr",
"Roman Garnett",
"Cynthia Lo"
] | null | null | We present a novel application of Bayesian optimization to the field of surface science: rapidly and accurately searching for the global minimum on potential energy surfaces. Controlling molecule-surface interactions is key for applications ranging from environmental catalysis to gas sensing. We present pragmatic techn... | [] | null | 95 | null | null | [
0.0030386466532945633,
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-0.06354827433824539,
0.029922502115368843,
0.028992293402552605,
-0.0344524160027504,
-... |
On the Iteration Complexity of Oblivious First-Order Optimization Algorithms | https://proceedings.mlr.press/v48/arjevani16.html | [
"Yossi Arjevani",
"Ohad Shamir"
] | null | null | We consider a broad class of first-order optimization algorithms which are \emphoblivious, in the sense that their step sizes are scheduled regardless of the function under consideration, except for limited side-information such as smoothness or strong convexity parameters. With the knowledge of these two parameters, w... | [] | null | 96 | 1605.03529 | title_snapshot | [
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0.01420209277421236,
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-0.01921989768743515,
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-0.054503146559000015,
-0... |
Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning | https://proceedings.mlr.press/v48/lid16.html | [
"Xingguo Li",
"Tuo Zhao",
"Raman Arora",
"Han Liu",
"Jarvis Haupt"
] | null | null | We propose a stochastic variance reduced optimization algorithm for solving a class of large-scale nonconvex optimization problems with cardinality constraints, and provide sufficient conditions under which the proposed algorithm enjoys strong linear convergence guarantees and optimal estimation accuracy in high dimens... | [] | null | 97 | null | null | [
-0.011159607209265232,
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0.005273884627968073,
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0.... |
Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation | https://proceedings.mlr.press/v48/wipf16.html | [
"David Wipf"
] | null | null | Variational Bayesian (VB) approximations anchor a wide variety of probabilistic models, where tractable posterior inference is almost never possible. Typically based on the so-called VB mean-field approximation to the Kullback-Leibler divergence, a posterior distribution is sought that factorizes across groups of laten... | [] | null | 98 | null | null | [
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-0.055063795298337936,
-0.019590774551033974,
0.007691520266234875,
-0.06221722811460495,
0.... |
Fast k-means with accurate bounds | https://proceedings.mlr.press/v48/newling16.html | [
"James Newling",
"Francois Fleuret"
] | null | null | We propose a novel accelerated exact k-means algorithm, which outperforms the current state-of-the-art low-dimensional algorithm in 18 of 22 experiments, running up to 3 times faster. We also propose a general improvement of existing state-of-the-art accelerated exact k-means algorithms through better estimates of the ... | [] | null | 99 | 1602.02514 | title_snapshot | [
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-0.0524996779859066,
0.0... |
Boolean Matrix Factorization and Noisy Completion via Message Passing | https://proceedings.mlr.press/v48/ravanbakhsha16.html | [
"Siamak Ravanbakhsh",
"Barnabas Poczos",
"Russell Greiner"
] | null | null | Boolean matrix factorization and Boolean matrix completion from noisy observations are desirable unsupervised data-analysis methods due to their interpretability, but hard to perform due to their NP-hardness. We treat these problems as maximum a posteriori inference problems in a graphical model and present a message p... | [] | null | 100 | 1509.08535 | title_snapshot | [
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-0.015385923907160759,
0.004899709485471249,
-0.05991994962096214,
0.00... |
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