We present a method for learning sparse representations shared across multiple tasks. This method is a generalization of thewell-known single-task 1-norm regularization. It is based on a novel non-convex regularizer which controls the number of learnedfeatures common across the tasks. We prove ...
http://scholar.google.com/scholar?q=%222008%22+Convex+Multi-task+Feature+Learning http://dl.acm.org/citation.cfm?id=1455903.1455908&preflayout=flat#citedby Quotes Author Keywords Collaborative Filtering;Inductive Transfer;Kernels;Multi-Task Learning;Regularization;Transfer Learning;Vector-Valued Functions...
First, we extend the method from a 2-task space to an n-task space. This expands the dimensionality of the task interpolation, providing more tasks choice for subsequent convex combination interpolation. Second, in the multi-task space, we randomly select multiple tasks and combine them using ...
Li C, Xie Y, Li Z, Zhu L (2024) Metacl: a semi-supervised meta learning architecture via contrastive learning. Int J Mach Learn Cybern 15(2):227–236 Article MATH Google Scholar Li H, Eigen D, Dodge S, Zeiler M, Wang X (2019) Finding task-relevant features for few-shot learning...
3.4. Multi convex decomposition – Figure 7 Having a learnable pipeline for a single convex object, we can now expand the expressivity of our model by repre- senting generic non-convex objects as compositions of con- vexes [66]. To achieve this task an encoder E outputs a low- 4 34 ...
(2021) involves only convex regularizer. For example, in the application of data hyper-cleaning, one can improve the learning performance by adding a nonsmooth and nonconvex regularizer to push the weights of the clean samples towards 1 while push those of the contaminated samples towards 0 (...
%convexAdam + Hyperparameter Optimisation TMI @article{siebert2024convexadam, title={ConvexAdam: Self-Configuring Dual-Optimisation-Based 3D Multitask Medical Image Registration}, author={Siebert, Hanna and Gro{\ss}br{\"o}hmer, Christoph and Hansen, Lasse and Heinrich, Mattias P}, journal={IEEE...
Decomposition techniques implement the so-called “divide and conquer” in convex optimization problems, being primal and dual decompositions the two classical approaches. Although both solutions achieve the goal of splitting the original program into se
Inverse multiobjective optimization provides a general framework for the unsupervised learning task of inferring parameters of a multiobjective decision making problem (DMP), based on a set of observed decisions from the human expert. However, the performance of this framework relies critically on the...
However, due to the gene expression datasets have the characteristics of small sample size, high dimensionality and high noise, the application of biostatistics and machine learning methods to analyze gene expression data is a challenging task, such as the low reproducibility of important biomarkers ...