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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
Inferring neural population dynamics from multiple partial recordings of the same neural circuit | https://proceedings.neurips.cc/paper_files/paper/2013/hash/01386bd6d8e091c2ab4c7c7de644d37b-Abstract.html | [
"Srini Turaga",
"Lars Buesing",
"Adam M Packer",
"Henry Dalgleish",
"Noah Pettit",
"Michael Hausser",
"Jakob H Macke"
] | null | null | Simultaneous recordings of the activity of large neural populations are extremely valuable as they can be used to infer the dynamics and interactions of neurons in a local circuit, shedding light on the computations performed. It is now possible to measure the activity of hundreds of neurons using 2-photon calcium imag... | [] | null | 1 | null | null | [
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Approximate Gaussian process inference for the drift function in stochastic differential equations | https://proceedings.neurips.cc/paper_files/paper/2013/hash/021bbc7ee20b71134d53e20206bd6feb-Abstract.html | [
"Andreas Ruttor",
"Philipp Batz",
"Manfred Opper"
] | null | null | We introduce a nonparametric approach for estimating drift functions in systems of stochastic differential equations from incomplete observations of the state vector. Using a Gaussian process prior over the drift as a function of the state vector, we develop an approximate EM algorithm to deal with the unobserved, late... | [] | null | 2 | null | null | [
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Third-Order Edge Statistics: Contour Continuation, Curvature, and Cortical Connections | https://proceedings.neurips.cc/paper_files/paper/2013/hash/024d7f84fff11dd7e8d9c510137a2381-Abstract.html | [
"Matthew Lawlor",
"Steven W Zucker"
] | null | null | Association field models have been used to explain human contour grouping performance and to explain the mean frequency of long-range horizontal connections across cortical columns in V1. However, association fields essentially depend on pairwise statistics of edges in natural scenes. We develop a spectral test of the ... | [] | null | 3 | 1306.3285 | title_snapshot | [
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Transportability from Multiple Environments with Limited Experiments | https://proceedings.neurips.cc/paper_files/paper/2013/hash/02522a2b2726fb0a03bb19f2d8d9524d-Abstract.html | [
"Elias Bareinboim",
"Sanghack Lee",
"Vasant Honavar",
"Judea Pearl"
] | null | null | This paper considers the problem of transferring experimental findings learned from multiple heterogeneous domains to a target environment, in which only limited experiments can be performed. We reduce questions of transportability from multiple domains and with limited scope to symbolic derivations in the do-calculus,... | [] | null | 4 | null | null | [
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On model selection consistency of penalized M-estimators: a geometric theory | https://proceedings.neurips.cc/paper_files/paper/2013/hash/0266e33d3f546cb5436a10798e657d97-Abstract.html | [
"Jason Lee",
"Yuekai Sun",
"Jonathan E Taylor"
] | null | null | Penalized M-estimators are used in diverse areas of science and engineering to fit high-dimensional models with some low-dimensional structure. Often, the penalties are \emph{geometrically decomposable}, \ie\ can be expressed as a sum of (convex) support functions. We generalize the notion of irrepresentable to geometr... | [] | null | 5 | null | null | [
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Robust Bloom Filters for Large MultiLabel Classification Tasks | https://proceedings.neurips.cc/paper_files/paper/2013/hash/043c3d7e489c69b48737cc0c92d0f3a2-Abstract.html | [
"Moustapha M Cisse",
"Nicolas Usunier",
"Thierry Artières",
"Patrick Gallinari"
] | null | null | This paper presents an approach to multilabel classification (MLC) with a large number of labels. Our approach is a reduction to binary classification in which label sets are represented by low dimensional binary vectors. This representation follows the principle of Bloom filters, a space-efficient data structure origi... | [] | null | 6 | null | null | [
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On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation | https://proceedings.neurips.cc/paper_files/paper/2013/hash/05311655a15b75fab86956663e1819cd-Abstract.html | [
"Harikrishna Narasimhan",
"Shivani Agarwal"
] | null | null | We investigate the relationship between three fundamental problems in machine learning: binary classification, bipartite ranking, and binary class probability estimation (CPE). It is known that a good binary CPE model can be used to obtain a good binary classification model (by thresholding at 0.5), and also to obtain ... | [] | null | 7 | null | null | [
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Sequential Transfer in Multi-armed Bandit with Finite Set of Models | https://proceedings.neurips.cc/paper_files/paper/2013/hash/062ddb6c727310e76b6200b7c71f63b5-Abstract.html | [
"Mohammad Gheshlaghi azar",
"Alessandro Lazaric",
"Emma Brunskill"
] | null | null | Learning from prior tasks and transferring that experience to improve future performance is critical for building lifelong learning agents. Although results in supervised and reinforcement learning show that transfer may significantly improve the learning performance, most of the literature on transfer is focused on ba... | [] | null | 8 | 1307.6887 | title_snapshot | [
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A Graphical Transformation for Belief Propagation: Maximum Weight Matchings and Odd-Sized Cycles | https://proceedings.neurips.cc/paper_files/paper/2013/hash/0768281a05da9f27df178b5c39a51263-Abstract.html | [
"Jinwoo Shin",
"Andrew E Gelfand",
"Misha Chertkov"
] | null | null | Max-product ‘belief propagation’ (BP) is a popular distributed heuristic for finding the Maximum A Posteriori (MAP) assignment in a joint probability distribution represented by a Graphical Model (GM). It was recently shown that BP converges to the correct MAP assignment for a class of loopy GMs with the following comm... | [] | null | 9 | 1306.1167 | title_snapshot | [
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A Kernel Test for Three-Variable Interactions | https://proceedings.neurips.cc/paper_files/paper/2013/hash/076a0c97d09cf1a0ec3e19c7f2529f2b-Abstract.html | [
"Dino Sejdinovic",
"Arthur Gretton",
"Wicher Bergsma"
] | null | null | We introduce kernel nonparametric tests for Lancaster three-variable interaction and for total independence, using embeddings of signed measures into a reproducing kernel Hilbert space. The resulting test statistics are straightforward to compute, and are used in powerful three-variable interaction tests, which are con... | [] | null | 10 | 1306.2281 | title_snapshot | [
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Accelerated Mini-Batch Stochastic Dual Coordinate Ascent | https://proceedings.neurips.cc/paper_files/paper/2013/hash/077e29b11be80ab57e1a2ecabb7da330-Abstract.html | [
"Shai Shalev-Shwartz",
"Tong Zhang"
] | null | null | Stochastic dual coordinate ascent (SDCA) is an effective technique for solving regularized loss minimization problems in machine learning. This paper considers an extension of SDCA under the mini-batch setting that is often used in practice. Our main contribution is to introduce an accelerated mini-batch version of SDC... | [] | null | 11 | 1305.2581 | title_snapshot | [
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A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks | https://proceedings.neurips.cc/paper_files/paper/2013/hash/07cdfd23373b17c6b337251c22b7ea57-Abstract.html | [
"Junming Yin",
"Qirong Ho",
"Eric P Xing"
] | null | null | We propose a scalable approach for making inference about latent spaces of large networks. With a succinct representation of networks as a bag of triangular motifs, a parsimonious statistical model, and an efficient stochastic variational inference algorithm, we are able to analyze real networks with over a million ver... | [] | null | 12 | null | null | [
