ICML
Collection
Accepted papers for ICML (International Conference on Machine Learning), 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
Stochastic Optimization with Importance Sampling for Regularized Loss Minimization | https://proceedings.mlr.press/v37/zhaoa15.html | [
"Peilin Zhao",
"Tong Zhang"
] | null | null | Uniform sampling of training data has been commonly used in traditional stochastic optimization algorithms such as Proximal Stochastic Mirror Descent (prox-SMD) and Proximal Stochastic Dual Coordinate Ascent (prox-SDCA). Although uniform sampling can guarantee that the sampled stochastic quantity is an unbiased estimat... | [] | null | 1 | null | null | [
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Approval Voting and Incentives in Crowdsourcing | https://proceedings.mlr.press/v37/shaha15.html | [
"Nihar Shah",
"Dengyong Zhou",
"Yuval Peres"
] | null | null | The growing need for labeled training data has made crowdsourcing an important part of machine learning. The quality of crowdsourced labels is, however, adversely affected by three factors: (1) the workers are not experts; (2) the incentives of the workers are not aligned with those of the requesters; and (3) the inter... | [] | null | 2 | 1502.05696 | title_snapshot | [
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A low variance consistent test of relative dependency | https://proceedings.mlr.press/v37/bounliphone15.html | [
"Wacha Bounliphone",
"Arthur Gretton",
"Arthur Tenenhaus",
"Matthew Blaschko"
] | null | null | We describe a novel non-parametric statistical hypothesis test of relative dependence between a source variable and two candidate target variables. Such a test enables us to determine whether one source variable is significantly more dependent on a first target variable or a second. Dependence is measured via the Hilbe... | [] | null | 3 | 1406.3852 | title_snapshot | [
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An Aligned Subtree Kernel for Weighted Graphs | https://proceedings.mlr.press/v37/bai15.html | [
"Lu Bai",
"Luca Rossi",
"Zhihong Zhang",
"Edwin Hancock"
] | null | null | In this paper, we develop a new entropic matching kernel for weighted graphs by aligning depth-based representations. We demonstrate that this kernel can be seen as an \textbfaligned subtree kernel that incorporates explicit subtree correspondences, and thus addresses the drawback of neglecting the relative locations b... | [] | null | 4 | null | null | [
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Spectral Clustering via the Power Method - Provably | https://proceedings.mlr.press/v37/boutsidis15.html | [
"Christos Boutsidis",
"Prabhanjan Kambadur",
"Alex Gittens"
] | null | null | Spectral clustering is one of the most important algorithms in data mining and machine intelligence; however, its computational complexity limits its application to truly large scale data analysis. The computational bottleneck in spectral clustering is computing a few of the top eigenvectors of the (normalized) Laplaci... | [] | null | 5 | 1311.2854 | title_snapshot | [
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Information Geometry and Minimum Description Length Networks | https://proceedings.mlr.press/v37/suna15.html | [
"Ke Sun",
"Jun Wang",
"Alexandros Kalousis",
"Stephan Marchand-Maillet"
] | null | null | We study parametric unsupervised mixture learning. We measure the loss of intrinsic information from the observations to complex mixture models, and then to simple mixture models. We present a geometric picture, where all these representations are regarded as free points in the space of probability distributions. Based... | [] | null | 6 | null | null | [
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Efficient Training of LDA on a GPU by Mean-for-Mode Estimation | https://proceedings.mlr.press/v37/tristan15.html | [
"Jean-Baptiste Tristan",
"Joseph Tassarotti",
"Guy Steele"
] | null | null | We introduce Mean-for-Mode estimation, a variant of an uncollapsed Gibbs sampler that we use to train LDA on a GPU. The algorithm combines benefits of both uncollapsed and collapsed Gibbs samplers. Like a collapsed Gibbs sampler — and unlike an uncollapsed Gibbs sampler — it has good statistical performance, and can us... | [] | null | 7 | null | null | [
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Adaptive Stochastic Alternating Direction Method of Multipliers | https://proceedings.mlr.press/v37/zhaob15.html | [
"Peilin Zhao",
"Jinwei Yang",
"Tong Zhang",
"Ping Li"
] | null | null | The Alternating Direction Method of Multipliers (ADMM) has been studied for years. Traditional ADMM algorithms need to compute, at each iteration, an (empirical) expected loss function on all training examples, resulting in a computational complexity proportional to the number of training examples. To reduce the comple... | [] | null | 8 | 1312.4564 | title_snapshot | [
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A Lower Bound for the Optimization of Finite Sums | https://proceedings.mlr.press/v37/agarwal15.html | [
"Alekh Agarwal",
"Leon Bottou"
] | null | null | This paper presents a lower bound for optimizing a finite sum of n functions, where each function is L-smooth and the sum is μ-strongly convex. We show that no algorithm can reach an error εin minimizing all functions from this class in fewer than Ω(n + \sqrtn(κ-1)\log(1/ε)) iterations, where κ=L/μis a surrogate condit... | [] | null | 9 | 1410.0723 | title_snapshot | [
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Learning Word Representations with Hierarchical Sparse Coding | https://proceedings.mlr.press/v37/yogatama15.html | [
"Dani Yogatama",
"Manaal Faruqui",
"Chris Dyer",
"Noah Smith"
] | null | null | We propose a new method for learning word representations using hierarchical regularization in sparse coding inspired by the linguistic study of word meanings. We show an efficient learning algorithm based on stochastic proximal methods that is significantly faster than previous approaches, making it possible to perfor... | [] | null | 10 | 1406.2035 | title_snapshot | [
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Learning Transferable Features with Deep Adaptation Networks | https://proceedings.mlr.press/v37/long15.html | [
"Mingsheng Long",
"Yue Cao",
"Jianmin Wang",
"Michael Jordan"
] | null | null | Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain... | [] | null | 11 | 1502.02791 | title_snapshot | [
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0.... |
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