Multi-task learning for HIV therapy screening

By Steffen Bickel, Jasmina Bogojeska, Thomas Lengauer, Tobias Scheffer. 2008

212 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

A Survey on Multi-Task Learning (2017, methodology): “A representative instance-based method is the multi-task distribution matching method proposed in [8], which first estimates density ratios between probabilities that each instance as well as its label belongs to both its own task and a mixture of all the tasks and then uses all the weighted trainin”
Learning in Implicit Generative Models (2016, background): “Finally, while we have focussed on the two sample hypothesis test, we believ e it could be advantageous to extend this reasoning to the case of multiple testing, where we simu ltaneously test several sets of data [2].”
Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study (2010, background): “Recently, [32] presented a multi-task learning approach to”
Transfer across Completely Different Feature Spaces via Spectral Embedding (2013, background): “…sets are heterogeneous because: 1) different feature spaces and distributions: one has text like features, while the other is microarray expression with numerical features; 2) different output spaces: one is labeled by the category of bacteria, while the other is labeled by the category of yeast.”
Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation (2014, background): “Multi-task learning is particularly useful when one has many related learning tasks to solve but only few training samples are available for each task, which is often the case in many real-world problems such as therapy screening [Bickel et al., 2008] and face verification [Wang et al.”
Domain Adaptation by Joint Distribution Invariant Projections (2020, background): “Similar design of such function form for r(z, y; θ) can also be found in [59].”
Feature-based transfer learning with real-world applications (2010, background): “, [219, 107, 49, 48, 87, 82, 21, 180, 67, 20, 148, 22, 147] for example), which assumes that certain parts of the data in the source domain can be reused for learning in the target domain by re-weighting.”
Transfer Learning by Distribution Matching for Targeted Advertising (2008, methodology): “Similar to the idea of learning under marginal shift by weighting the training examples, [8] devise a method for learning under joint shift of covariates and labels over multiple tasks that is based on instancespecific rescaling factors.”
Clustered Multi-Task Learning Via Alternating Structure Optimization (2011, unknown): “MTL has been applied in many appl ications including biomedical informatics [7], marketing [1], natural language processing [2] , and computer vision [3].”
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning (2016, background): “Some recent applications include computational genetics [63], image segmentation [1], HIV therapy screening [14], collaborative ﬁltering [18], age estimation from facial images [67], and sub-cellular location prediction [64], just to name a few prominent ones.”
$\ell_{p}-\ell_{q}$ Penalty for Sparse Linear and Sparse Multiple Kernel Multitask Learning (2011, background): “Application domains that have been shown to benefit from MTL are medical diagnosis [5], drug therapy prediction [ 6 ], vaccine design [22], and conjoint analysis [1].”
Online Multi-Task Learning Framework for Ensemble Forecasting (2017, unknown): “Multi-task learning has been successfully used to solve multiple related learning problems in many applications, including computer vision [6], [7], healthcare [8], [9], recommender systems [10], [11], natural language processing [12] and genomics [13].”
A Survey of Transfer and Multitask Learning in Bioinformatics (2011, background): “[50] studied the problem of predicting HIV therapy outcomes of different drug combinations based on observed genetic properties of the patients, where each task corresponds to a particular drug combination.”
Active Multi-task Learning via Bandits (2015, background): “For example, for predicting the therapeutic success of a given combination of drugs for a given strain of the HIV-1 [12], they designed a multi-task learning model to handle arbitrarily different data distributions for different tasks.”