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Multi-Prediction Deep Boltzmann Machines | https://proceedings.neurips.cc/paper_files/paper/2013/hash/0bb4aec1710521c12ee76289d9440817-Abstract.html | [
"Ian Goodfellow",
"Mehdi Mirza",
"Aaron Courville",
"Yoshua Bengio"
] | null | null | We introduce the Multi-Prediction Deep Boltzmann Machine (MP-DBM). The MP-DBM can be seen as a single probabilistic model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent nets that share parameters and approximately solve different inference problems. Prio... | [] | null | 13 | null | null | [
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Learning and using language via recursive pragmatic reasoning about other agents | https://proceedings.neurips.cc/paper_files/paper/2013/hash/0c0a7566915f4f24853fc4192689aa7e-Abstract.html | [
"Nathaniel J Smith",
"Noah Goodman",
"Michael Frank"
] | null | null | Language users are remarkably good at making inferences about speakers' intentions in context, and children learning their native language also display substantial skill in acquiring the meanings of unknown words. These two cases are deeply related: Language users invent new terms in conversation, and language learners... | [] | null | 14 | null | null | [
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Reinforcement Learning in Robust Markov Decision Processes | https://proceedings.neurips.cc/paper_files/paper/2013/hash/0deb1c54814305ca9ad266f53bc82511-Abstract.html | [
"Shiau Hong Lim",
"Huan Xu",
"Shie Mannor"
] | null | null | An important challenge in Markov decision processes is to ensure robustness with respect to unexpected or adversarial system behavior while taking advantage of well-behaving parts of the system. We consider a problem setting where some unknown parts of the state space can have arbitrary transitions while other parts ar... | [] | null | 15 | null | null | [
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Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel | https://proceedings.neurips.cc/paper_files/paper/2013/hash/0ed9422357395a0d4879191c66f4faa2-Abstract.html | [
"Tai Qin",
"Karl Rohe"
] | null | null | Spectral clustering is a fast and popular algorithm for finding clusters in networks. Recently, Chaudhuri et al. and Amini et al. proposed variations on the algorithm that artificially inflate the node degrees for improved statistical performance. The current paper extends the previous theoretical results to the more c... | [] | null | 16 | 1309.4111 | title_snapshot | [
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A Novel Two-Step Method for Cross Language Representation Learning | https://proceedings.neurips.cc/paper_files/paper/2013/hash/0ff39bbbf981ac0151d340c9aa40e63e-Abstract.html | [
"Min Xiao",
"Yuhong Guo"
] | null | null | Cross language text classification is an important learning task in natural language processing. A critical challenge of cross language learning lies in that words of different languages are in disjoint feature spaces. In this paper, we propose a two-step representation learning method to bridge the feature spaces of di... | [] | null | 17 | null | null | [
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Graphical Models for Inference with Missing Data | https://proceedings.neurips.cc/paper_files/paper/2013/hash/0ff8033cf9437c213ee13937b1c4c455-Abstract.html | [
"Karthika Mohan",
"Judea Pearl",
"Jin Tian"
] | null | null | We address the problem of deciding whether there exists a consistent estimator of a given relation Q, when data are missing not at random. We employ a formal representation called `Missingness Graphs' to explicitly portray the causal mechanisms responsible for missingness and to encode dependencies between these mechan... | [] | null | 18 | null | null | [
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Convex Tensor Decomposition via Structured Schatten Norm Regularization | https://proceedings.neurips.cc/paper_files/paper/2013/hash/109a0ca3bc27f3e96597370d5c8cf03d-Abstract.html | [
"Ryota Tomioka",
"Taiji Suzuki"
] | null | null | We propose a new class of structured Schatten norms for tensors that includes two recently proposed norms (overlapped'' and "latent'') for convex-optimization-based tensor decomposition. Based on the properties of the structured Schatten norms, we mathematically analyze the performance of "latent'' approach for tensor ... | [] | null | 19 | 1303.6370 | title_snapshot | [
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Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression | https://proceedings.neurips.cc/paper_files/paper/2013/hash/115f89503138416a242f40fb7d7f338e-Abstract.html | [
"Michalis Titsias RC AUEB",
"Miguel Lazaro-Gredilla"
] | null | null | We introduce a novel variational method that allows to approximately integrate out kernel hyperparameters, such as length-scales, in Gaussian process regression. This approach consists of a novel variant of the variational framework that has been recently developed for the Gaussian process latent variable model which a... | [] | null | 20 | null | null | [
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Efficient Online Inference for Bayesian Nonparametric Relational Models | https://proceedings.neurips.cc/paper_files/paper/2013/hash/13f320e7b5ead1024ac95c3b208610db-Abstract.html | [
"Dae Il Kim",
"Prem Gopalan",
"David Blei",
"Erik Sudderth"
] | null | null | Stochastic block models characterize observed network relationships via latent community memberships. In large social networks, we expect entities to participate in multiple communities, and the number of communities to grow with the network size. We introduce a new model for these phenomena, the hierarchical Dirichlet... | [] | null | 21 | null | null | [
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Convergence of Monte Carlo Tree Search in Simultaneous Move Games | https://proceedings.neurips.cc/paper_files/paper/2013/hash/1579779b98ce9edb98dd85606f2c119d-Abstract.html | [
"Viliam Lisy",
"Vojta Kovarik",
"Marc Lanctot",
"Branislav Bosansky"
] | null | null | In this paper, we study Monte Carlo tree search (MCTS) in zero-sum extensive-form games with perfect information and simultaneous moves. We present a general template of MCTS algorithms for these games, which can be instantiated by various selection methods. We formally prove that if a selection method is $\epsilon$-Ha... | [] | null | 22 | 1310.8613 | title_snapshot | [
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Learning to Pass Expectation Propagation Messages | https://proceedings.neurips.cc/paper_files/paper/2013/hash/1714726c817af50457d810aae9d27a2e-Abstract.html | [
"Nicolas Heess",
"Daniel Tarlow",
"John Winn"
] | null | null | Expectation Propagation (EP) is a popular approximate posterior inference algorithm that often provides a fast and accurate alternative to sampling-based methods. However, while the EP framework in theory allows for complex non-Gaussian factors, there is still a significant practical barrier to using them within EP, be... | [] | null | 23 | null | null | [
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Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits | https://proceedings.neurips.cc/paper_files/paper/2013/hash/17c276c8e723eb46aef576537e9d56d0-Abstract.html | [
"Ben Shababo",
"Brooks Paige",
"Ari Pakman",
"Liam Paninski"
] | null | null | We develop an inference and optimal design procedure for recovering synaptic weights in neural microcircuits. We base our procedure on data from an experiment in which populations of putative presynaptic neurons can be stimulated while a subthreshold recording is made from a single postsynaptic neuron. We present a rea... | [] | null | 24 | null | null | [
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Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization | https://proceedings.neurips.cc/paper_files/paper/2013/hash/184260348236f9554fe9375772ff966e-Abstract.html | [
"Nataliya Shapovalova",
"Michalis Raptis",
"Leonid Sigal",
"Greg Mori"
] | null | null | We propose a new weakly-supervised structured learning approach for recognition and spatio-temporal localization of actions in video. As part of the proposed approach we develop a generalization of the Max-Path search algorithm, which allows us to efficiently search over a structured space of multiple spatio-temporal p... | [] | null | 25 | null | null | [
