NeurIPS
Collection
Accepted papers for NeurIPS (Conference on Neural Information Processing Systems), one dataset per year. • 13 items • Updated
paper_id string | 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
002302d5a1c66195b6981e33e38df11d | From Stochastic Mixability to Fast Rates | https://proceedings.neurips.cc/paper_files/paper/2014/hash/002302d5a1c66195b6981e33e38df11d-Abstract.html | [
"Nishant A Mehta",
"Robert C. Williamson"
] | null | null | Empirical risk minimization (ERM) is a fundamental learning rule for statistical learning problems where the data is generated according to some unknown distribution $\mathsf{P}$ and returns a hypothesis $f$ chosen from a fixed class $\mathcal{F}$ with small loss $\ell$. In the parametric setting, depending upon $(\ell... | [] | null | 1 | 1406.3781 | title_snapshot |
014b0027decf8737e4c1242be3054307 | Active Regression by Stratification | https://proceedings.neurips.cc/paper_files/paper/2014/hash/014b0027decf8737e4c1242be3054307-Abstract.html | [
"Sivan Sabato",
"Remi Munos"
] | null | null | We propose a new active learning algorithm for parametric linear regression with random design. We provide finite sample convergence guarantees for general distributions in the misspecified model. This is the first active learner for this setting that provably can improve over passive learning. Unlike other learning se... | [] | null | 2 | 1410.5920 | title_snapshot |
0197ff74daa1c383cf9f4e190020f5c4 | Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition | https://proceedings.neurips.cc/paper_files/paper/2014/hash/0197ff74daa1c383cf9f4e190020f5c4-Abstract.html | [
"Hanie Sedghi",
"Anima Anandkumar",
"Edmond Jonckheere"
] | null | null | In this paper, we consider a multi-step version of the stochastic ADMM method with efficient guarantees for high-dimensional problems. We first analyze the simple setting, where the optimization problem consists of a loss function and a single regularizer (e.g. sparse optimization), and then extend to the multi-block s... | [] | null | 3 | 1402.5131 | title_judge |
02a12643ae21d984b93c9df82a9d2152 | Spatio-temporal Representations of Uncertainty in Spiking Neural Networks | https://proceedings.neurips.cc/paper_files/paper/2014/hash/02a12643ae21d984b93c9df82a9d2152-Abstract.html | [
"Cristina Savin",
"Sophie Deneve"
] | null | null | It has been long argued that, because of inherent ambiguity and noise, the brain needs to represent uncertainty in the form of probability distributions. The neural encoding of such distributions remains however highly controversial. Here we present a novel circuit model for representing multidimensional real-valued di... | [] | null | 4 | null | null |
03bc99773b4d3aa3cac5b59ce24d8afd | Biclustering Using Message Passing | https://proceedings.neurips.cc/paper_files/paper/2014/hash/03bc99773b4d3aa3cac5b59ce24d8afd-Abstract.html | [
"Luke O'Connor",
"Soheil Feizi"
] | null | null | Biclustering is the analog of clustering on a bipartite graph. Existent methods infer biclusters through local search strategies that find one cluster at a time; a common technique is to update the row memberships based on the current column memberships, and vice versa. We propose a biclustering algorithm that maximize... | [] | null | 5 | null | null |
04192426585542c54b96ba14445be996 | Identifying and attacking the saddle point problem in high-dimensional non-convex optimization | https://proceedings.neurips.cc/paper_files/paper/2014/hash/04192426585542c54b96ba14445be996-Abstract.html | [
"Yann N. Dauphin",
"Razvan Pascanu",
"Caglar Gulcehre",
"Kyunghyun Cho",
"Surya Ganguli",
"Yoshua Bengio"
] | null | null | A central challenge to many fields of science and engineering involves minimizing non-convex error functions over continuous, high dimensional spaces. Gradient descent or quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it is often thought that a main source of difficulty for these l... | [] | null | 6 | 1406.2572 | title_snapshot |
047f66ae639d534aad092409f428e130 | Clustered factor analysis of multineuronal spike data | https://proceedings.neurips.cc/paper_files/paper/2014/hash/047f66ae639d534aad092409f428e130-Abstract.html | [
"Lars Buesing",
"Timothy A. Machado",
"John P. Cunningham",
"Liam Paninski"
] | null | null | High-dimensional, simultaneous recordings of neural spiking activity are often explored, analyzed and visualized with the help of latent variable or factor models. Such models are however ill-equipped to extract structure beyond shared, distributed aspects of firing activity across multiple cells. Here, we extend unstr... | [] | null | 7 | null | null |
050a402944ba50e4ffc727ce02cfb403 | Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling | https://proceedings.neurips.cc/paper_files/paper/2014/hash/050a402944ba50e4ffc727ce02cfb403-Abstract.html | [
"Mingyuan Zhou"
] | null | null | The beta-negative binomial process (BNBP), an integer-valued stochastic process, is employed to partition a count vector into a latent random count matrix. As the marginal probability distribution of the BNBP that governs the exchangeable random partitions of grouped data has not yet been developed, current inference f... | [] | null | 8 | 1410.7812 | title_snapshot |
0525ce70d439c1ddeadc8277ca151195 | Gaussian Process Volatility Model | https://proceedings.neurips.cc/paper_files/paper/2014/hash/0525ce70d439c1ddeadc8277ca151195-Abstract.html | [
"Yue Wu",
"José Miguel Hernández Lobato",
"Zoubin Ghahramani"
] | null | null | The prediction of time-changing variances is an important task in the modeling of financial data. Standard econometric models are often limited as they assume rigid functional relationships for the evolution of the variance. Moreover, functional parameters are usually learned by maximum likelihood, which can lead to ov... | [] | null | 9 | 1402.3085 | title_snapshot |
056d7ac16aa3fc9dc241a20cfb56539c | Distributed Estimation, Information Loss and Exponential Families | https://proceedings.neurips.cc/paper_files/paper/2014/hash/056d7ac16aa3fc9dc241a20cfb56539c-Abstract.html | [
"Qiang Liu",
"Alexander Ihler"
] | null | null | Distributed learning of probabilistic models from multiple data repositories with minimum communication is increasingly important. We study a simple communication-efficient learning framework that first calculates the local maximum likelihood estimates (MLE) based on the data subsets, and then combines the local MLEs t... | [] | null | 10 | 1410.2653 | title_snapshot |
05a3e71d36f5c05318c0f70a6b7c485f | Cone-Constrained Principal Component Analysis | https://proceedings.neurips.cc/paper_files/paper/2014/hash/05a3e71d36f5c05318c0f70a6b7c485f-Abstract.html | [
"Yash Deshpande",
"Andrea Montanari",
"Emile Richard"
] | null | null | Estimating a vector from noisy quadratic observations is a task that arises naturally in many contexts, from dimensionality reduction, to synchronization and phase retrieval problems. It is often the case that additional information is available about the unknown vector (for instance, sparsity, sign or magnitude of its... | [] | null | 11 | null | null |
0673011fbdc464f51b05897b7db2d151 | Dynamic Rank Factor Model for Text Streams | https://proceedings.neurips.cc/paper_files/paper/2014/hash/0673011fbdc464f51b05897b7db2d151-Abstract.html | [
"Shaobo Han",
"Lin Du",
"Esther Salazar",
"Lawrence Carin"
] | null | null | We propose a semi-parametric and dynamic rank factor model for topic modeling, capable of (1) discovering topic prevalence over time, and (2) learning contemporary multi-scale dependence structures, providing topic and word correlations as a byproduct. The high-dimensional and time-evolving ordinal/rank observations (s... | [] | null | 12 | null | null |
06da2cfb2088f776d522b5cdafe677ab | Online combinatorial optimization with stochastic decision sets and adversarial losses | https://proceedings.neurips.cc/paper_files/paper/2014/hash/06da2cfb2088f776d522b5cdafe677ab-Abstract.html | [
