Bounds for linear multi-task learning

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August undefined, 2024

WebMulti-Task Reinforcement Learning with Context-based Representations Shagun Sodhani1 Amy Zhang 1 2 3Joelle Pineau Abstract The beneﬁt of multi-task learning over single … http://www.sciweavers.org/publications/bounds-linear-multi-task-learning

Boosted multi-task learning SpringerLink

Weba generative model of the source task, a linear approxima-tion of the value function in [12], or a discrete state space in [14]. These approaches do not consider the exploration … WebJun 16, 2024 · a related multi-task learning setting in sparse linear regression. (11) derives an information theoretic ... then discuss the minimax approach to deriving transfer learning lower bounds. 2.1 ... christian medrano

Conic Multi-task Classification SpringerLink

WebAbstract. We give dimension-free and data-dependent bounds for lin-ear multi-task learning where a common linear operator is chosen to preprocess data for a vector of task speci–c linear-thresholding classi-–ers. The complexity penalty of multi-task learning is bounded by a simple expression involving the margins of the task-speci–c ... WebWe give dimension-free and data-dependent bounds for linear multi-task learning where a common linear operator is chosen to preprocess data for a vector of task specific … WebWe give dimension-free and data-dependent bounds for linear multi-task learning where a common linear operator is chosen to preprocess data for a vector of task specific … christian medley lyrics

Bounds for Linear Multi-Task Learning The Journal of …

Bounds for Linear Multi-Task Learning Sciweavers

Webdevelop multi-task learning methods and theory as an extension of widely usedkernel learning methods developed within SLT or Regularization Theory, such as SVM and RN. We show that using a particular type of kernels, the regularized multi-task learning method we propose is equivalent to a single-task learning one when such a multi-task kernel ... WebMulti-Task Learning Multi-task learning (MTL) is a method to jointly learn shared representations from mul-tiple training tasks (Caruana,1997). Past research on MTL ... space learning with 2-layer linear models, and shows the upper bounds of their sample complexity are of the same order. In contrast, our theory is compatible with non-linear ... christian medleys with chordsWebBounds for Linear Multi-Task Learning. Abstract. We give dimension-free and data-dependent bounds for linear multi-task learning where a common linear operator is … christian medom

"WebDec 24, 2010 · Here, multi-task learning is particularly helpful as data sets from different countries vary largely in size because of the cost of editorial judgments. Further, the proposed method obtains state-of-the-art results on a publicly available multi-task dataset. ... Maurer, A. (2006). Bounds for linear multi-task learning. Journal of Machine ... " - Bounds for linear multi-task learning

Bounds for linear multi-task learning

Abstract 1. Introduction arXiv:2106.09017v1 [cs.LG] 16 Jun 2024

Webmulti-task learning is preferable to independent learning. Following the seminal work of Baxter(2000) several authors have given performance bounds under di erent assumptions of task-relatedness. In this paper we consider multi-task learning with trace-norm regu-larization (TNML), a technique for which e cient algorithms exist and which has been WebMar 25, 2009 · The bound is dimension free, justifies optimization of the pre-processing feature-map and explains the circumstances under which learning-to-learn is preferable …

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WebCiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract. We give dimension-free and data-dependent bounds for linear multi-task learning where a common linear operator is chosen to preprocess data for a vector of task speci…c linear-thresholding classi-…ers. The complexity penalty of multi-task learning is bounded by a … WebBounds for Linear Multi-Task Learning Andreas Maurer Adalbertstr. 55 D-80799 München [email protected] Abstract. We give dimension-free and data …

WebMaurer, A.: Bounds for linear multi-task learning. Journal of Machine Learning Research 7, 117–139 (2006) MATH Google Scholar Kakade, S.M., Shalev-Shwartz, S., Tewari, A.: Regularization techniques for learning with matrices. Journal of Machine Learning Research 13, 1865–1890 (2012) WebMay 23, 2015 · We focus on the multi-task linear representation learning setting [35], which has become popular in recent years as it is an expressive but tractable nonconvex setting for studying the sample ...

WebBounds for Linear Multi-Task Learning . Andreas Maurer; 7(5):117−139, 2006. Abstract. We give dimension-free and data-dependent bounds for linear multi-task learning … Webposed for multi-task learning (Figure 1 (left)), there are very few studies on how the learning bounds change under dif-ferent parameter regularizations. In this paper, we analyze the stability bounds under a general framework of multi-task learning using kernel ridge regression. Our formulation

WebSep 21, 2016 · There are situations when it is desirable to extend this result to the case when the class \(\mathcal {F}\) consists of vector-valued functions and the loss functions are Lipschitz functions defined on a more than one-dimensional space. Such occurs for example in the analysis of multi-class learning, K-means clustering or learning-to-learn.At …

WebCiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We give dimension-free and data-dependent bounds for linear multi-task learning where a common linear operator is chosen to preprocess data for a vector of task specific linear-thresholding classifiers. christian meehan easton paWebMulti-Task Learning Multi-task learning (MTL) is a method to jointly learn shared representations from mul-tiple training tasks (Caruana,1997). Past research on MTL is … christian meetups glasgowWebKeywords: learning to learn, transfer learning, multi-task learning 1. Introduction Simultaneous learning of different tasks under some common constraint, often called … christian meetup groupsWebBounds for Linear Multi-Task Learning . Andreas Maurer; 7(5):117−139, 2006. Abstract. We give dimension-free and data-dependent bounds for linear multi-task learning where a common linear operator is chosen to preprocess data for a vector of task specific linear-thresholding classifiers. The complexity penalty of multi-task learning is ... christian mehlhornWebMar 2, 2024 · In order to generalize LS-SVM from single-task to multi-task learning, inspired by the regularized multi-task learning (RMTL), this study proposes a novel multi-task learning approach, multi-task ... christian meeting placeWebJan 1, 2006 · Bounds for Linear Multi-Task Learning. January 2006 Authors: Andreas Maurer Abstract We give dimension-free and data-dependent bounds for linear multi … georgia michigan playoff gameWebin multi-task learning. These empirical results match our theoretical bounds, and corroborate the power of representation learning. 7 Conclusion and Future Work In this paper, we investigate representation learning for … christian mehl