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1. Full Text Transfer learning for reinforcement learning domains: A survey
by Taylor, Matthew E and Stone, Peter
Journal of Machine Learning Research, ISSN 1532-4435, 07/2009, Volume 10, pp. 1633 - 1685
The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples,... 
Transfer learning | Multi-task learning | Reinforcement learning | transfer learning | multi-task learning | FRAMEWORK | COMPOSING SOLUTIONS | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | reinforcement learning
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2. Full Text Convex multi-task feature learning
by Argyriou, Andreas and Evgeniou, Theodoros and Pontil, Massimiliano
Machine Learning, ISSN 0885-6125, 12/2008, Volume 73, Issue 3, pp. 243 - 272
We present a method for learning sparse representations shared across multiple tasks. This method is a generalization of the well-known single-task 1-norm... 
Transfer learning | Simulation and Modeling | Computing Methodologies | Multi-task learning | Kernels | Collaborative filtering | Automation and Robotics | Computer Science | Artificial Intelligence (incl. Robotics) | Language Translation and Linguistics | Regularization | Inductive transfer | Vector-valued functions | LINEAR-REGRESSION | MULTIPLE TASKS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Computer science | Algorithms | Management science | Management | Information technology | Mathematical optimization | Artificial intelligence
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3. Full Text Learning without Forgetting
by Li, Zhizhong and Hoiem, Derek
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 12/2018, Volume 40, Issue 12, pp. 2935 - 2947
When building a unified vision system or gradually adding new apabilities to a system, the usual assumption is that training data for all tasks is always... 
Deep learning | Knowledge engineering | Learning systems | transfer learning | multi-task learning | Neural networks | Training data | Feature extraction | visual recognition | Convolutional neural networks | Visual perception | deep learning | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Learning | Training | Artificial neural networks | Retraining | Vision systems | Tuning
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4. Full Text Learning from multiple sources
by Crammer, Koby and Kearns, Michael and Wortman, Jennifer
Journal of Machine Learning Research, ISSN 1532-4435, 08/2008, Volume 9, pp. 1757 - 1774
We consider the problem of learning accurate models from multiple sources of "nearby" data. Given distinct samples from multiple data sources and estimates of... 
Error bounds | Multi-task learning | multi-task learning | MODEL | error bounds | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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5. Full Text Flexible Clustered Multi-Task Learning by Learning Representative Tasks
by Qiang Zhou and Qi Zhao
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 02/2016, Volume 38, Issue 2, pp. 266 - 278
Multi-task learning (MTL) methods have shown promising performance by learning multiple relevant tasks simultaneously, which exploits to share useful... 
Learning systems | Visualization | Clustered Multi-Task Learning | Training data | Robustness | Covariance matrices | Kernel | Optimization | Representative Task | Group Sparsity | REGRESSION | Clustered multi-task learning | ALGORITHM | representative task | MULTIPLE TASKS | group sparsity | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Robust statistics | Mathematical optimization | Usage
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6. Full Text Robust Visual Tracking via Structured Multi-Task Sparse Learning
by Zhang, Tianzhu and Zhang, Tianzhu and Ghanem, Bernard and Ghanem, Bernard and Liu, Si and Liu, Si and Ahuja, Narendra and Ahuja, Narendra
International Journal of Computer Vision, ISSN 0920-5691, 1/2013, Volume 101, Issue 2, pp. 367 - 383
In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured... 
Pattern Recognition | Visual tracking | Graph | Computer Science | Computer Imaging, Vision, Pattern Recognition and Graphics | Image Processing and Computer Vision | Artificial Intelligence (incl. Robotics) | Sparse representation | Structure | Multi-task learning | Particle filter | MODELS | EIGENTRACKING | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | OBJECT TRACKING | Studies | Graph theory | Tracking control systems | Vision systems | Analysis
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7. Full Text Modeling disease progression via multi-task learning
by Zhou, Jiayu and Liu, Jun and Narayan, Vaibhav A and Ye, Jieping and Alzheimer's Dis Neuroimaging Initi and Alzheimer's Disease Neuroimaging Initiative
NeuroImage, ISSN 1053-8119, 09/2013, Volume 78, pp. 233 - 248
Alzheimer's disease (AD), the most common type of dementia, is a severe neurodegenerative disorder. Identifying biomarkers that can track the progress of the... 
Fused Lasso | ADAS-Cog | Alzheimer's disease | Multi-task learning | MMSE | Disease progression | REGRESSION | DIAGNOSIS | BASE-LINE | ATROPHY | MRI | ALZHEIMERS-DISEASE | CSF BIOMARKERS | MILD COGNITIVE IMPAIRMENT | NEUROSCIENCES | NEUROIMAGING | PREDICTION | SELECTION | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Magnetic Resonance Imaging | Regression Analysis | Algorithms | Artificial Intelligence | Humans | Female | Male | Aged | Disease Progression | Alzheimer Disease - pathology | Decision-making | Analysis | Amyloid beta-protein | Diagnostic imaging | Multitasking (Human behavior) | Medical imaging | Nuclear magnetic resonance--NMR | Mean square errors | Education | Cognitive ability | Biomarkers | Alzheimers disease | Age | Risk factors
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8. Full Text An efficient active learning method for multi-task learning
by Xiao, Yanshan and Chang, Zheng and Liu, Bo
Knowledge-Based Systems, ISSN 0950-7051, 10/2019, p. 105137
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9. Full Text Group online adaptive learning
by Zweig, Alon and Chechik, Gal
Machine Learning, ISSN 0885-6125, 10/2017, Volume 106, Issue 9, pp. 1747 - 1770
Sharing information among multiple learning agents can accelerate learning. It could be particularly useful if learners operate in continuously changing... 
