Бірнеше машиналық оқу міндеттерін бірдей шешу

Solving multiple machine learning tasks at the same time
Multi task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task specific models, when compared to training the models separately. Early versions of MTL were called "hints". In a widely cited 1997 paper, Rich Caruana gave the following characterization:<blockquote>Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task can help other tasks be learned better. Multi task learning works because regularization induced by requiring an algorithm to perform well on a related task can be superior to regularization that prevents overfitting by penalizing all complexity uniformly. One situation where MTL may be particularly helpful is if the tasks share significant commonalities and are generally slightly under sampled.

The key challenge in multi task learning, is how to combine learning signals from multiple tasks into a single model. This may strongly depend on how well different task agree with each other, or contradict each other. There are several ways to address this challenge:

Within the MTL paradigm, information can be shared across some or all of the tasks. Depending on the structure of task relatedness, one may want to share information selectively across the tasks. For example, tasks may be grouped or exist in a hierarchy, or be related according to some general metric. Suppose, as developed more formally below, that the parameter vector modeling each task is a linear combination of some underlying basis. Similarity in terms of this basis can indicate the relatedness of the tasks. For example, with sparsity, overlap of nonzero coefficients across tasks indicates commonality. A task grouping then corresponds to those tasks lying in a subspace generated by some subset of basis elements, where tasks in different groups may be disjoint or overlap arbitrarily in terms of their bases. Task relatedness can be imposed a priori or learned from the data. Hierarchical task relatedness can also be exploited implicitly without assuming a priori knowledge or learning relations explicitly. For example, the explicit learning of sample relevance across tasks can be done to guarantee the effectiveness of joint learning across multiple domains. an image based object classifier, can develop robust representations which may be useful to further algorithms learning related tasks. For example, the pre trained model can be used as a feature extractor to perform pre processing for another learning algorithm. Or the pre trained model can be used to initialize a model with similar architecture which is then fine tuned to learn a different classification task.

Traditionally Multi task learning and transfer of knowledge are applied to stationary learning settings. Their extension to non stationary environments is termed Group online adaptive learning (GOAL). Sharing information could be particularly useful if learners operate in continuously changing environments, because a learner could benefit from previous experience of another learner to quickly adapt to their new environment. Such group adaptive learning has numerous applications, from predicting financial time series, through content recommendation systems, to visual understanding for adaptive autonomous agents.

Multitask optimization: In some cases, the simultaneous training of seemingly related tasks may hinder performance compared to single task models. Commonly, MTL models employ task specific modules on top of a joint feature representation obtained using a shared module. Since this joint representation must capture useful features across all tasks, MTL may hinder individual task performance if the different tasks seek conflicting representation, i. e., the gradients of different tasks point to opposing directions or differ significantly in magnitude. This phenomenon is commonly referred to as negative transfer. To mitigate this issue, various MTL optimization methods have been proposed. Commonly, the per task gradients are combined into a joint update direction through various aggregation algorithms or heuristics. These methods include subtracting the projection of conflicted gradients, applying techniques from game theory, and using Bayesian modeling to get a distribution over gradients.

The MTL problem can be cast within the context of RKHSvv (a complete inner product space of vector valued functions equipped with a reproducing kernel). In particular, recent focus has been on cases where task structure can be identified via a separable kernel, described below. The presentation here derives from Ciliberto et al., 2015. suggested setting F as the Frobenius norm They optimized directly using block coordinate descent, not accounting for difficulties at the boundary of
Clustered tasks learning Jacob et al suggested to learn A in the setting where T tasks are organized in R disjoint clusters. In this case let be the matrix with Setting , and , the task matrix can be parameterized as a function of : , with terms that penalize the average, between clusters variance and within clusters variance respectively of the task predictions. M is not convex, but there is a convex relaxation In this formulation, .

Non convex penalties Penalties can be constructed such that A is constrained to be a graph Laplacian, or that A has low rank factorization. However these penalties are not convex, and the analysis of the barrier method proposed by Ciliberto et al. does not go through in these cases. Non separable kernels Separable kernels are limited, in particular they do not account for structures in the interaction space between the input and output domains jointly. Future work is needed to develop models for these kernels.

A Matlab package called Multi Task Learning via StructurAl Regularization (MALSAR) implements the following multi task learning algorithms: Mean Regularized Multi Task Learning, Multi Task Learning with Joint Feature Selection, Robust Multi Task Feature Learning, Trace Norm Regularized Multi Task Learning, Alternating Structural Optimization, Incoherent Low Rank and Sparse Learning, Robust Low Rank Multi Task Learning, Clustered Multi Task Learning, Multi Task Learning with Graph Structures.