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ICML
2010
IEEE
10 years 21 days ago
Comparing Clusterings in Space
This paper proposes a new method for comparing clusterings both partitionally and geometrically. Our approach is motivated by the following observation: the vast majority of previ...
Michael H. Coen, M. Hidayath Ansari, Nathanael Fil...
ICML
2010
IEEE
10 years 21 days ago
Online Prediction with Privacy
In this paper, we consider online prediction from expert advice in a situation where each expert observes its own loss at each time while the loss cannot be disclosed to others fo...
Jun Sakuma, Hiromi Arai
ICML
2010
IEEE
10 years 21 days ago
Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains
In the realm of multilabel classification (MLC), it has become an opinio communis that optimal predictive performance can only be achieved by learners that explicitly take label d...
Krzysztof Dembczynski, Weiwei Cheng, Eyke Hül...
ICML
2010
IEEE
10 years 21 days ago
Mining Clustering Dimensions
Many real-world datasets can be clustered along multiple dimensions. For example, text documents can be clustered not only by topic, but also by the author's gender or sentim...
Sajib Dasgupta, Vincent Ng
ICML
2010
IEEE
10 years 21 days ago
Learning optimally diverse rankings over large document collections
Most learning to rank research has assumed that the utility of different documents is independent, which results in learned ranking functions that return redundant results. The fe...
Aleksandrs Slivkins, Filip Radlinski, Sreenivas Go...
ICML
2010
IEEE
10 years 21 days ago
Implicit Regularization in Variational Bayesian Matrix Factorization
Matrix factorization into the product of lowrank matrices induces non-identifiability, i.e., the mapping between the target matrix and factorized matrices is not one-to-one. In th...
Shinichi Nakajima, Masashi Sugiyama
ICML
2010
IEEE
10 years 21 days ago
Constructing States for Reinforcement Learning
POMDPs are the models of choice for reinforcement learning (RL) tasks where the environment cannot be observed directly. In many applications we need to learn the POMDP structure ...
M. M. Hassan Mahmud
ICML
2010
IEEE
10 years 21 days ago
The Margin Perceptron with Unlearning
We introduce into the classical Perceptron algorithm with margin a mechanism of unlearning which in the course of the regular update allows for a reduction of possible contributio...
Constantinos Panagiotakopoulos, Petroula Tsampouka
ICML
2010
IEEE
10 years 21 days ago
Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting Lambda
Temporal difference (TD) algorithms are attractive for reinforcement learning due to their ease-of-implementation and use of "bootstrapped" return estimates to make effi...
Carlton Downey, Scott Sanner
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