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ICML
2010
IEEE
13 years 10 months ago
Metric Learning to Rank
We study metric learning as a problem of information retrieval. We present a general metric learning algorithm, based on the structural SVM framework, to learn a metric such that ...
Brian McFee, Gert R. G. Lanckriet
ICML
2010
IEEE
13 years 10 months ago
A Stick-Breaking Construction of the Beta Process
We present and derive a new stick-breaking construction of the beta process. The construction is closely related to a special case of the stick-breaking construction of the Dirich...
John William Paisley, Aimee Zaas, Christopher W. W...
ICML
2010
IEEE
13 years 10 months ago
Boosting for Regression Transfer
The goal of transfer learning is to improve the learning of a new target concept given knowledge of related source concept(s). We introduce the first boosting-based algorithms for...
David Pardoe, Peter Stone
ICML
2010
IEEE
13 years 10 months ago
On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning
A learning problem might have several measures of complexity (e.g., norm and dimensionality) that affect the generalization error. What is the interaction between these complexiti...
Percy Liang, Nati Srebro
ICML
2010
IEEE
13 years 10 months ago
Convergence, Targeted Optimality, and Safety in Multiagent Learning
This paper introduces a novel multiagent learning algorithm, Convergence with Model Learning and Safety (or CMLeS in short), which achieves convergence, targeted optimality agains...
Doran Chakraborty, Peter Stone
ICML
2010
IEEE
13 years 10 months ago
Causal filter selection in microarray data
The importance of bringing causality into play when designing feature selection methods is more and more acknowledged in the machine learning community. This paper proposes a filt...
Gianluca Bontempi, Patrick Emmanuel Meyer
ICML
2010
IEEE
13 years 10 months ago
Gaussian Processes Multiple Instance Learning
This paper proposes a multiple instance learning (MIL) algorithm for Gaussian processes (GP). The GP-MIL model inherits two crucial benefits from GP: (i) a principle manner of lea...
Minyoung Kim, Fernando De la Torre
ICML
2010
IEEE
13 years 10 months ago
Conditional Topic Random Fields
Generative topic models such as LDA are limited by their inability to utilize nontrivial input features to enhance their performance, and many topic models assume that topic assig...
Jun Zhu, Eric P. Xing
ICML
2010
IEEE
13 years 10 months ago
Bayesian Multi-Task Reinforcement Learning
We consider the problem of multi-task reinforcement learning where the learner is provided with a set of tasks, for which only a small number of samples can be generated for any g...
Alessandro Lazaric, Mohammad Ghavamzadeh