Linear Algorithms for Online Multitask Classification

By Giovanni Cavallanti, Nicolò Cesa-Bianchi, Claudio Gentile. 2010

201 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Management Science and Operations Research, Optimization & Training

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Citations in the corpus

An Overview of Multi-task Learning (2018, methodology): “In[77],onlineMTLalgorithms,which adopt perceptrons as a basic model and measure task relations based on shared geometric structures REVIEW among tasks, are proposed for multi-task classification problems.”
A Survey on Multi-Task Learning (2017, methodology): “Moreover, online multi-task learning [137], [138], [139], [140], [141], [142] is also capable of handling this situation.”
Model Zoo: A Growing Brain That Learns Continually (2021, background): “There are a number of algorithmic tools to estimate task relatedness, e.g., (Evgeniou et al., 2005; Cavallanti et al., 2010; Kumar and Daume III, 2012), and although such methods are popular in transfer learning (Pentina and Lampert, 2015; Jaakkola and Haussler, 1999), one cannot apply them in…”
Efficient Representations for Lifelong Learning and Autoencoding (2014, background): “[11] considers multi-task learning where explicit known relationships among tasks are exploited for faster learning.”
The Benefit of Multitask Representation Learning (2015, background): “…their applications to speciﬁc problems (see Ando and Zhang, 2005; Argyriou et al., 2008; Baxter, 2000; Ben-David and Schuller, 2003; Caruana, 1998; Cavallanti et al., 2010; Kuzborskij and Orabona, 2013; Maurer et al., 2013; Pentina and Lampert, 2014; Widmer et al., 2013, and references therein).”
Regularization Techniques for Learning with Matrices (2009, background): “Several examples are multi-class categorization (Crammer and Singer, 2000), multi-task and multi-view learning (Cavallanti et al., 2008; Agarwal et al., 2008), and online PCA (Warmuth and Kuzmin, 2006).”
Towards more Reliable Transfer Learning (2018, background): “It is crucial to learn task relationships in multi-task learning problems, either used as priors [5] or learned adaptively [19].”
Adaptive Smoothed Online Multi-Task Learning (2016, methodology): “al [8] assumes that task relationships are available a priori.”
Inducing Crosslingual Distributed Representations of Words (2012, methodology): “KEYWORDS: distributed representations, multilingual learning, direct transfer of annotation.”
Asynchronous Online Federated Learning for Edge Devices (2019, background): “Previous online learning approaches [14, 15, 17] with multiple clients are not capable of solving the aforementioned challenges because these approaches share training samples of each client with other clients; which has the same privacy concern as centralized data centers.”
Online Multi-Task Learning Framework for Ensemble Forecasting (2017, methodology): “5) Linear Algorithms for Online Multi-task Learning(LAOM) [35]: The original method was proposed for multi-task classification.”
A Graphbased Framework for Multi-Task Multi-View Learning (2011, background): “For example, in (Cavallanti et al., 2010), the authors study linear algorithms for online multi-task multi-view learning.”
Provable Guarantees for Gradient-Based Meta-Learning (2019, background): “Online LTL: Learning-to-learn and multi-task learning (MTL) have both been extensively studied in the online setting, although our setting differs significantly from the one usually studied in online MTL (Abernethy et al., 2007; Dekel et al., 2007; Cavallanti et al., 2010).”