"Gergely Neu",
"Michal Valko"
] | null | null | Most work on sequential learning assumes a fixed set of actions that are available all the time. However, in practice, actions can consist of picking subsets of readings from sensors that may break from time to time, road segments that can be blocked or goods that are out of stock. In this paper we study learning algor... | [] | null | 13 | 2604.25269 | title_snapshot |
06ead039a193550d1d1d8c4b7f8124ee | Magnitude-sensitive preference formation` | https://proceedings.neurips.cc/paper_files/paper/2014/hash/06ead039a193550d1d1d8c4b7f8124ee-Abstract.html | [
"Nisheeth Srivastava",
"Ed Vul",
"Paul R. Schrater"
] | null | null | Our understanding of the neural computations that underlie the ability of animals to choose among options has advanced through a synthesis of computational modeling, brain imaging and behavioral choice experiments. Yet, there remains a gulf between theories of preference learning and accounts of the real, economic choi... | [] | null | 14 | null | null |
06f714eca850a0799089c8e9f076ed7b | Learning convolution filters for inverse covariance estimation of neural network connectivity | https://proceedings.neurips.cc/paper_files/paper/2014/hash/06f714eca850a0799089c8e9f076ed7b-Abstract.html | [
"George Mohler"
] | null | null | We consider the problem of inferring direct neural network connections from Calcium imaging time series. Inverse covariance estimation has proven to be a fast and accurate method for learning macro- and micro-scale network connectivity in the brain and in a recent Kaggle Connectomics competition inverse covariance was ... | [] | null | 15 | null | null |
07a45842fcab1f6116c50549a437c254 | Sparse PCA via Covariance Thresholding | https://proceedings.neurips.cc/paper_files/paper/2014/hash/07a45842fcab1f6116c50549a437c254-Abstract.html | [
"Yash Deshpande",
"Andrea Montanari"
] | null | null | In sparse principal component analysis we are given noisy observations of a low-rank matrix of dimension $n\times p$ and seek to reconstruct it under additional sparsity assumptions. In particular, we assume here that the principal components $\bv_1,\dots,\bv_r$ have at most $k_1, \cdots, k_q$ non-zero entries respecti... | [] | null | 16 | 1311.5179 | title_snapshot |
08211bbb6d687bff251342162c6a5f84 | Online Optimization for Max-Norm Regularization | https://proceedings.neurips.cc/paper_files/paper/2014/hash/08211bbb6d687bff251342162c6a5f84-Abstract.html | [
"Jie Shen",
"Huan Xu",
"Ping Li"
] | null | null | Max-norm regularizer has been extensively studied in the last decade as it promotes an effective low rank estimation of the underlying data. However, max-norm regularized problems are typically formulated and solved in a batch manner, which prevents it from processing big data due to possible memory bottleneck. In this... | [] | null | 17 | 1406.3190 | title_judge |
0942e5741531db4483d0cc9d6b83ace2 | Optimizing Energy Production Using Policy Search and Predictive State Representations | https://proceedings.neurips.cc/paper_files/paper/2014/hash/0942e5741531db4483d0cc9d6b83ace2-Abstract.html | [
"Yuri Grinberg",
"Doina Precup",
"Michel Gendreau"
] | null | null | We consider the challenging practical problem of optimizing the power production of a complex of hydroelectric power plants, which involves control over three continuous action variables, uncertainty in the amount of water inflows and a variety of constraints that need to be satisfied. We propose a policy-search-based ... | [] | null | 18 | null | null |
096e2c25cfb42668e439dfc0162b2520 | Dependent nonparametric trees for dynamic hierarchical clustering | https://proceedings.neurips.cc/paper_files/paper/2014/hash/096e2c25cfb42668e439dfc0162b2520-Abstract.html | [
"Kumar Avinava Dubey",
"Qirong Ho",
"Sinead A Williamson",
"Eric P Xing"
] | null | null | Hierarchical clustering methods offer an intuitive and powerful way to model a wide variety of data sets. However, the assumption of a fixed hierarchy is often overly restrictive when working with data generated over a period of time: We expect both the structure of our hierarchy, and the parameters of the clusters, to... | [] | null | 19 | null | null |
099268c3121d49937a67a052c51f865d | Kernel Mean Estimation via Spectral Filtering | https://proceedings.neurips.cc/paper_files/paper/2014/hash/099268c3121d49937a67a052c51f865d-Abstract.html | [
"Krikamol Muandet",
"Bharath Sriperumbudur",
"Bernhard Schölkopf"
] | null | null | The problem of estimating the kernel mean in a reproducing kernel Hilbert space (RKHS) is central to kernel methods in that it is used by classical approaches (e.g., when centering a kernel PCA matrix), and it also forms the core inference step of modern kernel methods (e.g., kernel-based non-parametric tests) that rel... | [] | null | 20 | 1411.0900 | title_snapshot |
09939c83d244f420d893535340da3ae4 | Beyond Disagreement-Based Agnostic Active Learning | https://proceedings.neurips.cc/paper_files/paper/2014/hash/09939c83d244f420d893535340da3ae4-Abstract.html | [
"Chicheng Zhang",
"Kamalika Chaudhuri"
] | null | null | We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels. The main algorithms for this problem are {\em{disagreement-based active learning}}, ... | [] | null | 21 | 1407.2657 | title_snapshot |
0a158fff343cd8aa7f09f90d014cf7dd | Distance-Based Network Recovery under Feature Correlation | https://proceedings.neurips.cc/paper_files/paper/2014/hash/0a158fff343cd8aa7f09f90d014cf7dd-Abstract.html | [
"David Adametz",
"Volker Roth"
] | null | null | We present an inference method for Gaussian graphical models when only pairwise distances of n objects are observed. Formally, this is a problem of estimating an n x n covariance matrix from the Mahalanobis distances dMH(xi, xj), where object xi lives in a latent feature space. We solve the problem in fully Bayesian fa... | [] | null | 22 | null | null |
0a33562d6e9b20a57626befba498ded3 | Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers | https://proceedings.neurips.cc/paper_files/paper/2014/hash/0a33562d6e9b20a57626befba498ded3-Abstract.html | [
"Bruno Conejo",
"Nikos Komodakis",
"Sebastien Leprince",
"Jean Philippe Avouac"
] | null | null | We propose a general and versatile framework that significantly speeds-up graphical model optimization while maintaining an excellent solution accuracy. The proposed approach, refereed as Inference by Learning or IbyL, relies on a multi-scale pruning scheme that progressively reduces the solution space by use of a coar... | [] | null | 23 | 1409.4205 | title_judge |
0a8cd36e8193ba3773f8bcb9ed416ebb | A Complete Variational Tracker | https://proceedings.neurips.cc/paper_files/paper/2014/hash/0a8cd36e8193ba3773f8bcb9ed416ebb-Abstract.html | [
"Ryan D Turner",
"Steven Bottone",
"Bhargav Avasarala"
] | null | null | We introduce a novel probabilistic tracking algorithm that incorporates combinatorial data association constraints and model-based track management using variational Bayes. We use a Bethe entropy approximation to incorporate data association constraints that are often ignored in previous probabilistic tracking algorith... | [] | null | 24 | null | null |
0d3ec37c63fcda06f737f0a3eb8d54ae | Optimal prior-dependent neural population codes under shared input noise | https://proceedings.neurips.cc/paper_files/paper/2014/hash/0d3ec37c63fcda06f737f0a3eb8d54ae-Abstract.html | [
"Agnieszka Grabska-Barwinska",
"Jonathan W Pillow"
] | null | null | The brain uses population codes to form distributed, noise-tolerant representations of sensory and motor variables. Recent work has examined the theoretical optimality of such codes in order to gain insight into the principles governing population codes found in the brain. However, the majority of the population coding... | [] | null | 25 | null | null |