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Integrated Non-Factorized Variational Inference | https://proceedings.neurips.cc/paper_files/paper/2013/hash/1896a3bf730516dd643ba67b4c447d36-Abstract.html | [
"Shaobo Han",
"Xuejun Liao",
"Lawrence Carin"
] | null | null | We present a non-factorized variational method for full posterior inference in Bayesian hierarchical models, with the goal of capturing the posterior variable dependencies via efficient and possibly parallel computation. Our approach unifies the integrated nested Laplace approximation (INLA) under the variational frame... | [] | null | 26 | null | null | [
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A Gang of Bandits | https://proceedings.neurips.cc/paper_files/paper/2013/hash/18997733ec258a9fcaf239cc55d53363-Abstract.html | [
"Nicolò Cesa-Bianchi",
"Claudio Gentile",
"Giovanni Zappella"
] | null | null | Multi-armed bandit problems are receiving a great deal of attention because they adequately formalize the exploration-exploitation trade-offs arising in several industrially relevant applications, such as online advertisement and, more generally, recommendation systems. In many cases, however, these applications have a... | [] | null | 27 | 1306.0811 | title_snapshot | [
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Multiclass Total Variation Clustering | https://proceedings.neurips.cc/paper_files/paper/2013/hash/19bc916108fc6938f52cb96f7e087941-Abstract.html | [
"Xavier Bresson",
"Thomas Laurent",
"David Uminsky",
"James von Brecht"
] | null | null | Ideas from the image processing literature have recently motivated a new set of clustering algorithms that rely on the concept of total variation. While these algorithms perform well for bi-partitioning tasks, their recursive extensions yield unimpressive results for multiclass clustering tasks. This paper presents a g... | [] | null | 28 | 1306.1185 | title_snapshot | [
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Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors | https://proceedings.neurips.cc/paper_files/paper/2013/hash/1aa48fc4880bb0c9b8a3bf979d3b917e-Abstract.html | [
"Xiaoqin Zhang",
"Di Wang",
"Zhengyuan Zhou",
"Yi Ma"
] | null | null | In this work, we propose a general method for recovering low-rank three-order tensors, in which the data can be deformed by some unknown transformation and corrupted by arbitrary sparse errors. Since the unfolding matrices of a tensor are interdependent, we introduce auxiliary variables and relax the hard equality cons... | [] | null | 29 | null | null | [
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BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables | https://proceedings.neurips.cc/paper_files/paper/2013/hash/1abb1e1ea5f481b589da52303b091cbb-Abstract.html | [
"Cho-Jui Hsieh",
"Matyas A Sustik",
"Inderjit S Dhillon",
"Pradeep K Ravikumar",
"Russell Poldrack"
] | null | null | The l1-regularized Gaussian maximum likelihood estimator (MLE) has been shown to have strong statistical guarantees in recovering a sparse inverse covariance matrix even under high-dimensional settings. However, it requires solving a difficult non-smooth log-determinant program with number of parameters scaling quadrat... | [] | null | 30 | null | null | [
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Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching | https://proceedings.neurips.cc/paper_files/paper/2013/hash/1afa34a7f984eeabdbb0a7d494132ee5-Abstract.html | [
"Marcelo Fiori",
"Pablo Sprechmann",
"Joshua Vogelstein",
"Pablo Muse",
"Guillermo Sapiro"
] | null | null | Graph matching is a challenging problem with very important applications in a wide range of fields, from image and video analysis to biological and biomedical problems. We propose a robust graph matching algorithm inspired in sparsity-related techniques. We cast the problem, resembling group or collaborative sparsity f... | [] | null | 31 | 1311.6425 | title_snapshot | [
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Optimal integration of visual speed across different spatiotemporal frequency channels | https://proceedings.neurips.cc/paper_files/paper/2013/hash/1baff70e2669e8376347efd3a874a341-Abstract.html | [
"Matjaz Jogan",
"Alan Stocker"
] | null | null | How does the human visual system compute the speed of a coherent motion stimulus that contains motion energy in different spatiotemporal frequency bands? Here we propose that perceived speed is the result of optimal integration of speed information from independent spatiotemporal frequency tuned channels. We formalize ... | [] | null | 32 | null | null | [
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Translating Embeddings for Modeling Multi-relational Data | https://proceedings.neurips.cc/paper_files/paper/2013/hash/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html | [
"Antoine Bordes",
"Nicolas Usunier",
"Alberto Garcia-Duran",
"Jason Weston",
"Oksana Yakhnenko"
] | null | null | We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose, TransE, a method which mod... | [] | null | 33 | null | null | [
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Synthesizing Robust Plans under Incomplete Domain Models | https://proceedings.neurips.cc/paper_files/paper/2013/hash/1e1d184167ca7676cf665225e236a3d2-Abstract.html | [
"Tuan A Nguyen",
"Subbarao Kambhampati",
"Minh Do"
] | null | null | Most current planners assume complete domain models and focus on generating correct plans. Unfortunately, domain modeling is a laborious and error-prone task, thus real world agents have to plan with incomplete domain models. While domain experts cannot guarantee completeness, often they are able to circumscribe the in... | [] | null | 34 | 1104.5069 | title_snapshot | [
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Learning Gaussian Graphical Models with Observed or Latent FVSs | https://proceedings.neurips.cc/paper_files/paper/2013/hash/1f4477bad7af3616c1f933a02bfabe4e-Abstract.html | [
"Ying Liu",
"Alan Willsky"
] | null | null | Gaussian Graphical Models (GGMs) or Gauss Markov random fields are widely used in many applications, and the trade-off between the modeling capacity and the efficiency of learning and inference has been an important research problem. In this paper, we study the family of GGMs with small feedback vertex sets (FVSs), whe... | [] | null | 35 | 1311.2241 | title_snapshot | [
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Extracting regions of interest from biological images with convolutional sparse block coding | https://proceedings.neurips.cc/paper_files/paper/2013/hash/1f50893f80d6830d62765ffad7721742-Abstract.html | [
"Marius Pachitariu",
"Adam M Packer",
"Noah Pettit",
"Henry Dalgleish",
"Michael Hausser",
"Maneesh Sahani"
] | null | null | Biological tissue is often composed of cells with similar morphologies replicated throughout large volumes and many biological applications rely on the accurate identification of these cells and their locations from image data. Here we develop a generative model that captures the regularities present in images composed... | [] | null | 36 | null | null | [
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Training and Analysing Deep Recurrent Neural Networks | https://proceedings.neurips.cc/paper_files/paper/2013/hash/1ff8a7b5dc7a7d1f0ed65aaa29c04b1e-Abstract.html | [
"Michiel Hermans",
"Benjamin Schrauwen"
] | null | null | Time series often have a temporal hierarchy, with information that is spread out over multiple time scales. Common recurrent neural networks, however, do not explicitly accommodate such a hierarchy, and most research on them has been focusing on training algorithms rather than on their basic architecture. In this pa- p... | [] | null | 37 | null | null | [
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Low-Rank Matrix and Tensor Completion via Adaptive Sampling | https://proceedings.neurips.cc/paper_files/paper/2013/hash/2050e03ca119580f74cca14cc6e97462-Abstract.html | [
