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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
A Discriminative Latent Variable Model for Online Clustering | https://proceedings.mlr.press/v32/samdani14.html | [
"Rajhans Samdani",
"Kai-Wei Chang",
"Dan Roth"
] | null | null | This paper presents a latent variable structured prediction model for discriminative supervised clustering of items called the Latent Left-linking Model (L3M). We present an online clustering algorithm for L3M based on a feature-based item similarity function. We provide a learning framework for estimating the similari... | [] | null | 1 | null | null | [
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Kernel Mean Estimation and Stein Effect | https://proceedings.mlr.press/v32/muandet14.html | [
"Krikamol Muandet",
"Kenji Fukumizu",
"Bharath Sriperumbudur",
"Arthur Gretton",
"Bernhard Schoelkopf"
] | null | null | A mean function in reproducing kernel Hilbert space (RKHS), or a kernel mean, is an important part of many algorithms ranging from kernel principal component analysis to Hilbert-space embedding of distributions. Given a finite sample, an empirical average is the standard estimate for the true kernel mean. We show that ... | [] | null | 2 | null | null | [
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Demystifying Information-Theoretic Clustering | https://proceedings.mlr.press/v32/steeg14.html | [
"Greg Ver Steeg",
"Aram Galstyan",
"Fei Sha",
"Simon DeDeo"
] | null | null | We propose a novel method for clustering data which is grounded in information-theoretic principles and requires no parametric assumptions. Previous attempts to use information theory to define clusters in an assumption-free way are based on maximizing mutual information between data and cluster labels. We demonstrate... | [] | null | 3 | 1310.4210 | title_snapshot | [
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Covering Number for Efficient Heuristic-based POMDP Planning | https://proceedings.mlr.press/v32/zhanga14.html | [
"Zongzhang Zhang",
"David Hsu",
"Wee Sun Lee"
] | null | null | The difficulty of POMDP planning depends on the size of the search space involved. Heuristics are often used to reduce the search space size and improve computational efficiency; however, there are few theoretical bounds on their effectiveness. In this paper, we use the covering number to characterize the size of the ... | [] | null | 4 | null | null | [
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The Coherent Loss Function for Classification | https://proceedings.mlr.press/v32/yanga14.html | [
"Wenzhuo Yang",
"Melvyn Sim",
"Huan Xu"
] | null | null | A prediction rule in binary classification that aims to achieve the lowest probability of misclassification involves minimizing over a non-convex, 0-1 loss function, which is typically a computationally intractable optimization problem. To address the intractability, previous methods consider minimizing the cumulative ... | [] | null | 5 | null | null | [
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Fast Stochastic Alternating Direction Method of Multipliers | https://proceedings.mlr.press/v32/zhong14.html | [
"Wenliang Zhong",
"James Kwok"
] | null | null | We propose a new stochastic alternating direction method of multipliers (ADMM) algorithm, which incrementally approximates the full gradient in the linearized ADMM formulation. Besides having a low per-iteration complexity as existing stochastic ADMM algorithms, it improves the convergence rate on convex problems from... | [] | null | 6 | 1308.3558 | title_snapshot | [
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Active Detection via Adaptive Submodularity | https://proceedings.mlr.press/v32/chena14.html | [
"Yuxin Chen",
"Hiroaki Shioi",
"Cesar Fuentes Montesinos",
"Lian Pin Koh",
"Serge Wich",
"Andreas Krause"
] | null | null | Efficient detection of multiple object instances is one of the fundamental challenges in computer vision. For certain object categories, even the best automatic systems are yet unable to produce high-quality detection results, and fully manual annotation would be an expensive process. How can detection algorithms inter... | [] | null | 7 | null | null | [
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Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization | https://proceedings.mlr.press/v32/shalev-shwartz14.html | [
"Shai Shalev-Shwartz",
"Tong Zhang"
] | null | null | We introduce a proximal version of the stochastic dual coordinate ascent method and show how to accelerate the method using an inner-outer iteration procedure. We analyze the runtime of the framework and obtain rates that improve state-of-the-art results for various key machine learning optimization problems including ... | [] | null | 8 | 1309.2375 | title_snapshot | [
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An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization | https://proceedings.mlr.press/v32/lin14.html | [
"Qihang Lin",
"Lin Xiao"
] | null | null | We first propose an adaptive accelerated proximal gradient(APG) method for minimizing strongly convex composite functions with unknown convexity parameters. This method incorporates a restarting scheme to automatically estimate the strong convexity parameter and achieves a nearly optimal iteration complexity. Then we c... | [] | null | 9 | null | null | [
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Recurrent Convolutional Neural Networks for Scene Labeling | https://proceedings.mlr.press/v32/pinheiro14.html | [
"Pedro Pinheiro",
"Ronan Collobert"
] | null | null | The goal of the scene labeling task is to assign a class label to each pixel in an image. To ensure a good visual coherence and a high class accuracy, it is essential for a model to capture long range pixel) label dependencies in images. In a feed-forward architecture, this can be achieved simply by considering a suf... | [] | null | 10 | null | null | [
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A Statistical Perspective on Algorithmic Leveraging | https://proceedings.mlr.press/v32/ma14.html | [
"Ping Ma",
"Michael Mahoney",
"Bin Yu"
] | null | null | One popular method for dealing with large-scale data sets is sampling. Using the empirical statistical leverage scores as an importance sampling distribution, the method of algorithmic leveraging samples and rescales rows/columns of data matrices to reduce the data size before performing computations on the subproblem.... | [] | null | 11 | 1306.5362 | title_snapshot | [
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-... |
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