Adaptive learning | Control, Robotics, Mechatronics | Computer Science | Artificial Intelligence (incl. Robotics) | Domain adaptation | Computing Methodologies | Simulation and Modeling | Language Translation and Linguistics | Multi-task learning | Online learning | Knowledge transfer | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Algorithms | Multitasking (Human behavior) | Analysis | Intelligent tutoring systems | Adaptive systems | Visual tasks | Navigation | Distance learning | Information sharing | Editors | Adaptive algorithms | Machine learning | Changing environments | Artificial intelligence | Recommender systems
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10. Full Text Local rademacher complexity-based learning guarantees for multi-task learning
by Yousefi, Niloofar and Lei, Yunwen and Kloft, Marius and Mollaghasemi, Mansooreh and Anagnostopoulos, Georgios C
Journal of Machine Learning Research, ISSN 1532-4435, 08/2018, Volume 19
We show a Talagrand-type concentration inequality for Multi-Task Learning (MTL), with which we establish sharp excess risk bounds for MTL in terms of the Local... 
Local Rademacher Complexity | Multi-task Learning | Excess Risk Bounds | INEQUALITIES | BOUNDS | ALGORITHM | RISK | ERROR | MODEL | MULTIPLE TASKS | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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11. Full Text Learning multiple tasks with kernel methods
by Evgeniou, Theodores and Micchelli, Charles A and Pontil, Massimiliano
Journal of Machine Learning Research, ISSN 1533-7928, 2005, Volume 6, pp. 615 - 637
We study the problem of learning many related tasks simultaneously using kernel methods and regularization. The standard single-task kernel methods, such as... 
Kernels | Learning algorithms | Multi-task learning | Regularization | Vector-valued functions | multi-task learning | vector-valued functions | learning algorithms | BIAS | regularization | kernels | MODEL | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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12. Full Text Distributed Clustering and Learning Over Networks
by Xiaochuan Zhao and Sayed, Ali H
IEEE Transactions on Signal Processing, ISSN 1053-587X, 07/2015, Volume 63, Issue 13, pp. 3285 - 3300
Distributed processing over networks relies on in-network processing and cooperation among neighboring agents. Cooperation is beneficial when agents share a... 
distributed optimization | Heuristic algorithms | Noise | multi-task networks | Vectors | Topology | Indexes | unsupervised learning | Adaptive networks | Network topology | Clustering algorithms | consensus adaptation | distributed learning | clustering | diffusion adaptation | APPROXIMATION | CONSENSUS | ALGORITHMS | SENSOR NETWORKS | STRATEGIES | ENGINEERING, ELECTRICAL & ELECTRONIC | SUBGRADIENT METHODS | OPTIMIZATION | QUADRATIC-FORMS | Network architecture | Usage | Distribution (Probability theory) | Research | Vector spaces | Analysis | Cooperation | Learning | Networks | Error analysis | Decay | Clusters | Transaction processing | Clustering
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13. Full Text Boosted multi-task learning
by Chapelle, Olivier and Chapelle, Olivier and Shivaswamy, Pannagadatta and Shivaswamy, Pannagadatta and Vadrevu, Srinivas and Vadrevu, Srinivas and Weinberger, Kilian and Weinberger, Kilian and Zhang, Ya and Zhang, Ya and Tseng, Belle and Tseng, Belle
Machine Learning, ISSN 0885-6125, 10/2011, Volume 85, Issue 1, pp. 149 - 173
In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model,... 
Ranking | Control, Robotics, Mechatronics | Computer Science | Artificial Intelligence (incl. Robotics) | Computing Methodologies | Simulation and Modeling | Boosting | Decision trees | Language Translation and Linguistics | Multi-task learning | Web search | GRADIENT DESCENT | ALGORITHMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Computer science | Online searching | Internet/Web search services | Algorithms | Database searching | Analysis | Multitasking (Human behavior) | World Wide Web
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14. Full Text Event Oriented Dictionary Learning for Complex Event Detection
by Yan Yan and Yi Yang and Deyu Meng and Gaowen Liu and Wei Tong and Hauptmann, Alexander G and Sebe, Nicu
IEEE Transactions on Image Processing, ISSN 1057-7149, 06/2015, Volume 24, Issue 6, pp. 1867 - 1878
Complex event detection is a retrieval task with the goal of finding videos of a particular event in a large-scale unconstrained Internet video archive, given... 