"Akshay Krishnamurthy",
"Aarti Singh"
] | null | null | We study low rank matrix and tensor completion and propose novel algorithms that employ adaptive sampling schemes to obtain strong performance guarantees for these problems. Our algorithms exploit adaptivity to identify entries that are highly informative for identifying the column space of the matrix (tensor) and cons... | [] | null | 38 | 1304.4672 | title_snapshot | [
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Fast Determinantal Point Process Sampling with Application to Clustering | https://proceedings.neurips.cc/paper_files/paper/2013/hash/20d135f0f28185b84a4cf7aa51f29500-Abstract.html | [
"Byungkon Kang"
] | null | null | Determinantal Point Process (DPP) has gained much popularity for modeling sets of diverse items. The gist of DPP is that the probability of choosing a particular set of items is proportional to the determinant of a positive definite matrix that defines the similarity of those items. However, computing the determinant r... | [] | null | 39 | null | null | [
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Matrix factorization with binary components | https://proceedings.neurips.cc/paper_files/paper/2013/hash/226d1f15ecd35f784d2a20c3ecf56d7f-Abstract.html | [
"Martin Slawski",
"Matthias Hein",
"Pavlo Lutsik"
] | null | null | Motivated by an application in computational biology, we consider constrained low-rank matrix factorization problems with $\{0,1\}$-constraints on one of the factors. In addition to the the non-convexity shared with more general matrix factorization schemes, our problem is further complicated by a combinatorial constra... | [] | null | 40 | 1401.6024 | title_snapshot | [
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Reshaping Visual Datasets for Domain Adaptation | https://proceedings.neurips.cc/paper_files/paper/2013/hash/2291d2ec3b3048d1a6f86c2c4591b7e0-Abstract.html | [
"Boqing Gong",
"Kristen Grauman",
"Fei Sha"
] | null | null | In visual recognition problems, the common data distribution mismatches between training and testing make domain adaptation essential. However, image data is difficult to manually divide into the discrete domains required by adaptation algorithms, and the standard practice of equating datasets with domains is a weak pr... | [] | null | 41 | null | null | [
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Perfect Associative Learning with Spike-Timing-Dependent Plasticity | https://proceedings.neurips.cc/paper_files/paper/2013/hash/22fb0cee7e1f3bde58293de743871417-Abstract.html | [
"Christian Albers",
"Maren Westkott",
"Klaus Pawelzik"
] | null | null | Recent extensions of the Perceptron, as e.g. the Tempotron, suggest that this theoretical concept is highly relevant also for understanding networks of spiking neurons in the brain. It is not known, however, how the computational power of the Perceptron and of its variants might be accomplished by the plasticity mechan... | [] | null | 42 | null | null | [
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Tracking Time-varying Graphical Structure | https://proceedings.neurips.cc/paper_files/paper/2013/hash/233509073ed3432027d48b1a83f5fbd2-Abstract.html | [
"Erich Kummerfeld",
"David Danks"
] | null | null | Structure learning algorithms for graphical models have focused almost exclusively on stable environments in which the underlying generative process does not change; that is, they assume that the generating model is globally stationary. In real-world environments, however, such changes often occur without warning or si... | [] | null | 43 | null | null | [
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Phase Retrieval using Alternating Minimization | https://proceedings.neurips.cc/paper_files/paper/2013/hash/242c100dc94f871b6d7215b868a875f8-Abstract.html | [
"Praneeth Netrapalli",
"Prateek Jain",
"Sujay Sanghavi"
] | null | null | Phase retrieval problems involve solving linear equations, but with missing sign (or phase, for complex numbers) information. Over the last two decades, a popular generic empirical approach to the many variants of this problem has been one of alternating minimization; i.e. alternating between estimating the missing pha... | [] | null | 44 | 1306.0160 | title_snapshot | [
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Unsupervised Structure Learning of Stochastic And-Or Grammars | https://proceedings.neurips.cc/paper_files/paper/2013/hash/24681928425f5a9133504de568f5f6df-Abstract.html | [
"Kewei Tu",
"Maria Pavlovskaia",
"Song-Chun Zhu"
] | null | null | Stochastic And-Or grammars compactly represent both compositionality and reconfigurability and have been used to model different types of data such as images and events. We present a unified formalization of stochastic And-Or grammars that is agnostic to the type of the data being modeled, and propose an unsupervised a... | [] | null | 45 | null | null | [
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Learning Multi-level Sparse Representations | https://proceedings.neurips.cc/paper_files/paper/2013/hash/26337353b7962f533d78c762373b3318-Abstract.html | [
"Ferran Diego Andilla",
"Fred A. Hamprecht"
] | null | null | Bilinear approximation of a matrix is a powerful paradigm of unsupervised learning. In some applications, however, there is a natural hierarchy of concepts that ought to be reflected in the unsupervised analysis. For example, in the neurosciences image sequence considered here, there are the semantic concepts of pixel ... | [] | null | 46 | null | null | [
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Estimation, Optimization, and Parallelism when Data is Sparse | https://proceedings.neurips.cc/paper_files/paper/2013/hash/2812e5cf6d8f21d69c91dddeefb792a7-Abstract.html | [
"John Duchi",
"Michael I Jordan",
"Brendan McMahan"
] | null | null | We study stochastic optimization problems when the \emph{data} is sparse, which is in a sense dual to the current understanding of high-dimensional statistical learning and optimization. We highlight both the difficulties---in terms of increased sample complexity that sparse data necessitates---and the potential benefi... | [] | null | 47 | null | null | [
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Predictive PAC Learning and Process Decompositions | https://proceedings.neurips.cc/paper_files/paper/2013/hash/28267ab848bcf807b2ed53c3a8f8fc8a-Abstract.html | [
"Cosma Shalizi",
"Aryeh Kontorovich"
] | null | null | We informally call a stochastic process learnable if it admits a generalization error approaching zero in probability for any concept class with finite VC-dimension (IID processes are the simplest example). A mixture of learnable processes need not be learnable itself, and certainly its generalization error need not de... | [] | null | 48 | 1309.4859 | title_snapshot | [
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Scalable Inference for Logistic-Normal Topic Models | https://proceedings.neurips.cc/paper_files/paper/2013/hash/285f89b802bcb2651801455c86d78f2a-Abstract.html | [
"Jianfei Chen",
"Jun Zhu",
"Zi Wang",
"Xun Zheng",
"Bo Zhang"
] | null | null | Logistic-normal topic models can effectively discover correlation structures among latent topics. However, their inference remains a challenge because of the non-conjugacy between the logistic-normal prior and multinomial topic mixing proportions. Existing algorithms either make restricting mean-field assumptions or ar... | [] | null | 49 | null | null | [
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A multi-agent control framework for co-adaptation in brain-computer interfaces | https://proceedings.neurips.cc/paper_files/paper/2013/hash/286674e3082feb7e5afb92777e48821f-Abstract.html | [
"Josh S Merel",
"Roy Fox",
"Tony Jebara",
"Liam Paninski"
] | null | null | In a closed-loop brain-computer interface (BCI), adaptive decoders are used to learn parameters suited to decoding the user's neural response. Feedback to the user provides information which permits the neural tuning to also adapt. We present an approach to model this process of co-adaptation between the encoding model... | [] | null | 50 | null | null | [
