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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
An Optimal Policy for Target Localization with Application to Electron Microscopy | https://proceedings.mlr.press/v28/sznitman13.html | [
"Raphael Sznitman",
"Aurelien Lucchi",
"Peter Frazier",
"Bruno Jedynak",
"Pascal Fua"
] | null | null | This paper considers the task of finding a target location by making a limited number of sequential observations. Each observation results from evaluating an imperfect classifier of a chosen cost and accuracy on an interval of chosen length and position. Within a Bayesian framework, we study the problem of minimizing... | [] | null | 1 | null | null | [
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Domain Generalization via Invariant Feature Representation | https://proceedings.mlr.press/v28/muandet13.html | [
"Krikamol Muandet",
"David Balduzzi",
"Bernhard Schölkopf"
] | null | null | This paper investigates domain generalization: How to take knowledge acquired from an arbitrary number of related domains and apply it to previously unseen domains? We propose Domain-Invariant Component Analysis (DICA), a kernel-based optimization algorithm that learns an invariant transformation by minimizing the diss... | [] | null | 2 | 1301.2115 | title_snapshot | [
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A Spectral Learning Approach to Range-Only SLAM | https://proceedings.mlr.press/v28/boots13.html | [
"Byron Boots",
"Geoff Gordon"
] | null | null | We present a novel spectral learning algorithm for simultaneous localization and mapping (SLAM) from range data with known correspondences. This algorithm is an instance of a general spectral system identification framework, from which it inherits several desirable properties, including statistical consistency and no ... | [] | null | 3 | 1207.2491 | title_snapshot | [
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Near-Optimal Bounds for Cross-Validation via Loss Stability | https://proceedings.mlr.press/v28/kumar13a.html | [
"Ravi Kumar",
"Daniel Lokshtanov",
"Sergei Vassilvitskii",
"Andrea Vattani"
] | null | null | Multi-fold cross-validation is an established practice to estimate the error rate of a learning algorithm. Quantifying the variance reduction gains due to cross-validation has been challenging due to the inherent correlations introduced by the folds. In this work we introduce a new and weak measure of stability... | [] | null | 4 | null | null | [
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Sparsity-Based Generalization Bounds for Predictive Sparse Coding | https://proceedings.mlr.press/v28/mehta13.html | [
"Nishant Mehta",
"Alexander Gray"
] | null | null | The goal of predictive sparse coding is to learn a representation of examples as sparse linear combinations of elements from a dictionary, such that a learned hypothesis linear in the new representation performs well on a predictive task. Predictive sparse coding has demonstrated impressive performance on a variety of ... | [] | null | 5 | null | null | [
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Sparse Uncorrelated Linear Discriminant Analysis | https://proceedings.mlr.press/v28/zhang13.html | [
"Xiaowei Zhang",
"Delin Chu"
] | null | null | In this paper, we develop a novel approach for sparse uncorrelated linear discriminant analysis (ULDA). Our proposal is based on characterization of all solutions of the generalized ULDA. We incorporate sparsity into the ULDA transformation by seeking the solution with minimum \ell_1-norm from all minimum dimension sol... | [] | null | 6 | null | null | [
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Block-Coordinate Frank-Wolfe Optimization for Structural SVMs | https://proceedings.mlr.press/v28/lacoste-julien13.html | [
"Simon Lacoste-Julien",
"Martin Jaggi",
"Mark Schmidt",
"Patrick Pletscher"
] | null | null | We propose a randomized block-coordinate variant of the classic Frank-Wolfe algorithm for convex optimization with block-separable constraints. Despite its lower iteration cost, we show that it achieves a similar convergence rate in duality gap as the full Frank-Wolfe algorithm. We also show that, when applied to the d... | [] | null | 7 | 1207.4747 | title_snapshot | [
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Fast Probabilistic Optimization from Noisy Gradients | https://proceedings.mlr.press/v28/hennig13.html | [
"Philipp Hennig"
] | null | null | Stochastic gradient descent remains popular in large-scale machine learning, on account of its very low computational cost and robustness to noise. However, gradient descent is only linearly efficient and not transformation invariant. Scaling by a local measure can substantially improve its performance. One natural cho... | [] | null | 8 | null | null | [
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Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes | https://proceedings.mlr.press/v28/shamir13.html | [
"Ohad Shamir",
"Tong Zhang"
] | null | null | Stochastic Gradient Descent (SGD) is one of the simplest and most popular stochastic optimization methods. While it has already been theoretically studied for decades, the classical analysis usually required non-trivial smoothness assumptions, which do not apply to many modern applications of SGD with non-smooth object... | [] | null | 9 | 1212.1824 | title_snapshot | [
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Stochastic Alternating Direction Method of Multipliers | https://proceedings.mlr.press/v28/ouyang13.html | [
"Hua Ouyang",
"Niao He",
"Long Tran",
"Alexander Gray"
] | null | null | The Alternating Direction Method of Multipliers (ADMM) has received lots of attention recently due to the tremendous demand from large-scale and data-distributed machine learning applications. In this paper, we present a stochastic setting for optimization problems with non-smooth composite objective functions. To solv... | [] | null | 10 | null | null | [
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Noisy Sparse Subspace Clustering | https://proceedings.mlr.press/v28/wang13.html | [
"Yu-Xiang Wang",
"Huan Xu"
] | null | null | This paper considers the problem of subspace clustering under noise. Specifically, we study the behavior of Sparse Subspace Clustering (SSC) when either adversarial or random noise is added to the unlabelled input data points, which are assumed to lie in a union of low-dimensional subspaces. We show that a modified ve... | [] | null | 11 | 1309.1233 | title_snapshot | [
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... |
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