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Takeuchi Koh

2 accepted papers

2017

Localized Lasso for High-Dimensional Regression

AISTATS 2017poster

We introduce the localized Lasso, which learns models that both are interpretable and have a high predictive power in problems with high dimensionality d and small sample size n. More specifically, we consider a function defined by local sparse models, one at each data point. We introduce sample-wi…

Cited by 63SourcePDFScholar
2015

Cross-domain recommendation without shared users or items by sharing latent vector distributions

AISTATS 2015poster

We propose a cross-domain recommendation method for predicting the ratings of items in different domains, where neither users nor items are shared across domains. The proposed method is based on matrix factorization, which learns a latent vector for each user and each item. Matrix factorization tech…

Cited by 28SourcePDFScholar