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Conditional Random Fields via Univariate Exponential Families | https://proceedings.neurips.cc/paper_files/paper/2013/hash/28f0b864598a1291557bed248a998d4e-Abstract.html | [
"Eunho Yang",
"Pradeep K Ravikumar",
"Genevera I Allen",
"Zhandong Liu"
] | null | null | Conditional random fields, which model the distribution of a multivariate response conditioned on a set of covariates using undirected graphs, are widely used in a variety of multivariate prediction applications. Popular instances of this class of models such as categorical-discrete CRFs, Ising CRFs, and conditional Ga... | [] | null | 51 | null | null | [
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Adaptivity to Local Smoothness and Dimension in Kernel Regression | https://proceedings.neurips.cc/paper_files/paper/2013/hash/28fc2782ea7ef51c1104ccf7b9bea13d-Abstract.html | [
"Samory Kpotufe",
"Vikas Garg"
] | null | null | We present the first result for kernel regression where the procedure adapts locally at a point $x$ to both the unknown local dimension of the metric and the unknown H\{o}lder-continuity of the regression function at $x$. The result holds with high probability simultaneously at all points $x$ in a metric space of unkno... | [] | null | 52 | null | null | [
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Online Learning with Costly Features and Labels | https://proceedings.neurips.cc/paper_files/paper/2013/hash/291597a100aadd814d197af4f4bab3a7-Abstract.html | [
"Navid Zolghadr",
"Gabor Bartok",
"Russell Greiner",
"András György",
"Csaba Szepesvari"
] | null | null | This paper introduces the online probing" problem: In each round, the learner is able to purchase the values of a subset of feature values. After the learner uses this information to come up with a prediction for the given round, he then has the option of paying for seeing the loss that he is evaluated against. Either ... | [] | null | 53 | null | null | [
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An Approximate, Efficient LP Solver for LP Rounding | https://proceedings.neurips.cc/paper_files/paper/2013/hash/2a50e9c2d6b89b95bcb416d6857f8b45-Abstract.html | [
"Srikrishna Sridhar",
"Stephen Wright",
"Christopher Re",
"Ji Liu",
"Victor Bittorf",
"Ce Zhang"
] | null | null | Many problems in machine learning can be solved by rounding the solution of an appropriate linear program. We propose a scheme that is based on a quadratic program relaxation which allows us to use parallel stochastic-coordinate-descent to approximately solve large linear programs efficiently. Our software is an order ... | [] | null | 54 | null | null | [
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Regression-tree Tuning in a Streaming Setting | https://proceedings.neurips.cc/paper_files/paper/2013/hash/2a9d121cd9c3a1832bb6d2cc6bd7a8a7-Abstract.html | [
"Samory Kpotufe",
"Francesco Orabona"
] | null | null | We consider the problem of maintaining the data-structures of a partition-based regression procedure in a setting where the training data arrives sequentially over time. We prove that it is possible to maintain such a structure in time $O(\log n)$ at any time step $n$ while achieving a nearly-optimal regression rate of... | [] | null | 55 | null | null | [
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Estimating LASSO Risk and Noise Level | https://proceedings.neurips.cc/paper_files/paper/2013/hash/2b8a61594b1f4c4db0902a8a395ced93-Abstract.html | [
"Mohsen Bayati",
"Murat A Erdogdu",
"Andrea Montanari"
] | null | null | We study the fundamental problems of variance and risk estimation in high dimensional statistical modeling. In particular, we consider the problem of learning a coefficient vector $\theta_0\in R^p$ from noisy linear observation $y=X\theta_0+w\in R^n$ and the popular estimation procedure of solving an $\ell_1$-penalized... | [] | null | 56 | null | null | [
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Demixing odors - fast inference in olfaction | https://proceedings.neurips.cc/paper_files/paper/2013/hash/2bcab9d935d219641434683dd9d18a03-Abstract.html | [
"Agnieszka Grabska-Barwinska",
"Jeff Beck",
"Alexandre Pouget",
"Peter Latham"
] | null | null | The olfactory system faces a difficult inference problem: it has to determine what odors are present based on the distributed activation of its receptor neurons. Here we derive neural implementations of two approximate inference algorithms that could be used by the brain. One is a variational algorithm (which builds on... | [] | null | 57 | null | null | [
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Zero-Shot Learning Through Cross-Modal Transfer | https://proceedings.neurips.cc/paper_files/paper/2013/hash/2d6cc4b2d139a53512fb8cbb3086ae2e-Abstract.html | [
"Richard Socher",
"Milind Ganjoo",
"Christopher D. Manning",
"Andrew Ng"
] | null | null | This work introduces a model that can recognize objects in images even if no training data is available for the object class. The only necessary knowledge about unseen categories comes from unsupervised text corpora. Unlike previous zero-shot learning models, which can only differentiate between unseen classes, our mod... | [] | null | 58 | 1301.3666 | title_snapshot | [
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Optimistic policy iteration and natural actor-critic: A unifying view and a non-optimality result | https://proceedings.neurips.cc/paper_files/paper/2013/hash/2dace78f80bc92e6d7493423d729448e-Abstract.html | [
"Paul Wagner"
] | null | null | Approximate dynamic programming approaches to the reinforcement learning problem are often categorized into greedy value function methods and value-based policy gradient methods. As our first main result, we show that an important subset of the latter methodology is, in fact, a limiting special case of a general formul... | [] | null | 59 | null | null | [
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Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC | https://proceedings.neurips.cc/paper_files/paper/2013/hash/2dffbc474aa176b6dc957938c15d0c8b-Abstract.html | [
"Roger Frigola",
"Fredrik Lindsten",
"Thomas B Schön",
"Carl Edward Rasmussen"
] | null | null | State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning in nonlinear nonparametric state-space models. We place a Gaussian process prior over the transition dynamics, resulting... | [] | null | 60 | 1306.2861 | title_snapshot | [
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Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex | https://proceedings.neurips.cc/paper_files/paper/2013/hash/309928d4b100a5d75adff48a9bfc1ddb-Abstract.html | [
"Sam Patterson",
"Yee Whye Teh"
] | null | null | In this paper we investigate the use of Langevin Monte Carlo methods on the probability simplex and propose a new method, Stochastic gradient Riemannian Langevin dynamics, which is simple to implement and can be applied online. We apply this method to latent Dirichlet allocation in an online setting, and demonstrate th... | [] | null | 61 | null | null | [
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When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity | https://proceedings.neurips.cc/paper_files/paper/2013/hash/31b3b31a1c2f8a370206f111127c0dbd-Abstract.html | [
"Anima Anandkumar",
"Daniel J. Hsu",
"Majid Janzamin",
"Sham M. Kakade"
] | null | null | Overcomplete latent representations have been very popular for unsupervised feature learning in recent years. In this paper, we specify which overcomplete models can be identified given observable moments of a certain order. We consider probabilistic admixture or topic models in the overcomplete regime, where the numbe... | [] | null | 62 | 1308.2853 | title_snapshot | [
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Sign Cauchy Projections and Chi-Square Kernel | https://proceedings.neurips.cc/paper_files/paper/2013/hash/3210ddbeaa16948a702b6049b8d9a202-Abstract.html | [
"Ping Li",
"Gennady Samorodnitsk",
"John Hopcroft"
] | null | null | The method of Cauchy random projections is popular for computing the $l_1$ distance in high dimension. In this paper, we propose to use only the signs of the projected data and show that the probability of collision (i.e., when the two signs differ) can be accurately approximated as a function of the chi-square ($\chi^... | [] | null | 63 | 1308.1009 | title_judge | [