0e0a0236834aed19e133e651331210db | Conditional Swap Regret and Conditional Correlated Equilibrium | https://proceedings.neurips.cc/paper_files/paper/2014/hash/0e0a0236834aed19e133e651331210db-Abstract.html | [
"Mehryar Mohri",
"Scott Yang"
] | null | null | We introduce a natural extension of the notion of swap regret, conditional swap regret, that allows for action modifications conditioned on the player’s action history. We prove a series of new results for conditional swap regret minimization. We present algorithms for minimizing conditional swap regret with bounded co... | [] | null | 26 | null | null |
0e7f2179300fe21031b938a265a39409 | Extracting Latent Structure From Multiple Interacting Neural Populations | https://proceedings.neurips.cc/paper_files/paper/2014/hash/0e7f2179300fe21031b938a265a39409-Abstract.html | [
"Joao Semedo",
"Amin Zandvakili",
"Adam Kohn",
"Christian K. Machens",
"Byron M. Yu"
] | null | null | Developments in neural recording technology are rapidly enabling the recording of populations of neurons in multiple brain areas simultaneously, as well as the identification of the types of neurons being recorded (e.g., excitatory vs. inhibitory). There is a growing need for statistical methods to study the interactio... | [] | null | 27 | null | null |
0f0b653ef2261da4d9655441deb6cc55 | Near-optimal Reinforcement Learning in Factored MDPs | https://proceedings.neurips.cc/paper_files/paper/2014/hash/0f0b653ef2261da4d9655441deb6cc55-Abstract.html | [
"Ian Osband",
"Benjamin Van Roy"
] | null | null | Any reinforcement learning algorithm that applies to all Markov decision processes (MDPs) will suffer $\Omega(\sqrt{SAT})$ regret on some MDP, where $T$ is the elapsed time and $S$ and $A$ are the cardinalities of the state and action spaces. This implies $T = \Omega(SA)$ time to guarantee a near-optimal policy. In man... | [] | null | 28 | 1403.3741 | title_snapshot |
0f41d814a243c98c672bdbfabaa40f5e | Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning | https://proceedings.neurips.cc/paper_files/paper/2014/hash/0f41d814a243c98c672bdbfabaa40f5e-Abstract.html | [
"Brendan McMahan",
"Matthew Streeter"
] | null | null | We analyze new online gradient descent algorithms for distributed systems with large delays between gradient computations and the corresponding updates. Using insights from adaptive gradient methods, we develop algorithms that adapt not only to the sequence of gradients, but also to the precise update delays that occur... | [] | null | 29 | null | null |
0fa42ea281a5043992988e446f91417f | Difference of Convex Functions Programming for Reinforcement Learning | https://proceedings.neurips.cc/paper_files/paper/2014/hash/0fa42ea281a5043992988e446f91417f-Abstract.html | [
"Bilal Piot",
"Matthieu Geist",
"Olivier Pietquin"
] | null | null | Large Markov Decision Processes (MDPs) are usually solved using Approximate Dynamic Programming (ADP) methods such as Approximate Value Iteration (AVI) or Approximate Policy Iteration (API). The main contribution of this paper is to show that, alternatively, the optimal state-action value function can be estimated usin... | [] | null | 30 | null | null |
0fdd9219a3552881cfe283e8bd759744 | SerialRank: Spectral Ranking using Seriation | https://proceedings.neurips.cc/paper_files/paper/2014/hash/0fdd9219a3552881cfe283e8bd759744-Abstract.html | [
"Fajwel Fogel",
"Alexandre d'Aspremont",
"Milan Vojnovic"
] | null | null | We describe a seriation algorithm for ranking a set of n items given pairwise comparisons between these items. Intuitively, the algorithm assigns similar rankings to items that compare similarly with all others. It does so by constructing a similarity matrix from pairwise comparisons, using seriation methods to reorder... | [] | null | 31 | null | null |
10a0a61756f0b41fad8270c03da9375d | RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning | https://proceedings.neurips.cc/paper_files/paper/2014/hash/10a0a61756f0b41fad8270c03da9375d-Abstract.html | [
"Marek Petrik",
"Dharmashankar Subramanian"
] | null | null | We describe how to use robust Markov decision processes for value function approximation with state aggregation. The robustness serves to reduce the sensitivity to the approximation error of sub-optimal policies in comparison to classical methods such as fitted value iteration. This results in reducing the bounds on th... | [] | null | 32 | null | null |
11459f04a46a9e348cdeee6986fcf5f2 | Covariance shrinkage for autocorrelated data | https://proceedings.neurips.cc/paper_files/paper/2014/hash/11459f04a46a9e348cdeee6986fcf5f2-Abstract.html | [
"Daniel Bartz",
"Klaus-Robert Müller"
] | null | null | The accurate estimation of covariance matrices is essential for many signal processing and machine learning algorithms. In high dimensional settings the sample covariance is known to perform poorly, hence regularization strategies such as analytic shrinkage of Ledoit/Wolf are applied. In the standard setting, i.i.d. da... | [] | null | 33 | null | null |
115f841d5edaaef4d084469ea159e3f4 | Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization | https://proceedings.neurips.cc/paper_files/paper/2014/hash/115f841d5edaaef4d084469ea159e3f4-Abstract.html | [
"Meisam Razaviyayn",
"Mingyi Hong",
"Zhi-Quan Luo",
"Jong-Shi Pang"
] | null | null | Consider the problem of minimizing the sum of a smooth (possibly non-convex) and a convex (possibly nonsmooth) function involving a large number of variables. A popular approach to solve this problem is the block coordinate descent (BCD) method whereby at each iteration only one variable block is updated while the rema... | [] | null | 34 | 1406.3665 | title_snapshot |
11e9c51241de4f0cae8dc1b7ef3dfe3a | Exact Post Model Selection Inference for Marginal Screening | https://proceedings.neurips.cc/paper_files/paper/2014/hash/11e9c51241de4f0cae8dc1b7ef3dfe3a-Abstract.html | [
"Jason D. Lee",
"Jonathan E. Taylor"
] | null | null | We develop a framework for post model selection inference, via marginal screening, in linear regression. At the core of this framework is a result that characterizes the exact distribution of linear functions of the response $y$, conditional on the model being selected (``condition on selection framework). This allows ... | [] | null | 35 | 1402.5596 | title_snapshot |
11f57302e794a5097ee729d99e6c69fb | Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs | https://proceedings.neurips.cc/paper_files/paper/2014/hash/11f57302e794a5097ee729d99e6c69fb-Abstract.html | [
"David I Inouye",
"Pradeep K Ravikumar",
"Inderjit S Dhillon"
] | null | null | We develop a fast algorithm for the Admixture of Poisson MRFs (APM) topic model and propose a novel metric to directly evaluate this model. The APM topic model recently introduced by Inouye et al. (2014) is the first topic model that allows for word dependencies within each topic unlike in previous topic models like LD... | [] | null | 36 | null | null |
12d763696f54acee4f1b4a3e86b89cfc | Sequential Monte Carlo for Graphical Models | https://proceedings.neurips.cc/paper_files/paper/2014/hash/12d763696f54acee4f1b4a3e86b89cfc-Abstract.html | [
"Christian Andersson Naesseth",
"Fredrik Lindsten",
"Thomas B Schön"
] | null | null | We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a monotonically increasing sequence of probability spaces. By targeting these auxili... | [] | null | 37 | 1402.0330 | title_snapshot |
130799de861d011345ca384d5116652d | Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks | https://proceedings.neurips.cc/paper_files/paper/2014/hash/130799de861d011345ca384d5116652d-Abstract.html | [
"Mario Marchand",
"Hongyu Su",
"Emilie Morvant",
"Juho Rousu",
"John S Shawe-Taylor"
