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AAAI
2011
14 years 6 months ago
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models
Coarse-to-fine approaches use sequences of increasingly fine approximations to control the complexity of inference and learning. These techniques are often used in NLP and visio...
Chloe Kiddon, Pedro Domingos
AAAI
2012
13 years 9 months ago
A Sequential Decision Approach to Ordinal Preferences in Recommender Systems
We propose a novel sequential decision approach to modeling ordinal ratings in collaborative filtering problems. The rating process is assumed to start from the lowest level, eva...
Truyen Tran, Dinh Q. Phung, Svetha Venkatesh
AAAI
2012
13 years 9 months ago
A Dynamic Rationalization of Distance Rationalizability
Distance rationalizability is an intuitive paradigm for developing and studying voting rules: given a notion of consensus and a distance function on preference profiles, a ration...
Craig Boutilier, Ariel D. Procaccia
AAAI
2012
13 years 9 months ago
Kernel-Based Reinforcement Learning on Representative States
Markov decision processes (MDPs) are an established framework for solving sequential decision-making problems under uncertainty. In this work, we propose a new method for batchmod...
Branislav Kveton, Georgios Theocharous
AAAI
2012
13 years 9 months ago
Manifold Warping: Manifold Alignment over Time
Knowledge transfer is computationally challenging, due in part to the curse of dimensionality, compounded by source and target domains expressed using different features (e.g., do...
Hoa Trong Vu, Clifton Carey, Sridhar Mahadevan