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Transfer Learning in a Transductive Setting | https://proceedings.neurips.cc/paper_files/paper/2013/hash/3295c76acbf4caaed33c36b1b5fc2cb1-Abstract.html | [
"Marcus Rohrbach",
"Sandra Ebert",
"Bernt Schiele"
] | null | null | Category models for objects or activities typically rely on supervised learning requiring sufficiently large training sets. Transferring knowledge from known categories to novel classes with no or only a few labels however is far less researched even though it is a common scenario. In this work, we extend transfer lear... | [] | null | 64 | null | null | [
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0... |
Solving inverse problem of Markov chain with partial observations | https://proceedings.neurips.cc/paper_files/paper/2013/hash/32b30a250abd6331e03a2a1f16466346-Abstract.html | [
"Tetsuro Morimura",
"Takayuki Osogami",
"Tsuyoshi Ide"
] | null | null | The Markov chain is a convenient tool to represent the dynamics of complex systems such as traffic and social systems, where probabilistic transition takes place between internal states. A Markov chain is characterized by initial-state probabilities and a state-transition probability matrix. In the traditional setting,... | [] | null | 65 | null | null | [
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Wavelets on Graphs via Deep Learning | https://proceedings.neurips.cc/paper_files/paper/2013/hash/33e8075e9970de0cfea955afd4644bb2-Abstract.html | [
"Raif Rustamov",
"Leonidas Guibas"
] | null | null | An increasing number of applications require processing of signals defined on weighted graphs. While wavelets provide a flexible tool for signal processing in the classical setting of regular domains, the existing graph wavelet constructions are less flexible -- they are guided solely by the structure of the underlying... | [] | null | 66 | null | null | [
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Stochastic Convex Optimization with Multiple Objectives | https://proceedings.neurips.cc/paper_files/paper/2013/hash/3493894fa4ea036cfc6433c3e2ee63b0-Abstract.html | [
"Mehrdad Mahdavi",
"Tianbao Yang",
"Rong Jin"
] | null | null | In this paper, we are interested in the development of efficient algorithms for convex optimization problems in the simultaneous presence of multiple objectives and stochasticity in the first-order information. We cast the stochastic multiple objective optimization problem into a constrained optimization problem by cho... | [] | null | 67 | null | null | [
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Bayesian Hierarchical Community Discovery | https://proceedings.neurips.cc/paper_files/paper/2013/hash/35cf8659cfcb13224cbd47863a34fc58-Abstract.html | [
"Charles Blundell",
"Yee Whye Teh"
] | null | null | We propose an efficient Bayesian nonparametric model for discovering hierarchical community structure in social networks. Our model is a tree-structured mixture of potentially exponentially many stochastic blockmodels. We describe a family of greedy agglomerative model selection algorithms whose worst case scales quadr... | [] | null | 68 | null | null | [
0.010392907075583935,
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Contrastive Learning Using Spectral Methods | https://proceedings.neurips.cc/paper_files/paper/2013/hash/36a16a2505369e0c922b6ea7a23a56d2-Abstract.html | [
"James Y Zou",
"Daniel J. Hsu",
"David C. Parkes",
"Ryan P. Adams"
] | null | null | In many natural settings, the analysis goal is not to characterize a single data set in isolation, but rather to understand the difference between one set of observations and another. For example, given a background corpus of news articles together with writings of a particular author, one may want a topic model that e... | [] | null | 69 | null | null | [
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Deep Fisher Networks for Large-Scale Image Classification | https://proceedings.neurips.cc/paper_files/paper/2013/hash/37a749d808e46495a8da1e5352d03cae-Abstract.html | [
"Karen Simonyan",
"Andrea Vedaldi",
"Andrew Zisserman"
] | null | null | As massively parallel computations have become broadly available with modern GPUs, deep architectures trained on very large datasets have risen in popularity. Discriminatively trained convolutional neural networks, in particular, were recently shown to yield state-of-the-art performance in challenging image classificat... | [] | null | 70 | null | null | [
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0.0125... |
Linear Convergence with Condition Number Independent Access of Full Gradients | https://proceedings.neurips.cc/paper_files/paper/2013/hash/37f0e884fbad9667e38940169d0a3c95-Abstract.html | [
"Lijun Zhang",
"Mehrdad Mahdavi",
"Rong Jin"
] | null | null | For smooth and strongly convex optimization, the optimal iteration complexity of the gradient-based algorithm is $O(\sqrt{\kappa}\log 1/\epsilon)$, where $\kappa$ is the conditional number. In the case that the optimization problem is ill-conditioned, we need to evaluate a larger number of full gradients, which could b... | [] | null | 71 | null | null | [
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Learning with Noisy Labels | https://proceedings.neurips.cc/paper_files/paper/2013/hash/3871bd64012152bfb53fdf04b401193f-Abstract.html | [
"Nagarajan Natarajan",
"Inderjit S Dhillon",
"Pradeep K Ravikumar",
"Ambuj Tewari"
] | null | null | In this paper, we theoretically study the problem of binary classification in the presence of random classification noise --- the learner, instead of seeing the true labels, sees labels that have independently been flipped with some small probability. Moreover, random label noise is \emph{class-conditional} --- the fli... | [] | null | 72 | null | null | [
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Variational Policy Search via Trajectory Optimization | https://proceedings.neurips.cc/paper_files/paper/2013/hash/38af86134b65d0f10fe33d30dd76442e-Abstract.html | [
"Sergey Levine",
"Vladlen Koltun"
] | null | null | In order to learn effective control policies for dynamical systems, policy search methods must be able to discover successful executions of the desired task. While random exploration can work well in simple domains, complex and high-dimensional tasks present a serious challenge, particularly when combined with high-dim... | [] | null | 73 | null | null | [
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Dropout Training as Adaptive Regularization | https://proceedings.neurips.cc/paper_files/paper/2013/hash/38db3aed920cf82ab059bfccbd02be6a-Abstract.html | [
"Stefan Wager",
"Sida Wang",
"Percy Liang"
] | null | null | Dropout and other feature noising schemes control overfitting by artificially corrupting the training data. For generalized linear models, dropout performs a form of adaptive regularization. Using this viewpoint, we show that the dropout regularizer is first-order equivalent to an $\LII$ regularizer applied after scali... | [] | null | 74 | 1307.1493 | title_snapshot | [
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Prior-free and prior-dependent regret bounds for Thompson Sampling | https://proceedings.neurips.cc/paper_files/paper/2013/hash/39461a19e9eddfb385ea76b26521ea48-Abstract.html | [
"Sebastien Bubeck",
"Che-Yu Liu"
] | null | null | We consider the stochastic multi-armed bandit problem with a prior distribution on the reward distributions. We are interested in studying prior-free and prior-dependent regret bounds, very much in the same spirit than the usual distribution-free and distribution-dependent bounds for the non-Bayesian stochastic bandit.... | [] | null | 75 | 1304.5758 | title_snapshot | [
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Geometric optimisation on positive definite matrices for elliptically contoured distributions | https://proceedings.neurips.cc/paper_files/paper/2013/hash/3948ead63a9f2944218de038d8934305-Abstract.html | [
"Suvrit Sra",