] | null | null | We show that the usual score function for conditional Markov networks can be written as the expectation over the scores of their spanning trees. We also show that a small random sample of these output trees can attain a significant fraction of the margin obtained by the complete graph and we provide conditions under wh... | [] | null | 38 | null | null |
14e9ba1581e99c7b546f18c9ba313a97 | Dimensionality Reduction with Subspace Structure Preservation | https://proceedings.neurips.cc/paper_files/paper/2014/hash/14e9ba1581e99c7b546f18c9ba313a97-Abstract.html | [
"Devansh Arpit",
"Ifeoma Nwogu",
"Venu Govindaraju"
] | null | null | Modeling data as being sampled from a union of independent subspaces has been widely applied to a number of real world applications. However, dimensionality reduction approaches that theoretically preserve this independence assumption have not been well studied. Our key contribution is to show that $2K$ projection vect... | [] | null | 39 | 1412.2404 | title_snapshot |
15c8caab99e6e6bed7418464beaf41a5 | Compressive Sensing of Signals from a GMM with Sparse Precision Matrices | https://proceedings.neurips.cc/paper_files/paper/2014/hash/15c8caab99e6e6bed7418464beaf41a5-Abstract.html | [
"Jianbo Yang",
"Xuejun Liao",
"Minhua Chen",
"Lawrence Carin"
] | null | null | This paper is concerned with compressive sensing of signals drawn from a Gaussian mixture model (GMM) with sparse precision matrices. Previous work has shown: (i) a signal drawn from a given GMM can be perfectly reconstructed from r noise-free measurements if the (dominant) rank of each covariance matrix is less than r... | [] | null | 40 | null | null |
16577b42c2a7b2820435b84f2f5389ff | Extreme bandits | https://proceedings.neurips.cc/paper_files/paper/2014/hash/16577b42c2a7b2820435b84f2f5389ff-Abstract.html | [
"Alexandra Carpentier",
"Michal Valko"
] | null | null | In many areas of medicine, security, and life sciences, we want to allocate limited resources to different sources in order to detect extreme values. In this paper, we study an efficient way to allocate these resources sequentially under limited feedback. While sequential design of experiments is well studied in bandit... | [] | null | 41 | 2604.24545 | title_snapshot |
1730b5e375aa93bc0ad1f923182a6642 | Low Rank Approximation Lower Bounds in Row-Update Streams | https://proceedings.neurips.cc/paper_files/paper/2014/hash/1730b5e375aa93bc0ad1f923182a6642-Abstract.html | [
"David P. Woodruff"
] | null | null | We study low-rank approximation in the streaming model in which the rows of an $n \times d$ matrix $A$ are presented one at a time in an arbitrary order. At the end of the stream, the streaming algorithm should output a $k \times d$ matrix $R$ so that $\|A-AR^{\dagger}R\|_F^2 \leq (1+\eps)\|A-A_k\|_F^2$, where $A_k$ is... | [] | null | 42 | null | null |
178eb467f26013c4a2db409f2255f893 | Efficient Minimax Strategies for Square Loss Games | https://proceedings.neurips.cc/paper_files/paper/2014/hash/178eb467f26013c4a2db409f2255f893-Abstract.html | [
"Wouter M. Koolen",
"Alan Malek",
"Peter L Bartlett"
] | null | null | We consider online prediction problems where the loss between the prediction and the outcome is measured by the squared Euclidean distance and its generalization, the squared Mahalanobis distance. We derive the minimax solutions for the case where the prediction and action spaces are the simplex (this setup is sometime... | [] | null | 43 | null | null |
17ac4eb332d6ac6956ea2e835464e03b | Probabilistic low-rank matrix completion on finite alphabets | https://proceedings.neurips.cc/paper_files/paper/2014/hash/17ac4eb332d6ac6956ea2e835464e03b-Abstract.html | [
"Jean Lafond",
"Olga Klopp",
"Éric Moulines",
"Joseph Salmon"
] | null | null | The task of reconstructing a matrix given a sample of observed entries is known as the \emph{matrix completion problem}. Such a consideration arises in a wide variety of problems, including recommender systems, collaborative filtering, dimensionality reduction, image processing, quantum physics or multi-class classific... | [] | null | 44 | 1412.2632 | title_snapshot |
1835d9d1508eb178b500220a9ddf75a7 | Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data | https://proceedings.neurips.cc/paper_files/paper/2014/hash/1835d9d1508eb178b500220a9ddf75a7-Abstract.html | [
"Karthika Mohan",
"Judea Pearl"
] | null | null | We address the problem of deciding whether a causal or probabilistic query is estimable from data corrupted by missing entries, given a model of missingness process. We extend the results of Mohan et al, 2013 by presenting more general conditions for recovering probabilistic queries of the form P(y|x) and P(y,x) as wel... | [] | null | 45 | null | null |
186b3d044a8c9898679d98dbd0d9b860 | On Model Parallelization and Scheduling Strategies for Distributed Machine Learning | https://proceedings.neurips.cc/paper_files/paper/2014/hash/186b3d044a8c9898679d98dbd0d9b860-Abstract.html | [
"Seunghak Lee",
"Jin Kyu Kim",
"Xun Zheng",
"Qirong Ho",
"Garth A. Gibson",
"Eric P. Xing"
] | null | null | Distributed machine learning has typically been approached from a data parallel perspective, where big data are partitioned to multiple workers and an algorithm is executed concurrently over different data subsets under various synchronization schemes to ensure speed-up and/or correctness. A sibling problem that has re... | [] | null | 46 | null | null |
195c9c0797f42473f2c2f922c4cf52cf | Mondrian Forests: Efficient Online Random Forests | https://proceedings.neurips.cc/paper_files/paper/2014/hash/195c9c0797f42473f2c2f922c4cf52cf-Abstract.html | [
"Balaji Lakshminarayanan",
"Daniel M. Roy",
"Yee Whye Teh"
] | null | null | Ensembles of randomized decision trees, usually referred to as random forests, are widely used for classification and regression tasks in machine learning and statistics. Random forests achieve competitive predictive performance and are computationally efficient to train and test, making them excellent candidates for r... | [] | null | 47 | 1406.2673 | title_snapshot |
19ea3982b415d7bb3363917eb3d60c4a | Learning Deep Features for Scene Recognition using Places Database | https://proceedings.neurips.cc/paper_files/paper/2014/hash/19ea3982b415d7bb3363917eb3d60c4a-Abstract.html | [
"Bolei Zhou",
"Agata Lapedriza",
"Jianxiong Xiao",
"Antonio Torralba",
"Aude Oliva"
] | null | null | Scene recognition is one of the hallmark tasks of computer vision, allowing definition of a context for object recognition. Whereas the tremendous recent progress in object recognition tasks is due to the availability of large datasets like ImageNet and the rise of Convolutional Neural Networks (CNNs) for learning high... | [] | null | 48 | null | null |
1a744d7059a715367fd9e10da6981385 | A Framework for Testing Identifiability of Bayesian Models of Perception | https://proceedings.neurips.cc/paper_files/paper/2014/hash/1a744d7059a715367fd9e10da6981385-Abstract.html | [
"Luigi Acerbi",
"Wei Ji Ma",
"Sethu Vijayakumar"
] | null | null | Bayesian observer models are very effective in describing human performance in perceptual tasks, so much so that they are trusted to faithfully recover hidden mental representations of priors, likelihoods, or loss functions from the data. However, the intrinsic degeneracy of the Bayesian framework, as multiple combinat... | [] | null | 49 | null | null |
1adaeb993eba95859121a43ea61bd858 | Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation | https://proceedings.neurips.cc/paper_files/paper/2014/hash/1adaeb993eba95859121a43ea61bd858-Abstract.html | [
"Emily Denton",
"Wojciech Zaremba",
"Joan Bruna",
"Yann LeCun",
"Rob Fergus"
] | null | null | We present techniques for speeding up the test-time evaluation of large convolutional networks, designed for object recognition tasks. These models deliver impressive accuracy, but each image evaluation requires millions of floating point operations, making their deployment on smartphones and Internet-scale clusters pr... | [] | null | 50 | 1404.0736 | title_snapshot |
