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» A Preference Model for Structured Supervised Learning Tasks
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ICML
2010
IEEE
14 years 7 months 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
KDD
2009
ACM
156views Data Mining» more  KDD 2009»
15 years 10 months ago
Turning down the noise in the blogosphere
In recent years, the blogosphere has experienced a substantial increase in the number of posts published daily, forcing users to cope with information overload. The task of guidin...
Khalid El-Arini, Gaurav Veda, Dafna Shahaf, Carlos...
TASLP
2008
98views more  TASLP 2008»
14 years 9 months ago
Unsupervised Single-Channel Music Source Separation by Average Harmonic Structure Modeling
Source separation of musical signals is an appealing but difficult problem, especially in the single-channel case. In this paper, an unsupervised single-channel music source separa...
Zhiyao Duan, Yungang Zhang, Changshui Zhang, Zhenw...
ICML
2010
IEEE
14 years 10 months ago
Learning Efficiently with Approximate Inference via Dual Losses
Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation...
Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Glo...
LAMAS
2005
Springer
15 years 3 months ago
Multi-agent Relational Reinforcement Learning
In this paper we report on using a relational state space in multi-agent reinforcement learning. There is growing evidence in the Reinforcement Learning research community that a r...
Tom Croonenborghs, Karl Tuyls, Jan Ramon, Maurice ...