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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling | https://proceedings.neurips.cc/paper_files/paper/2015/hash/01f78be6f7cad02658508fe4616098a9-Abstract.html | [
"Zheng Qu",
"Peter Richtarik",
"Tong Zhang"
] | null | null | We study the problem of minimizing the average of a large number of smooth convex functions penalized with a strongly convex regularizer. We propose and analyze a novel primal-dual method (Quartz) which at every iteration samples and updates a random subset of the dual variables, chosen according to an arbitrary distri... | [] | null | 1 | 1411.5873 | title_judge | [
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Associative Memory via a Sparse Recovery Model | https://proceedings.neurips.cc/paper_files/paper/2015/hash/020c8bfac8de160d4c5543b96d1fdede-Abstract.html | [
"Arya Mazumdar",
"Ankit Singh Rawat"
] | null | null | An associative memory is a structure learned from a dataset $\mathcal{M}$ of vectors (signals) in a way such that, given a noisy version of one of the vectors as input, the nearest valid vector from $\mathcal{M}$ (nearest neighbor) is provided as output, preferably via a fast iterative algorithm. Traditionally, binary ... | [] | null | 2 | null | null | [
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Policy Gradient for Coherent Risk Measures | https://proceedings.neurips.cc/paper_files/paper/2015/hash/024d7f84fff11dd7e8d9c510137a2381-Abstract.html | [
"Aviv Tamar",
"Yinlam Chow",
"Mohammad Ghavamzadeh",
"Shie Mannor"
] | null | null | Several authors have recently developed risk-sensitive policy gradient methods that augment the standard expected cost minimization problem with a measure of variability in cost. These studies have focused on specific risk-measures, such as the variance or conditional value at risk (CVaR). In this work, we extend the p... | [] | null | 3 | 1502.03919 | title_snapshot | [
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A fast, universal algorithm to learn parametric nonlinear embeddings | https://proceedings.neurips.cc/paper_files/paper/2015/hash/02522a2b2726fb0a03bb19f2d8d9524d-Abstract.html | [
"Miguel A. Carreira-Perpinan",
"Max Vladymyrov"
] | null | null | Nonlinear embedding algorithms such as stochastic neighbor embedding do dimensionality reduction by optimizing an objective function involving similarities between pairs of input patterns. The result is a low-dimensional projection of each input pattern. A common way to define an out-of-sample mapping is to optimize th... | [] | null | 4 | null | null | [
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Stochastic Online Greedy Learning with Semi-bandit Feedbacks | https://proceedings.neurips.cc/paper_files/paper/2015/hash/0266e33d3f546cb5436a10798e657d97-Abstract.html | [
"Tian Lin",
"Jian Li",
"Wei Chen"
] | null | null | The greedy algorithm is extensively studied in the field of combinatorial optimization for decades. In this paper, we address the online learning problem when the input to the greedy algorithm is stochastic with unknown parameters that have to be learned over time. We first propose the greedy regret and $\epsilon$-quas... | [] | null | 5 | null | null | [
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SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals | https://proceedings.neurips.cc/paper_files/paper/2015/hash/02a32ad2669e6fe298e607fe7cc0e1a0-Abstract.html | [
"Qing Sun",
"Dhruv Batra"
] | null | null | This paper formulates the search for a set of bounding boxes (as needed in object proposal generation) as a monotone submodular maximization problem over the space of all possible bounding boxes in an image. Since the number of possible bounding boxes in an image is very large $O(#pixels^2)$, even a single linear scan ... | [] | null | 6 | null | null | [
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Robust Portfolio Optimization | https://proceedings.neurips.cc/paper_files/paper/2015/hash/02e74f10e0327ad868d138f2b4fdd6f0-Abstract.html | [
"Huitong Qiu",
"Fang Han",
"Han Liu",
"Brian Caffo"
] | null | null | We propose a robust portfolio optimization approach based on quantile statistics. The proposed method is robust to extreme events in asset returns, and accommodates large portfolios under limited historical data. Specifically, we show that the risk of the estimated portfolio converges to the oracle optimal risk with pa... | [] | null | 7 | null | null | [
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Top-k Multiclass SVM | https://proceedings.neurips.cc/paper_files/paper/2015/hash/0336dcbab05b9d5ad24f4333c7658a0e-Abstract.html | [
"Maksim Lapin",
"Matthias Hein",
"Bernt Schiele"
] | null | null | Class ambiguity is typical in image classification problems with a large number of classes. When classes are difficult to discriminate, it makes sense to allow k guesses and evaluate classifiers based on the top-k error instead of the standard zero-one loss. We propose top-k multiclass SVM as a direct method to optimiz... | [] | null | 8 | 1511.06683 | title_snapshot | [
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Less is More: Nyström Computational Regularization | https://proceedings.neurips.cc/paper_files/paper/2015/hash/03e0704b5690a2dee1861dc3ad3316c9-Abstract.html | [
"Alessandro Rudi",
"Raffaello Camoriano",
"Lorenzo Rosasco"
] | null | null | We study Nyström type subsampling approaches to large scale kernel methods, and prove learning bounds in the statistical learning setting, where random sampling and high probability estimates are considered. In particular, we prove that these approaches can achieve optimal learning bounds, provided the subsampling leve... | [] | null | 9 | 1507.04717 | title_snapshot | [
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Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models | https://proceedings.neurips.cc/paper_files/paper/2015/hash/04ecb1fa28506ccb6f72b12c0245ddbc-Abstract.html | [
"Akihiro Kishimoto",
"Radu Marinescu",
"Adi Botea"
] | null | null | The paper presents and evaluates the power of parallel search for exact MAP inference in graphical models. We introduce a new parallel shared-memory recursive best-first AND/OR search algorithm, called SPRBFAOO, that explores the search space in a best-first manner while operating with restricted memory. Our experiment... | [] | null | 10 | null | null | [
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Differentially private subspace clustering | https://proceedings.neurips.cc/paper_files/paper/2015/hash/051e4e127b92f5d98d3c79b195f2b291-Abstract.html | [
"Yining Wang",
"Yu-Xiang Wang",
"Aarti Singh"
] | null | null | Subspace clustering is an unsupervised learning problem that aims at grouping data points into multiple clusters'' so that data points in a single cluster lie approximately on a low-dimensional linear subspace. It is originally motivated by 3D motion segmentation in computer vision, but has recently been generically ap... | [] | null | 11 | null | null | [
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