1b4d1297f046956c58ea594238948e16 | Exponential Concentration of a Density Functional Estimator | https://proceedings.neurips.cc/paper_files/paper/2014/hash/1b4d1297f046956c58ea594238948e16-Abstract.html | [
"Shashank Singh",
"Barnabas Poczos"
] | null | null | We analyse a plug-in estimator for a large class of integral functionals of one or more continuous probability densities. This class includes important families of entropy, divergence, mutual information, and their conditional versions. For densities on the d-dimensional unit cube [0,1]^d that lie in a beta-Holder smoo... | [] | null | 51 | 1603.08584 | title_snapshot |
1c64ee92596e8ea5050fc435a1d57459 | Weighted importance sampling for off-policy learning with linear function approximation | https://proceedings.neurips.cc/paper_files/paper/2014/hash/1c64ee92596e8ea5050fc435a1d57459-Abstract.html | [
"A. Rupam Mahmood",
"Hado P van Hasselt",
"Richard S. Sutton"
] | null | null | Importance sampling is an essential component of off-policy model-free reinforcement learning algorithms. However, its most effective variant, \emph{weighted} importance sampling, does not carry over easily to function approximation and, because of this, it is not utilized in existing off-policy learning algorithms. In... | [] | null | 52 | null | null |
1c8490c54331f54ba59e2f0036498668 | Efficient Structured Matrix Rank Minimization | https://proceedings.neurips.cc/paper_files/paper/2014/hash/1c8490c54331f54ba59e2f0036498668-Abstract.html | [
"Adams Wei Yu",
"Wanli Ma",
"Yaoliang Yu",
"Jaime G. Carbonell",
"Suvrit Sra"
] | null | null | We study the problem of finding structured low-rank matrices using nuclear norm regularization where the structure is encoded by a linear map. In contrast to most known approaches for linearly structured rank minimization, we do not (a) use the full SVD; nor (b) resort to augmented Lagrangian techniques; nor (c) solve ... | [] | null | 53 | 1509.02447 | title_snapshot |
1dc9398707356a25bbcf61f7b3aa682e | Provable Submodular Minimization using Wolfe's Algorithm | https://proceedings.neurips.cc/paper_files/paper/2014/hash/1dc9398707356a25bbcf61f7b3aa682e-Abstract.html | [
"Deeparnab Chakrabarty",
"Prateek Jain",
"Pravesh Kothari"
] | null | null | Owing to several applications in large scale learning and vision problems, fast submodular function minimization (SFM) has become a critical problem. Theoretically, unconstrained SFM can be performed in polynomial time (Iwata and Orlin 2009), however these algorithms are not practical. In 1976, Wolfe proposed an algori... | [] | null | 54 | 1411.0095 | title_snapshot |
1f1f37ef046902cfd7abecc00f2fc9af | Top Rank Optimization in Linear Time | https://proceedings.neurips.cc/paper_files/paper/2014/hash/1f1f37ef046902cfd7abecc00f2fc9af-Abstract.html | [
"Nan Li",
"Rong Jin",
"Zhi-Hua Zhou"
] | null | null | Bipartite ranking aims to learn a real-valued ranking function that orders positive instances before negative instances. Recent efforts of bipartite ranking are focused on optimizing ranking accuracy at the top of the ranked list. Most existing approaches are either to optimize task specific metrics or to extend the ra... | [] | null | 55 | 1410.1462 | title_snapshot |
1f4fead9959b046b360e97432a1fab09 | On Multiplicative Multitask Feature Learning | https://proceedings.neurips.cc/paper_files/paper/2014/hash/1f4fead9959b046b360e97432a1fab09-Abstract.html | [
"Xin Wang",
"Jinbo Bi",
"Shipeng Yu",
"Jiangwen Sun"
] | null | null | We investigate a general framework of multiplicative multitask feature learning which decomposes each task's model parameters into a multiplication of two components. One of the components is used across all tasks and the other component is task-specific. Several previous methods have been proposed as special cases of ... | [] | null | 56 | 1610.07563 | title_snapshot |
21085aa904b9fe66bf35f67c34d176d0 | Flexible Transfer Learning under Support and Model Shift | https://proceedings.neurips.cc/paper_files/paper/2014/hash/21085aa904b9fe66bf35f67c34d176d0-Abstract.html | [
"Xuezhi Wang",
"Jeff Schneider"
] | null | null | Transfer learning algorithms are used when one has sufficient training data for one supervised learning task (the source/training domain) but only very limited training data for a second task (the target/test domain) that is similar but not identical to the first. Previous work on transfer learning has focused on relat... | [] | null | 57 | null | null |
215a61e48cfa5a74fe875610b42e9991 | Controlling privacy in recommender systems | https://proceedings.neurips.cc/paper_files/paper/2014/hash/215a61e48cfa5a74fe875610b42e9991-Abstract.html | [
"Yu Xin",
"Tommi Jaakkola"
] | null | null | Recommender systems involve an inherent trade-off between accuracy of recommendations and the extent to which users are willing to release information about their preferences. In this paper, we explore a two-tiered notion of privacy where there is a small set of public'' users who are willing to share their preferences... | [] | null | 58 | null | null |
2161abe764d3d61f4d3da5fdbed84297 | Deep Networks with Internal Selective Attention through Feedback Connections | https://proceedings.neurips.cc/paper_files/paper/2014/hash/2161abe764d3d61f4d3da5fdbed84297-Abstract.html | [
"Marijn F Stollenga",
"Jonathan Masci",
"Faustino Gomez",
"Jürgen Schmidhuber"
] | null | null | Traditional convolutional neural networks (CNN) are stationary and feedforward. They neither change their parameters during evaluation nor use feedback from higher to lower layers. Real brains, however, do. So does our Deep Attention Selective Network (dasNet) architecture. DasNet's feedback structure can dynamically a... | [] | null | 59 | 1407.3068 | title_snapshot |
219e596e4af808699ce63a9f709e661c | Spectral Methods for Indian Buffet Process Inference | https://proceedings.neurips.cc/paper_files/paper/2014/hash/219e596e4af808699ce63a9f709e661c-Abstract.html | [
"Hsiao-Yu Tung",
"Alexander J Smola"
] | null | null | The Indian Buffet Process is a versatile statistical tool for modeling distributions over binary matrices. We provide an efficient spectral algorithm as an alternative to costly Variational Bayes and sampling-based algorithms. We derive a novel tensorial characterization of the moments of the Indian Buffet Process prop... | [] | null | 60 | null | null |
21c1339deedba0772fc80581df2eb989 | On Sparse Gaussian Chain Graph Models | https://proceedings.neurips.cc/paper_files/paper/2014/hash/21c1339deedba0772fc80581df2eb989-Abstract.html | [
"Calvin McCarter",
"Seyoung Kim"
] | null | null | In this paper, we address the problem of learning the structure of Gaussian chain graph models in a high-dimensional space. Chain graph models are generalizations of undirected and directed graphical models that contain a mixed set of directed and undirected edges. While the problem of sparse structure learning has bee... | [] | null | 61 | null | null |
234037af73bfcdefaf7b65426bd5a295 | Feature Cross-Substitution in Adversarial Classification | https://proceedings.neurips.cc/paper_files/paper/2014/hash/234037af73bfcdefaf7b65426bd5a295-Abstract.html | [
"Bo Li",
"Yevgeniy Vorobeychik"
] | null | null | The success of machine learning, particularly in supervised settings, has led to numerous attempts to apply it in adversarial settings such as spam and malware detection. The core challenge in this class of applications is that adversaries are not static data generators, but make a deliberate effort to evade the classi... | [] | null | 62 | null | null |
24402144990624b417229a96ad7fa7bc | A Drifting-Games Analysis for Online Learning and Applications to Boosting | https://proceedings.neurips.cc/paper_files/paper/2014/hash/24402144990624b417229a96ad7fa7bc-Abstract.html | [
"Haipeng Luo",
"Robert E. Schapire"
