NeurIPS
Collection
Accepted papers for NeurIPS (Conference on Neural Information Processing Systems), one dataset per year. • 13 items • Updated
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
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 | [
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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 | [
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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 | [
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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 | [
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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 | [
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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 | [
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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 | [
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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 | [
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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 | [
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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 | [
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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 | [
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-... |
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