"Reshad Hosseini"
] | null | null | Hermitian positive definite matrices (HPD) recur throughout statistics and machine learning. In this paper we develop \emph{geometric optimisation} for globally optimising certain nonconvex loss functions arising in the modelling of data via elliptically contoured distributions (ECDs). We exploit the remarkable structu... | [] | null | 76 | 1312.1039 | title_judge | [
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Capacity of strong attractor patterns to model behavioural and cognitive prototypes | https://proceedings.neurips.cc/paper_files/paper/2013/hash/39e4973ba3321b80f37d9b55f63ed8b8-Abstract.html | [
"Abbas Edalat"
] | null | null | We solve the mean field equations for a stochastic Hopfield network with temperature (noise) in the presence of strong, i.e., multiply stored patterns, and use this solution to obtain the storage capacity of such a network. Our result provides for the first time a rigorous solution of the mean field equations for the s... | [] | null | 77 | null | null | [
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Manifold-based Similarity Adaptation for Label Propagation | https://proceedings.neurips.cc/paper_files/paper/2013/hash/3a835d3215755c435ef4fe9965a3f2a0-Abstract.html | [
"Masayuki Karasuyama",
"Hiroshi Mamitsuka"
] | null | null | Label propagation is one of the state-of-the-art methods for semi-supervised learning, which estimates labels by propagating label information through a graph. Label propagation assumes that data points (nodes) connected in a graph should have similar labels. Consequently, the label estimation heavily depends on edge w... | [] | null | 78 | null | null | [
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New Subsampling Algorithms for Fast Least Squares Regression | https://proceedings.neurips.cc/paper_files/paper/2013/hash/3cec07e9ba5f5bb252d13f5f431e4bbb-Abstract.html | [
"Paramveer Dhillon",
"Yichao Lu",
"Dean P. Foster",
"Lyle Ungar"
] | null | null | We address the problem of fast estimation of ordinary least squares (OLS) from large amounts of data ($n \gg p$). We propose three methods which solve the big data problem by subsampling the covariance matrix using either a single or two stage estimation. All three run in the order of size of input i.e. O($np$) and our... | [] | null | 79 | null | null | [
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A message-passing algorithm for multi-agent trajectory planning | https://proceedings.neurips.cc/paper_files/paper/2013/hash/3dd48ab31d016ffcbf3314df2b3cb9ce-Abstract.html | [
"José Bento",
"Nate Derbinsky",
"Javier Alonso-Mora",
"Jonathan S. Yedidia"
] | null | null | We describe a novel approach for computing collision-free \emph{global} trajectories for $p$ agents with specified initial and final configurations, based on an improved version of the alternating direction method of multipliers (ADMM) algorithm. Compared with existing methods, our approach is naturally parallelizable ... | [] | null | 80 | 1311.4527 | title_snapshot | [
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Solving the multi-way matching problem by permutation synchronization | https://proceedings.neurips.cc/paper_files/paper/2013/hash/3df1d4b96d8976ff5986393e8767f5b2-Abstract.html | [
"Deepti Pachauri",
"Risi Kondor",
"Vikas Singh"
] | null | null | The problem of matching not just two, but m different sets of objects to each other arises in a variety of contexts, including finding the correspondence between feature points across multiple images in computer vision. At present it is usually solved by matching the sets pairwise, in series. In contrast, we propose a ... | [] | null | 81 | null | null | [
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Auditing: Active Learning with Outcome-Dependent Query Costs | https://proceedings.neurips.cc/paper_files/paper/2013/hash/40008b9a5380fcacce3976bf7c08af5b-Abstract.html | [
"Sivan Sabato",
"Anand D Sarwate",
"Nati Srebro"
] | null | null | We propose a learning setting in which unlabeled data is free, and the cost of a label depends on its value, which is not known in advance. We study binary classification in an extreme case, where the algorithm only pays for negative labels. Our motivation are applications such as fraud detection, in which investigatin... | [] | null | 82 | 1306.2347 | title_snapshot | [
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Restricting exchangeable nonparametric distributions | https://proceedings.neurips.cc/paper_files/paper/2013/hash/4122cb13c7a474c1976c9706ae36521d-Abstract.html | [
"Sinead A Williamson",
"Steve N MacEachern",
"Eric P Xing"
] | null | null | Distributions over exchangeable matrices with infinitely many columns are useful in constructing nonparametric latent variable models. However, the distribution implied by such models over the number of features exhibited by each data point may be poorly-suited for many modeling tasks. In this paper, we propose a class... | [] | null | 83 | 1209.1145 | title_snapshot | [
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On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization | https://proceedings.neurips.cc/paper_files/paper/2013/hash/41ae36ecb9b3eee609d05b90c14222fb-Abstract.html | [
"Ke Hou",
"Zirui Zhou",
"Anthony Man-Cho So",
"Zhi-Quan Luo"
] | null | null | Motivated by various applications in machine learning, the problem of minimizing a convex smooth loss function with trace norm regularization has received much attention lately. Currently, a popular method for solving such problem is the proximal gradient method (PGM), which is known to have a sublinear rate of converg... | [] | null | 84 | null | null | [
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Eluder Dimension and the Sample Complexity of Optimistic Exploration | https://proceedings.neurips.cc/paper_files/paper/2013/hash/41bfd20a38bb1b0bec75acf0845530a7-Abstract.html | [
"Daniel Russo",
"Benjamin Van Roy"
] | null | null | This paper considers the sample complexity of the multi-armed bandit with dependencies among the arms. Some of the most successful algorithms for this problem use the principle of optimism in the face of uncertainty to guide exploration. The clearest example of this is the class of upper confidence bound (UCB) algorith... | [] | null | 85 | null | null | [
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Efficient Algorithm for Privately Releasing Smooth Queries | https://proceedings.neurips.cc/paper_files/paper/2013/hash/428fca9bc1921c25c5121f9da7815cde-Abstract.html | [
"Ziteng Wang",
"Kai Fan",
"Jiaqi Zhang",
"Liwei Wang"
] | null | null | We study differentially private mechanisms for answering \emph{smooth} queries on databases consisting of data points in $\mathbb{R}^d$. A $K$-smooth query is specified by a function whose partial derivatives up to order $K$ are all bounded. We develop an $\epsilon$-differentially private mechanism which for the class ... | [] | null | 86 | null | null | [
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0.0089... |
Buy-in-Bulk Active Learning | https://proceedings.neurips.cc/paper_files/paper/2013/hash/43baa6762fa81bb43b39c62553b2970d-Abstract.html | [
"Liu Yang",
"Jaime Carbonell"
] | null | null | In many practical applications of active learning, it is more cost-effective to request labels in large batches, rather than one-at-a-time. This is because the cost of labeling a large batch of examples at once is often sublinear in the number of examples in the batch. In this work, we study the label complexity of act... | [] | null | 87 | null | null | [
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On Poisson Graphical Models | https://proceedings.neurips.cc/paper_files/paper/2013/hash/43feaeeecd7b2fe2ae2e26d917b6477d-Abstract.html | [
"Eunho Yang",
"Pradeep K Ravikumar",
"Genevera I Allen",
"Zhandong Liu"
] | null | null | Undirected graphical models, such as Gaussian graphical models, Ising, and multinomial/categorical graphical models, are widely used in a variety of applications for modeling distributions over a large number of variables. These standard instances, however, are ill-suited to modeling count data, which are increasingly ... | [] | null | 88 | null | null | [