] | null | null | We provide a general mechanism to design online learning algorithms based on a minimax analysis within a drifting-games framework. Different online learning settings (Hedge, multi-armed bandit problems and online convex optimization) are studied by converting into various kinds of drifting games. The original minimax a... | [] | null | 63 | 1406.1856 | title_snapshot |
249d963cf2a1f9539622f86ae66924da | Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models | https://proceedings.neurips.cc/paper_files/paper/2014/hash/249d963cf2a1f9539622f86ae66924da-Abstract.html | [
"Michalis K. Titsias",
"Christopher Yau"
] | null | null | We introduce a novel sampling algorithm for Markov chain Monte Carlo-based Bayesian inference for factorial hidden Markov models. This algorithm is based on an auxiliary variable construction that restricts the model space allowing iterative exploration in polynomial time. The sampling approach overcomes limitations wi... | [] | null | 64 | null | null |
24b9769502b00c79bfd0d5ef3a616ca6 | Causal Inference through a Witness Protection Program | https://proceedings.neurips.cc/paper_files/paper/2014/hash/24b9769502b00c79bfd0d5ef3a616ca6-Abstract.html | [
"Ricardo Silva",
"Robin Evans"
] | null | null | One of the most fundamental problems in causal inference is the estimation of a causal effect when variables are confounded. This is difficult in an observational study because one has no direct evidence that all confounders have been adjusted for. We introduce a novel approach for estimating causal effects that exploi... | [] | null | 65 | 1406.0531 | title_snapshot |
252839721e444cb4a8e15ceaa9a8776f | Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision | https://proceedings.neurips.cc/paper_files/paper/2014/hash/252839721e444cb4a8e15ceaa9a8776f-Abstract.html | [
"Deepti Pachauri",
"Risi Kondor",
"Gautam Sargur",
"Vikas Singh"
] | null | null | Consistently matching keypoints across images, and the related problem of finding clusters of nearby images, are critical components of various tasks in Computer Vision, including Structure from Motion (SfM). Unfortunately, occlusion and large repetitive structures tend to mislead most currently used matching algorithm... | [] | null | 66 | null | null |
253f0c4f7b19222b9059d1ae115e05b8 | Feedforward Learning of Mixture Models | https://proceedings.neurips.cc/paper_files/paper/2014/hash/253f0c4f7b19222b9059d1ae115e05b8-Abstract.html | [
"Matthew Lawlor",
"Steven W Zucker"
] | null | null | We develop a biologically-plausible learning rule that provably converges to the class means of general mixture models. This rule generalizes the classical BCM neural rule within a tensor framework, substantially increasing the generality of the learning problem it solves. It achieves this by incorporating triplets of ... | [] | null | 67 | null | null |
26b6534eeac6dfc4a53a5acf158b9579 | Causal Strategic Inference in Networked Microfinance Economies | https://proceedings.neurips.cc/paper_files/paper/2014/hash/26b6534eeac6dfc4a53a5acf158b9579-Abstract.html | [
"Mohammad T Irfan",
"Luis E. Ortiz"
] | null | null | Performing interventions is a major challenge in economic policy-making. We propose \emph{causal strategic inference} as a framework for conducting interventions and apply it to large, networked microfinance economies. The basic solution platform consists of modeling a microfinance market as a networked economy, learni... | [] | null | 68 | null | null |
281c09b4594c6228d49f663799897178 | Multivariate Regression with Calibration | https://proceedings.neurips.cc/paper_files/paper/2014/hash/281c09b4594c6228d49f663799897178-Abstract.html | [
"Han Liu",
"Lie Wang",
"Tuo Zhao"
] | null | null | We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an imp... | [] | null | 69 | null | null |
2928cc8b05cf1bf9f7563cb005b1e37e | Recovery of Coherent Data via Low-Rank Dictionary Pursuit | https://proceedings.neurips.cc/paper_files/paper/2014/hash/2928cc8b05cf1bf9f7563cb005b1e37e-Abstract.html | [
"Guangcan Liu",
"Ping Li"
] | null | null | The recently established RPCA method provides a convenient way to restore low-rank matrices from grossly corrupted observations. While elegant in theory and powerful in reality, RPCA is not an ultimate solution to the low-rank matrix recovery problem. Indeed, its performance may not be perfect even when data are strict... | [] | null | 70 | 1404.4032 | title_snapshot |
29883d52f2590df7dfb27c69493c91d8 | On Communication Cost of Distributed Statistical Estimation and Dimensionality | https://proceedings.neurips.cc/paper_files/paper/2014/hash/29883d52f2590df7dfb27c69493c91d8-Abstract.html | [
"Ankit Garg",
"Tengyu Ma",
"Huy L. Nguyễn"
] | null | null | We explore the connection between dimensionality and communication cost in distributed learning problems. Specifically we study the problem of estimating the mean $\vectheta$ of an unknown $d$ dimensional gaussian distribution in the distributed setting. In this problem, the samples from the unknown distribution are di... | [] | null | 71 | 1405.1665 | title_snapshot |
29c4ed5dd426f7a4d854e7c209b9ac25 | Real-Time Decoding of an Integrate and Fire Encoder | https://proceedings.neurips.cc/paper_files/paper/2014/hash/29c4ed5dd426f7a4d854e7c209b9ac25-Abstract.html | [
"Shreya Saxena",
"Munther Dahleh"
] | null | null | Neuronal encoding models range from the detailed biophysically-based Hodgkin Huxley model, to the statistical linear time invariant model specifying firing rates in terms of the extrinsic signal. Decoding the former becomes intractable, while the latter does not adequately capture the nonlinearities present in the neur... | [] | null | 72 | null | null |
29d8ab58bcd65e45a831feeaed051d23 | Transportability from Multiple Environments with Limited Experiments: Completeness Results | https://proceedings.neurips.cc/paper_files/paper/2014/hash/29d8ab58bcd65e45a831feeaed051d23-Abstract.html | [
"Elias Bareinboim",
"Judea Pearl"
] | null | null | This paper addresses the problem of $mz$-transportability, that is, transferring causal knowledge collected in several heterogeneous domains to a target domain in which only passive observations and limited experimental data can be collected. The paper first establishes a necessary and sufficient condition for deciding... | [] | null | 73 | null | null |
2b0524a3000678a1f66bf38d546c8fd8 | Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain | https://proceedings.neurips.cc/paper_files/paper/2014/hash/2b0524a3000678a1f66bf38d546c8fd8-Abstract.html | [
"Charles Y Zheng",
"Franco Pestilli",
"Ariel Rokem"
] | null | null | Diffusion-weighted magnetic resonance imaging (DWI) and fiber tractography are the only methods to measure the structure of the white matter in the living human brain. The diffusion signal has been modelled as the combined contribution from many individual fascicles of nerve fibers passing through each location in the ... | [] | null | 74 | 1409.7134 | title_snapshot |
2b434b7c27c372d232dc6ba4c5402a09 | Convex Deep Learning via Normalized Kernels | https://proceedings.neurips.cc/paper_files/paper/2014/hash/2b434b7c27c372d232dc6ba4c5402a09-Abstract.html | [
"Özlem Aslan",
"Xinhua Zhang",
"Dale Schuurmans"
] | null | null | Deep learning has been a long standing pursuit in machine learning, which until recently was hampered by unreliable training methods before the discovery of improved heuristics for embedded layer training. A complementary research strategy is to develop alternative modeling architectures that admit efficient training m... | [] | null | 75 | null | null |
2b764b803acec2d590f02b160f8a3700 | Rates of Convergence for Nearest Neighbor Classification | https://proceedings.neurips.cc/paper_files/paper/2014/hash/2b764b803acec2d590f02b160f8a3700-Abstract.html | [
"Kamalika Chaudhuri",
"Sanjoy Dasgupta"
