Multitask generalized eigenvalue program
… To address this issue, we propose the multitask generalized eigenvalue program (MTGEP)
algorithm, which jointly solves K related GEPs. By leveraging knowledge of other GEPs, we …
algorithm, which jointly solves K related GEPs. By leveraging knowledge of other GEPs, we …
Eigenfunction-based multitask learning in a reproducing kernel Hilbert space
… nonparametric multitask learning … eigenvalues and corresponding eigenfunctions of a
predefined integral operator on an RKHS. In our method, we formulate our objective for multitask …
predefined integral operator on an RKHS. In our method, we formulate our objective for multitask …
Multitasking the Davidson algorithm for the large, sparse eigenvalue problem
VM Umar, CF Fischer - The International Journal of …, 1989 - journals.sagepub.com
… to use only matrices of moderate size (mostly N in the range of 200 to 500), since the
programs embedding the eigenvalue problems require a certain amount of memory themselves. …
programs embedding the eigenvalue problems require a certain amount of memory themselves. …
Multitask principal component analysis
… We leverage this issue by casting the PCA into a multitask framework, and doing so, we
show how to solve simultaneously several related PCA problems. Hence, we propose a novel …
show how to solve simultaneously several related PCA problems. Hence, we propose a novel …
Generalization bounds of multitask learning from perspective of vector-valued function learning
… F-related to study the generalizability of multitask classification based on the generalized
Vapnik–Chervonenkis (VC) dimension. Maurer [14] presented the generalization bounds for …
Vapnik–Chervonenkis (VC) dimension. Maurer [14] presented the generalization bounds for …
A regularization approach to learning task relationships in multitask learning
… , our approach—multitask relationship learning (MTRL)—can … symmetric multitask learning
setting and then generalize it to … MTRL and some existing multitask learning methods. …
setting and then generalize it to … MTRL and some existing multitask learning methods. …
Learning to multitask
… multitask models have been proposed. In order to identify an effective multitask model for
a given multitask problem, we propose a learning framework called Learning to MultiTask (…
a given multitask problem, we propose a learning framework called Learning to MultiTask (…
Generalized concomitant multi-task lasso for sparse multimodal regression
… Motivated by our application to the M/EEG inverse problem (see Section 5.3, we present all
the results in a multi-task setting. These results are still valid for single task problems (q = 1), …
the results in a multi-task setting. These results are still valid for single task problems (q = 1), …
A spectral regularization framework for multi-task structure learning
Learning the common structure shared by a set of supervised tasks is an important practical
and theoretical problem. Knowledge of this structure may lead to bet-ter generalization …
and theoretical problem. Knowledge of this structure may lead to bet-ter generalization …
Algorithm-dependent generalization bounds for multi-task learning
Often, tasks are collected for multi-task learning (MTL) because they share similar feature
structures. Based on this observation, in this paper, we present novel algorithm-dependent …
structures. Based on this observation, in this paper, we present novel algorithm-dependent …