Silicon compounds | Concept Selection | Visualization | Complex Event Detection | Dictionaries | Event detection | Semantics | Feature extraction | Supervised Multi-task Dictionary Learning | Event Oriented Dictionary Learning | Videos | FUSION | Complex event detection | event oriented dictionary learning | ALGORITHM | SPARSE | supervised multi-task dictionary learning | concept selection | SELECTION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Usage | Research | Image processing | Indexes | Visualization (Computers)
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15. Full Text Algorithm-Dependent Generalization Bounds for Multi-Task Learning
by Tongliang Liu and Dacheng Tao and Mingli Song and Maybank, Stephen J
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 02/2017, Volume 39, Issue 2, pp. 227 - 241
Often, tasks are collected for multi-task learning (MTL) because they share similar feature structures. Based on this observation, in this paper, we present... 
Algorithm design and analysis | regularization | Stability analysis | Complexity theory | Electronic mail | Multi-task learning | Convergence | Training | inductive bias | generalization | Prediction algorithms | learning theory | stability | learning to learn | Multi-Task learning | COVERING NUMBERS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Domains | Algorithms | Parameters | Bias | Machine learning
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16. Full Text Generalized hierarchical kernel learning
by Jawanpuria, Pratik and Nath, Jagarlapudi Saketha and Ramakrishnan, Ganesh
Journal of Machine Learning Research, ISSN 1532-4435, 03/2015, Volume 16, pp. 617 - 652
This paper generalizes the framework of Hierarchical Kernel Learning (HKL) and illustrates its utility in the domain of rule learning. HKL involves Multiple... 
Multiple kernel learning | Mixed-norm regularization | Active set method | Multi-task learning | Rule ensemble learning | REGRESSION | mixed-norm regularization | rule ensemble learning | MODEL | MULTIPLE TASKS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | multi-task learning | multiple kernel learning | active set method | FRAMEWORK | OPTIMIZATION | SELECTION | AUTOMATION & CONTROL SYSTEMS
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17. Full Text Regularization techniques for learning with matrices
by Kakade, Sham M and Shalev-Shwartz, Shai and Tewari, Ambuj
Journal of Machine Learning Research, ISSN 1532-4435, 06/2012, Volume 13, pp. 1865 - 1890
There is growing body of learning problems for which it is natural to organize the parameters into a matrix. As a result, it becomes easy to impose... 
Multiple kernel learning | Strong convexity | Multi-class learning | Generalization bounds | Regret bounds | Multi-task learning | Regularization | REGRESSION | multi-task learning | multi-class learning | multiple kernel learning | BOUNDS | regularization | generalization bounds | strong convexity | regret bounds | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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18. Full Text A theory of transfer learning with applications to active learning
by Yang, Liu and Yang, Liu and Hanneke, Steve and Hanneke, Steve and Carbonell, Jaime and Carbonell, Jaime
Machine Learning, ISSN 0885-6125, 2/2013, Volume 90, Issue 2, pp. 161 - 189
We explore a transfer learning setting, in which a finite sequence of target concepts are sampled independently with an unknown distribution from a known... 
Control, Robotics, Mechatronics | Transfer learning | Active learning | Computer Science | Bayesian learning | Artificial Intelligence (incl. Robotics) | Computing Methodologies | Simulation and Modeling | Statistical learning theory | Sample complexity | Language Translation and Linguistics | Multi-task learning | RATES | CONVERGENCE | MODEL | MULTIPLE TASKS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Multitasking (Human behavior) | Computer science | Artificial intelligence | Bayesian analysis | Statistics | Learning | Tasks | Asymptotic properties | Mathematical analysis | Focusing | Machine learning
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19. Full Text Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach
by Han, Hu and Jain, Anil K and Wang, Fang and Shan, Shiguang and Chen, Xilin
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 11/2018, Volume 40, Issue 11, pp. 2597 - 2609
Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed... 
Support vector machines | Hair | multi-task learning | Correlation | Databases | Face recognition | attribute correlation | Estimation | Predictive models | Face | attribute heterogeneity | heterogeneous attribute estimation | AGE ESTIMATION | GENDER | CLASSIFICATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Learning | Heterogeneity | Digital media | Demographics | Neural networks | Artificial neural networks
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20. Full Text Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition
by An-An Liu and Yu-Ting Su and Wei-Zhi Nie and Kankanhalli, Mohan
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 01/2017, Volume 39, Issue 1, pp. 102 - 114
This paper proposes a hierarchical clustering multi-task learning (HC-MTL) method for joint human action grouping and recognition. Specifically, we formulate... 
Learning systems | Legged locomotion | Action recognition | multi-task learning | Clustering methods | Social network services | task grouping | task relatedness measure | Linear programming | Data models | Indexes | TASKS | CONTEXT | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Learning | Algorithms | Artificial Intelligence | Humans | Least-Squares Analysis | Pattern Recognition, Automated | Machine Learning | Task Performance and Analysis | Cluster Analysis | Databases, Factual | Research | Clustering (Computers) | Human motion | Clustering | Moving object recognition | Groups | Feature recognition | Machine learning
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