] | null | null | We analyze the behavior of nearest neighbor classification in metric spaces and provide finite-sample, distribution-dependent rates of convergence under minimal assumptions. These are more general than existing bounds, and enable us, as a by-product, to establish the universal consistency of nearest neighbor in a broad... | [] | null | 76 | 1407.0067 | title_snapshot |
2d6cd90d4f3fa50e6d9bdbc81a2e3712 | Stochastic Proximal Gradient Descent with Acceleration Techniques | https://proceedings.neurips.cc/paper_files/paper/2014/hash/2d6cd90d4f3fa50e6d9bdbc81a2e3712-Abstract.html | [
"Atsushi Nitanda"
] | null | null | Proximal gradient descent (PGD) and stochastic proximal gradient descent (SPGD) are popular methods for solving regularized risk minimization problems in machine learning and statistics. In this paper, we propose and analyze an accelerated variant of these methods in the mini-batch setting. This method incorporates two... | [] | null | 77 | null | null |
2d6e6b9675fb31f6c5250b7ea73fc37d | Augur: Data-Parallel Probabilistic Modeling | https://proceedings.neurips.cc/paper_files/paper/2014/hash/2d6e6b9675fb31f6c5250b7ea73fc37d-Abstract.html | [
"Jean-Baptiste Tristan",
"Daniel Huang",
"Joseph Tassarotti",
"Adam Pocock",
"Stephen J. Green",
"Guy L. Steele",
"Jr"
] | null | null | Implementing inference procedures for each new probabilistic model is time-consuming and error-prone. Probabilistic programming addresses this problem by allowing a user to specify the model and then automatically generating the inference procedure. To make this practical it is important to generate high performance in... | [] | null | 78 | null | null |
2e2f5540941a46e2f642b33f3276928d | An Autoencoder Approach to Learning Bilingual Word Representations | https://proceedings.neurips.cc/paper_files/paper/2014/hash/2e2f5540941a46e2f642b33f3276928d-Abstract.html | [
"Sarath Chandar A P",
"Stanislas Lauly",
"Hugo Larochelle",
"Mitesh M Khapra",
"Balaraman Ravindran",
"Vikas Raykar",
"Amrita Saha"
] | null | null | Cross-language learning allows us to use training data from one language to build models for a different language. Many approaches to bilingual learning require that we have word-level alignment of sentences from parallel corpora. In this work we explore the use of autoencoder-based methods for cross-language learning ... | [] | null | 79 | 1402.1454 | title_snapshot |
2e4ffe197475393c15c92fdfb1820cbd | Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology | https://proceedings.neurips.cc/paper_files/paper/2014/hash/2e4ffe197475393c15c92fdfb1820cbd-Abstract.html | [
"Rémi Lemonnier",
"Kevin Scaman",
"Nicolas Vayatis"
] | null | null | In this paper, we derive theoretical bounds for the long-term influence of a node in an Independent Cascade Model (ICM). We relate these bounds to the spectral radius of a particular matrix and show that the behavior is sub-critical when this spectral radius is lower than 1. More specifically, we point out that, in gen... | [] | null | 80 | 1407.4744 | title_snapshot |
2ee30f32fc44b88955b02c8a08aa069e | Latent Support Measure Machines for Bag-of-Words Data Classification | https://proceedings.neurips.cc/paper_files/paper/2014/hash/2ee30f32fc44b88955b02c8a08aa069e-Abstract.html | [
"Yuya Yoshikawa",
"Tomoharu Iwata",
"Hiroshi Sawada"
] | null | null | In many classification problems, the input is represented as a set of features, e.g., the bag-of-words (BoW) representation of documents. Support vector machines (SVMs) are widely used tools for such classification problems. The performance of the SVMs is generally determined by whether kernel values between data point... | [] | null | 81 | null | null |
2f9d64528ced0ea456b16aa7268f3463 | Deep Learning Face Representation by Joint Identification-Verification | https://proceedings.neurips.cc/paper_files/paper/2014/hash/2f9d64528ced0ea456b16aa7268f3463-Abstract.html | [
"Yi Sun",
"Yuheng Chen",
"Xiaogang Wang",
"Xiaoou Tang"
] | null | null | The key challenge of face recognition is to develop effective feature representations for reducing intra-personal variations while enlarging inter-personal differences. In this paper, we show that it can be well solved with deep learning and using both face identification and verification signals as supervision. The De... | [] | null | 82 | 1406.4773 | title_snapshot |
3000e56b48442cd23b49e5064bf1a9e6 | Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces | https://proceedings.neurips.cc/paper_files/paper/2014/hash/3000e56b48442cd23b49e5064bf1a9e6-Abstract.html | [
"Hà Quang Minh",
"Marco San Biagio",
"Vittorio Murino"
] | null | null | This paper introduces a novel mathematical and computational framework, namely {\it Log-Hilbert-Schmidt metric} between positive definite operators on a Hilbert space. This is a generalization of the Log-Euclidean metric on the Riemannian manifold of positive definite matrices to the infinite-dimensional setting. The g... | [] | null | 83 | null | null |
3073554c5b5472df57e59d9d565ebe13 | Low-Rank Time-Frequency Synthesis | https://proceedings.neurips.cc/paper_files/paper/2014/hash/3073554c5b5472df57e59d9d565ebe13-Abstract.html | [
"Cédric Févotte",
"Matthieu Kowalski"
] | null | null | Many single-channel signal decomposition techniques rely on a low-rank factorization of a time-frequency transform. In particular, nonnegative matrix factorization (NMF) of the spectrogram -- the (power) magnitude of the short-time Fourier transform (STFT) -- has been considered in many audio applications. In this sett... | [] | null | 84 | null | null |
30fbd5e091f51d7cf19153ccd3a4c969 | Learning Multiple Tasks in Parallel with a Shared Annotator | https://proceedings.neurips.cc/paper_files/paper/2014/hash/30fbd5e091f51d7cf19153ccd3a4c969-Abstract.html | [
"Haim Cohen",
"Koby Crammer"
] | null | null | We introduce a new multi-task framework, in which $K$ online learners are sharing a single annotator with limited bandwidth. On each round, each of the $K$ learners receives an input, and makes a prediction about the label of that input. Then, a shared (stochastic) mechanism decides which of the $K$ inputs will be anno... | [] | null | 85 | null | null |
317fd294bfd5c40816ce48bae30b1d4c | large scale canonical correlation analysis with iterative least squares | https://proceedings.neurips.cc/paper_files/paper/2014/hash/317fd294bfd5c40816ce48bae30b1d4c-Abstract.html | [
"Yichao Lu",
"Dean P. Foster"
] | null | null | Canonical Correlation Analysis (CCA) is a widely used statistical tool with both well established theory and favorable performance for a wide range of machine learning problems. However, computing CCA for huge datasets can be very slow since it involves implementing QR decomposition or singular value decomposition of h... | [] | null | 86 | 1407.4508 | title_snapshot |
3180c2243f2d3667bbe3855854554dcf | PEWA: Patch-based Exponentially Weighted Aggregation for image denoising | https://proceedings.neurips.cc/paper_files/paper/2014/hash/3180c2243f2d3667bbe3855854554dcf-Abstract.html | [
"Charles Kervrann"
] | null | null | Patch-based methods have been widely used for noise reduction in recent years. In this paper, we propose a general statistical aggregation method which combines image patches denoised with several commonly-used algorithms. We show that weakly denoised versions of the input image obtained with standard methods, can serv... | [] | null | 87 | null | null |
320f39caebd792d18483222f92c4498e | Large-Margin Convex Polytope Machine | https://proceedings.neurips.cc/paper_files/paper/2014/hash/320f39caebd792d18483222f92c4498e-Abstract.html | [
"Alex Kantchelian",
"Michael C Tschantz",
"Ling Huang",
"Peter L Bartlett",
"Anthony D Joseph",
"J. D. Tygar"
] | null | null | We present the Convex Polytope Machine (CPM), a novel non-linear learning algorithm for large-scale binary classification tasks. The CPM finds a large margin convex polytope separator which encloses one class. We develop a stochastic gradient descent based algorithm that is amenable to massive datasets, and augment it ... | [] | null | 88 | null | null |
