Learning Gaussian processes from multiple tasks

By Kai Yu, Volker Tresp, Anton Schwaighofer. 2005

441 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods, Meta & Continual Learning, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Probabilistic & Bayesian Methods

In Meta & Continual Learning

In Signal Processing

Citations in the corpus

A Survey on Multi-Task Learning (2017, methodology): “When r equals 1, this model degenerates to a case where model parameters of different tasks share a prior, which is similar to several Bayesian MTL models such as [56], [57], [58] that are based on Gaussian processes and t processes.”
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes (2018, background): “Hierarchical Bayesian models have a long history of use in both transfer learning and domain adaptation (e.g., Lawrence & Platt, 2004; Yu et al., 2005; Gao et al., 2008; Daumé III, 2009; Wan et al., 2012).”
Probabilistic Model-Agnostic Meta-Learning (2018, background): “Hierarchical Bayesian models are a long-standing approach for few-shot learning that naturally allow for the ability to reason about uncertainty over functions [39, 7, 25, 43, 12, 4, 41].”
Learning Task Grouping and Overlap in Multi-task Learning (2012, background): “Assumptions that task parameters lie close to each other in some geometric sense (Evgeniou & Pontil, 2004), or parameters share a common prior (Yu et al., 2005; Lee et al., 2007; Daum´e III, 2009), or they lie in a low dimensional subspace (Argyriou et al., 2008a) or on a mani-Appearing in…”
Bayesian Multitask Learning with Latent Hierarchies (2009, methodology): “The key idea in the linear multitask model [16] is to model feature covariance; this is also the intuition behind the informative priors model [13], carried out in a more Bayesian framework.”
Surpassing Human-Level Face Verification Performance on LFW with GaussianFace (2014, background): “However, most of them [60, 11, 6, 44, 25, 49, 61] have only considered the symmetric multi-task learning, which means that all tasks have been assumed to be of equal importance, whereas our purpose is to enhance performance on a target task given all other source tasks.”
Multi-Stage Multi-Task Feature Learning (2012, neutral): “…learning is that all tasks should share some common structures including the prior or parameters of Bayesian models (Schwaighofer et al., 2005; Yu et al., 2005; Zhang et al., 2006), a similarity metric matrix (Parameswaran and Weinberger, 2010), a classification weight vector (Evgeniou and…”
Learning Multiple Tasks using Manifold Regularization (2010, background): “This notion of relatedness is usually incorporated in the form of a regularizer [4] [16] [13] or a prior [15] [22] [21].”
Multitask Learning without Label Correspondences (2010, background): “Task relatedness can also be modeled as learning functions that are close to each other in some sense [3, 9].”
Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning (2019, background): “Several papers analyze multi-task learning in the context of Gaussian processes (Schwaighofer et al., 2004; Yu et al., 2005; Bonilla et al., 2007), as well as other generative assumptions (HernándezLobato et al.”
Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network (2014, background): “Human pose estimation is a popular research topic in computer vision for its wide potential in many applications, such as video games, gesture control, action understanding, pose retrieval.”
Bayesian Online Multitask Learning of Gaussian Processes (2010, methodology): “Then, the availability of multiple training sets is exploited to learn a better kernel, e.g., using the EM algorithm [23], [24], [25].”
Robust multi-task feature learning (2012, unknown): “Under explicit parameter sharing, all the tasks explicitly share some common parameters; examples include hidden units in neural networks [8, 5], prior in hierarchical Bayesian models [4, 31, 36, 38], parameters of Gaussian process [18], feature mapping matrix [1], classification weight [11] and sim”
An Improved Multi-task Learning Approach with Applications in Medical Diagnosis (2008, methodology): “We validate the proposed collaborative learning approach by comparing it to standard single-task learning (STL) approaches where multiple tasks are tackled independently, and to two commonly-used multi-task learning (MTL) methods, a regularization-based MTL method in [15] and a Bayesian MTL method b”