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On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations | https://proceedings.neurips.cc/paper_files/paper/2013/hash/443cb001c138b2561a0d90720d6ce111-Abstract.html | [
"Tamir Hazan",
"Subhransu Maji",
"Tommi Jaakkola"
] | null | null | In this paper we describe how MAP inference can be used to sample efficiently from Gibbs distributions. Specifically, we provide means for drawing either approximate or unbiased samples from Gibbs' distributions by introducing low dimensional perturbations and solving the corresponding MAP assignments. Our approach als... | [] | null | 89 | 1309.7598 | title_snapshot | [
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Factorized Asymptotic Bayesian Inference for Latent Feature Models | https://proceedings.neurips.cc/paper_files/paper/2013/hash/45645a27c4f1adc8a7a835976064a86d-Abstract.html | [
"Kohei Hayashi",
"Ryohei Fujimaki"
] | null | null | This paper extends factorized asymptotic Bayesian (FAB) inference for latent feature models~(LFMs). FAB inference has not been applicable to models, including LFMs, without a specific condition on the Hesqsian matrix of a complete log-likelihood, which is required to derive a factorized information criterion''~(FIC). O... | [] | null | 90 | null | null | [
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Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation | https://proceedings.neurips.cc/paper_files/paper/2013/hash/456ac9b0d15a8b7f1e71073221059886-Abstract.html | [
"Martin Azizyan",
"Aarti Singh",
"Larry Wasserman"
] | null | null | While several papers have investigated computationally and statistically efficient methods for learning Gaussian mixtures, precise minimax bounds for their statistical performance as well as fundamental limits in high-dimensional settings are not well-understood. In this paper, we provide precise information theoretic ... | [] | null | 91 | 1306.2035 | title_snapshot | [
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Efficient Optimization for Sparse Gaussian Process Regression | https://proceedings.neurips.cc/paper_files/paper/2013/hash/46922a0880a8f11f8f69cbb52b1396be-Abstract.html | [
"Yanshuai Cao",
"Marcus A Brubaker",
"David J Fleet",
"Aaron Hertzmann"
] | null | null | We propose an efficient discrete optimization algorithm for selecting a subset of training data to induce sparsity for Gaussian process regression. The algorithm estimates this inducing set and the hyperparameters using a single objective, either the marginal likelihood or a variational free energy. The space and time ... | [] | null | 92 | 1310.6007 | title_snapshot | [
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Robust learning of low-dimensional dynamics from large neural ensembles | https://proceedings.neurips.cc/paper_files/paper/2013/hash/47a658229eb2368a99f1d032c8848542-Abstract.html | [
"David Pfau",
"Eftychios A Pnevmatikakis",
"Liam Paninski"
] | null | null | Recordings from large populations of neurons make it possible to search for hypothesized low-dimensional dynamics. Finding these dynamics requires models that take into account biophysical constraints and can be fit efficiently and robustly. Here, we present an approach to dimensionality reduction for neural data that ... | [] | null | 93 | null | null | [
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Causal Inference on Time Series using Restricted Structural Equation Models | https://proceedings.neurips.cc/paper_files/paper/2013/hash/47d1e990583c9c67424d369f3414728e-Abstract.html | [
"Jonas Peters",
"Dominik Janzing",
"Bernhard Schölkopf"
] | null | null | Causal inference uses observational data to infer the causal structure of the data generating system. We study a class of restricted Structural Equation Models for time series that we call Time Series Models with Independent Noise (TiMINo). These models require independent residual time series, whereas traditional meth... | [] | null | 94 | 1207.5136 | title_judge | [
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Better Approximation and Faster Algorithm Using the Proximal Average | https://proceedings.neurips.cc/paper_files/paper/2013/hash/49182f81e6a13cf5eaa496d51fea6406-Abstract.html | [
"Yao-Liang Yu"
] | null | null | It is a common practice to approximate complicated'' functions with more friendly ones. In large-scale machine learning applications, nonsmooth losses/regularizers that entail great computational challenges are usually approximated by smooth functions. We re-examine this powerful methodology and point out a nonsmooth a... | [] | null | 95 | null | null | [
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-0.01739664375782013,
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-0.030... |
Robust Low Rank Kernel Embeddings of Multivariate Distributions | https://proceedings.neurips.cc/paper_files/paper/2013/hash/49b8b4f95f02e055801da3b4f58e28b7-Abstract.html | [
"Le Song",
"Bo Dai"
] | null | null | Kernel embedding of distributions has led to many recent advances in machine learning. However, latent and low rank structures prevalent in real world distributions have rarely been taken into account in this setting. Furthermore, no prior work in kernel embedding literature has addressed the issue of robust embedding ... | [] | null | 96 | null | null | [
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0... |
Learning the Local Statistics of Optical Flow | https://proceedings.neurips.cc/paper_files/paper/2013/hash/4a213d37242bdcad8e7300e202e7caa4-Abstract.html | [
"Dan Rosenbaum",
"Daniel Zoran",
"Yair Weiss"
] | null | null | Motivated by recent progress in natural image statistics, we use newly available datasets with ground truth optical flow to learn the local statistics of optical flow and rigorously compare the learned model to prior models assumed by computer vision optical flow algorithms. We find that a Gaussian mixture model with 6... | [] | null | 97 | null | null | [
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Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis | https://proceedings.neurips.cc/paper_files/paper/2013/hash/4d2e7bd33c475784381a64e43e50922f-Abstract.html | [
"James R Voss",
"Luis Rademacher",
"Mikhail Belkin"
] | null | null | The performance of standard algorithms for Independent Component Analysis quickly deteriorates under the addition of Gaussian noise. This is partially due to a common first step that typically consists of whitening, i.e., applying Principal Component Analysis (PCA) and rescaling the components to have identity covarian... | [] | null | 98 | null | null | [
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-0.... |
Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization | https://proceedings.neurips.cc/paper_files/paper/2013/hash/4da04049a062f5adfe81b67dd755cecc-Abstract.html | [
"Julien Mairal"
] | null | null | Majorization-minimization algorithms consist of iteratively minimizing a majorizing surrogate of an objective function. Because of its simplicity and its wide applicability, this principle has been very popular in statistics and in signal processing. In this paper, we intend to make this principle scalable. We introduc... | [] | null | 99 | 1306.4650 | title_snapshot | [
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-0.009984... |
Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions | https://proceedings.neurips.cc/paper_files/paper/2013/hash/4f284803bd0966cc24fa8683a34afc6e-Abstract.html | [
"Yasin Abbasi Yadkori",
"Peter L Bartlett",
"Varun Kanade",
"Yevgeny Seldin",
"Csaba Szepesvari"
] | null | null | We study the problem of online learning Markov Decision Processes (MDPs) when both the transition distributions and loss functions are chosen by an adversary. We present an algorithm that, under a mixing assumption, achieves $O(\sqrt{T\log|\Pi|}+\log|\Pi|)$ regret with respect to a comparison set of policies $\Pi$. The... | [] | null | 100 | 1303.3055 | title_snapshot | [
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-0.023653708398342133,
-0.008691956289112568,
-0.06788081675767899,
... |
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