325db0cfacc5572332b8acaf5ef2c151 | Hardness of parameter estimation in graphical models | https://proceedings.neurips.cc/paper_files/paper/2014/hash/325db0cfacc5572332b8acaf5ef2c151-Abstract.html | [
"Guy Bresler",
"David Gamarnik",
"Devavrat Shah"
] | null | null | We consider the problem of learning the canonical parameters specifying an undirected graphical model (Markov random field) from the mean parameters. For graphical models representing a minimal exponential family, the canonical parameters are uniquely determined by the mean parameters, so the problem is feasible in pri... | [] | null | 89 | 1409.3836 | title_snapshot |
34d5bca0f6c6d2e9962c84f5bddc3468 | Decoupled Variational Gaussian Inference | https://proceedings.neurips.cc/paper_files/paper/2014/hash/34d5bca0f6c6d2e9962c84f5bddc3468-Abstract.html | [
"Mohammad Emtiyaz Khan"
] | null | null | Variational Gaussian (VG) inference methods that optimize a lower bound to the marginal likelihood are a popular approach for Bayesian inference. These methods are fast and easy to use, while being reasonably accurate. A difficulty remains in computation of the lower bound when the latent dimensionality $L$ is large. E... | [] | null | 90 | null | null |
34fde01345258939e718af181fc0f996 | Unsupervised Deep Haar Scattering on Graphs | https://proceedings.neurips.cc/paper_files/paper/2014/hash/34fde01345258939e718af181fc0f996-Abstract.html | [
"Xu Chen",
"Xiuyuan Cheng",
"Stéphane Mallat"
] | null | null | The classification of high-dimensional data defined on graphs is particularly difficult when the graph geometry is unknown. We introduce a Haar scattering transform on graphs, which computes invariant signal descriptors. It is implemented with a deep cascade of additions, subtractions and absolute values, which iterati... | [] | null | 91 | 1406.2390 | title_snapshot |
35ab33f5f9a61426560675e75c14cc0b | Sparse Space-Time Deconvolution for Calcium Image Analysis | https://proceedings.neurips.cc/paper_files/paper/2014/hash/35ab33f5f9a61426560675e75c14cc0b-Abstract.html | [
"Ferran Diego Andilla",
"Fred A. Hamprecht"
] | null | null | We describe a unified formulation and algorithm to find an extremely sparse representation for Calcium image sequences in terms of cell locations, cell shapes, spike timings and impulse responses. Solution of a single optimization problem yields cell segmentations and activity estimates that are on par with the state o... | [] | null | 92 | null | null |
35f1050a4381d2d216bf56ad46b0277d | Algorithms for CVaR Optimization in MDPs | https://proceedings.neurips.cc/paper_files/paper/2014/hash/35f1050a4381d2d216bf56ad46b0277d-Abstract.html | [
"Yinlam Chow",
"Mohammad Ghavamzadeh"
] | null | null | In many sequential decision-making problems we may want to manage risk by minimizing some measure of variability in costs in addition to minimizing a standard criterion. Conditional value-at-risk (CVaR) is a relatively new risk measure that addresses some of the shortcomings of the well-known variance-related risk meas... | [] | null | 93 | 1406.3339 | title_snapshot |
3644e33a5161ec5f3997a6acb98d4447 | On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures | https://proceedings.neurips.cc/paper_files/paper/2014/hash/3644e33a5161ec5f3997a6acb98d4447-Abstract.html | [
"Harikrishna Narasimhan",
"Rohit Vaish",
"Shivani Agarwal"
] | null | null | We study consistency properties of algorithms for non-decomposable performance measures that cannot be expressed as a sum of losses on individual data points, such as the F-measure used in text retrieval and several other performance measures used in class imbalanced settings. While there has been much work on designin... | [] | null | 94 | null | null |
377a6f507bc67aaac04a0eafca076ea2 | QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models | https://proceedings.neurips.cc/paper_files/paper/2014/hash/377a6f507bc67aaac04a0eafca076ea2-Abstract.html | [
"Cho-Jui Hsieh",
"Inderjit S. Dhillon",
"Pradeep Ravikumar",
"Stephen Becker",
"Peder A. Olsen"
] | null | null | In this paper, we develop a family of algorithms for optimizing superposition-structured” or “dirty” statistical estimators for high-dimensional problems involving the minimization of the sum of a smooth loss function with a hybrid regularization. Most of the current approaches are first-order methods, including proxim... | [] | null | 95 | null | null |
37f8ddca0e675015440e5ff536c8fa83 | Deep Convolutional Neural Network for Image Deconvolution | https://proceedings.neurips.cc/paper_files/paper/2014/hash/37f8ddca0e675015440e5ff536c8fa83-Abstract.html | [
"Li Xu",
"Jimmy SJ. Ren",
"Ce Liu",
"Jiaya Jia"
] | null | null | Many fundamental image-related problems involve deconvolution operators. Real blur degradation seldom complies with an deal linear convolution model due to camera noise, saturation, image compression, to name a few. Instead of perfectly modeling outliers, which is rather challenging from a generative model perspective,... | [] | null | 96 | null | null |
39945d578f616735572174bf5e8f155d | Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations | https://proceedings.neurips.cc/paper_files/paper/2014/hash/39945d578f616735572174bf5e8f155d-Abstract.html | [
"Zhenyao Zhu",
"Ping Luo",
"Xiaogang Wang",
"Xiaoou Tang"
] | null | null | Various factors, such as identities, views (poses), and illuminations, are coupled in face images. Disentangling the identity and view representations is a major challenge in face recognition. Existing face recognition systems either use handcrafted features or learn features discriminatively to improve recognition acc... | [] | null | 97 | null | null |
3a2ee4c801c8820c72af84e6b6c7ad2e | LSDA: Large Scale Detection through Adaptation | https://proceedings.neurips.cc/paper_files/paper/2014/hash/3a2ee4c801c8820c72af84e6b6c7ad2e-Abstract.html | [
"Judy Hoffman",
"Sergio Guadarrama",
"Eric Tzeng",
"Ronghang Hu",
"Jeff Donahue",
"Ross Girshick",
"Trevor Darrell",
"Kate Saenko"
] | null | null | A major challenge in scaling object detection is the difficulty of obtaining labeled images for large numbers of categories. Recently, deep convolutional neural networks (CNNs) have emerged as clear winners on object classification benchmarks, in part due to training with 1.2M+ labeled classification images. Unfortunat... | [] | null | 98 | 1407.5035 | title_snapshot |
3a71f5372dbc341c48a65df7e1efb831 | Deep Joint Task Learning for Generic Object Extraction | https://proceedings.neurips.cc/paper_files/paper/2014/hash/3a71f5372dbc341c48a65df7e1efb831-Abstract.html | [
"Xiaolong Wang",
"Liliang Zhang",
"Liang Lin",
"Zhujin Liang",
"Wangmeng Zuo"
] | null | null | This paper investigates how to extract objects-of-interest without relying on hand-craft features and sliding windows approaches, that aims to jointly solve two sub-tasks: (i) rapidly localizing salient objects from images, and (ii) accurately segmenting the objects based on the localizations. We present a general join... | [] | null | 99 | 1502.00743 | title_snapshot |
3a794b71830091b1e8048312eb649c88 | Distributed Power-law Graph Computing: Theoretical and Empirical Analysis | https://proceedings.neurips.cc/paper_files/paper/2014/hash/3a794b71830091b1e8048312eb649c88-Abstract.html | [
"Cong Xie",
"Ling Yan",
"Wu-Jun Li",
"Zhihua Zhang"
] | null | null | With the emergence of big graphs in a variety of real applications like social networks, machine learning based on distributed graph-computing~(DGC) frameworks has attracted much attention from big data machine learning community. In DGC frameworks, the graph partitioning~(GP) strategy plays a key role to affect the pe... | [] | null | 100 | null | null |
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