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» Generalization Bounds for Some Ordinal Regression Algorithms
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NIPS
2007
13 years 8 months ago
Multi-Task Learning via Conic Programming
When we have several related tasks, solving them simultaneously is shown to be more effective than solving them individually. This approach is called multi-task learning (MTL) and...
Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, ...
KDD
2004
ACM
132views Data Mining» more  KDD 2004»
14 years 7 months ago
Privacy preserving regression modelling via distributed computation
Reluctance of data owners to share their possibly confidential or proprietary data with others who own related databases is a serious impediment to conducting a mutually beneficia...
Ashish P. Sanil, Alan F. Karr, Xiaodong Lin, Jerom...
IPL
2008
79views more  IPL 2008»
13 years 7 months ago
Ski rental with two general options
We define and solve a simple extension of the ski-rental problem [4]. In the classical version, the algorithm needs to decide when to switch from renting to buying. In our version...
Zvi Lotker, Boaz Patt-Shamir, Dror Rawitz
CONSTRAINTS
2010
126views more  CONSTRAINTS 2010»
13 years 7 months ago
Lexicographically-ordered constraint satisfaction problems
Abstract. We describe a simple CSP formalism for handling multi-attribute preference problems with hard constraints, one that combines hard constraints and preferences so the two a...
Eugene C. Freuder, Robert Heffernan, Richard J. Wa...
NIPS
2008
13 years 8 months ago
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization
This work characterizes the generalization ability of algorithms whose predictions are linear in the input vector. To this end, we provide sharp bounds for Rademacher and Gaussian...
Sham M. Kakade, Karthik Sridharan, Ambuj Tewari