[{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ieee":"A. Pentina and C. Lampert, “Multi-task learning with labeled and unlabeled tasks,” presented at the ICML: International Conference on Machine Learning, Sydney, Australia, 2017, vol. 70, pp. 2807–2816.","short":"A. Pentina, C. Lampert, in:, ML Research Press, 2017, pp. 2807–2816.","ama":"Pentina A, Lampert C. Multi-task learning with labeled and unlabeled tasks. In: Vol 70. ML Research Press; 2017:2807-2816.","apa":"Pentina, A., &#38; Lampert, C. (2017). Multi-task learning with labeled and unlabeled tasks (Vol. 70, pp. 2807–2816). Presented at the ICML: International Conference on Machine Learning, Sydney, Australia: ML Research Press.","mla":"Pentina, Anastasia, and Christoph Lampert. <i>Multi-Task Learning with Labeled and Unlabeled Tasks</i>. Vol. 70, ML Research Press, 2017, pp. 2807–16.","chicago":"Pentina, Anastasia, and Christoph Lampert. “Multi-Task Learning with Labeled and Unlabeled Tasks,” 70:2807–16. ML Research Press, 2017.","ista":"Pentina A, Lampert C. 2017. Multi-task learning with labeled and unlabeled tasks. ICML: International Conference on Machine Learning, PMLR, vol. 70, 2807–2816."},"language":[{"iso":"eng"}],"oa":1,"department":[{"_id":"ChLa"}],"month":"06","publication_status":"published","publication_identifier":{"isbn":["9781510855144"]},"abstract":[{"text":"In multi-task learning, a learner is given a collection of prediction tasks and needs to solve all of them. In contrast to previous work, which required that annotated training data must be available for all tasks, we consider a new setting, in which for some tasks, potentially most of them, only unlabeled training data is provided. Consequently, to solve all tasks, information must be transferred between tasks with labels and tasks without labels. Focusing on an instance-based transfer method we analyze two variants of this setting: when the set of labeled tasks is fixed, and when it can be actively selected by the learner. We state and prove a generalization bound that covers both scenarios and derive from it an algorithm for making the choice of labeled tasks (in the active case) and for transferring information between the tasks in a principled way. We also illustrate the effectiveness of the algorithm on synthetic and real data. ","lang":"eng"}],"intvolume":"        70","date_created":"2018-12-11T11:49:37Z","volume":70,"oa_version":"Submitted Version","title":"Multi-task learning with labeled and unlabeled tasks","scopus_import":"1","day":"08","author":[{"full_name":"Pentina, Anastasia","id":"42E87FC6-F248-11E8-B48F-1D18A9856A87","last_name":"Pentina","first_name":"Anastasia"},{"last_name":"Lampert","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","first_name":"Christoph"}],"date_published":"2017-06-08T00:00:00Z","conference":{"location":"Sydney, Australia","end_date":"2017-08-11","start_date":"2017-08-06","name":"ICML: International Conference on Machine Learning"},"ec_funded":1,"status":"public","project":[{"_id":"2532554C-B435-11E9-9278-68D0E5697425","grant_number":"308036","name":"Lifelong Learning of Visual Scene Understanding","call_identifier":"FP7"}],"publist_id":"6399","external_id":{"isi":["000683309502093"]},"year":"2017","isi":1,"quality_controlled":"1","main_file_link":[{"url":"https://arxiv.org/abs/1602.06518","open_access":"1"}],"page":"2807 - 2816","type":"conference","_id":"999","date_updated":"2023-10-17T11:53:32Z","publisher":"ML Research Press","article_processing_charge":"No","alternative_title":["PMLR"]},{"file_date_updated":"2018-12-12T10:12:43Z","publication_status":"published","abstract":[{"text":"Better understanding of the potential benefits of information transfer and representation learning is an important step towards the goal of building intelligent systems that are able to persist in the world and learn over time. In this work, we consider a setting where the learner encounters a stream of tasks but is able to retain only limited information from each encountered task, such as a learned predictor. In contrast to most previous works analyzing this scenario, we do not make any distributional assumptions on the task generating process. Instead, we formulate a complexity measure that captures the diversity of the observed tasks. We provide a lifelong learning algorithm with error guarantees for every observed task (rather than on average). We show sample complexity reductions in comparison to solving every task in isolation in terms of our task complexity measure. Further, our algorithmic framework can naturally be viewed as learning a representation from encountered tasks with a neural network.","lang":"eng"}],"intvolume":"        29","has_accepted_license":"1","date_created":"2018-12-11T11:50:08Z","volume":29,"oa_version":"Published Version","title":"Lifelong learning with weighted majority votes","scopus_import":1,"day":"01","author":[{"last_name":"Pentina","full_name":"Pentina, Anastasia","id":"42E87FC6-F248-11E8-B48F-1D18A9856A87","first_name":"Anastasia"},{"full_name":"Urner, Ruth","last_name":"Urner","first_name":"Ruth"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"short":"A. Pentina, R. Urner, in:, Neural Information Processing Systems, 2016, pp. 3619–3627.","ieee":"A. Pentina and R. Urner, “Lifelong learning with weighted majority votes,” presented at the NIPS: Neural Information Processing Systems, Barcelona, Spain, 2016, vol. 29, pp. 3619–3627.","ama":"Pentina A, Urner R. Lifelong learning with weighted majority votes. In: Vol 29. Neural Information Processing Systems; 2016:3619-3627.","mla":"Pentina, Anastasia, and Ruth Urner. <i>Lifelong Learning with Weighted Majority Votes</i>. Vol. 29, Neural Information Processing Systems, 2016, pp. 3619–27.","apa":"Pentina, A., &#38; Urner, R. (2016). Lifelong learning with weighted majority votes (Vol. 29, pp. 3619–3627). Presented at the NIPS: Neural Information Processing Systems, Barcelona, Spain: Neural Information Processing Systems.","ista":"Pentina A, Urner R. 2016. Lifelong learning with weighted majority votes. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 29, 3619–3627.","chicago":"Pentina, Anastasia, and Ruth Urner. “Lifelong Learning with Weighted Majority Votes,” 29:3619–27. Neural Information Processing Systems, 2016."},"pubrep_id":"775","language":[{"iso":"eng"}],"oa":1,"file":[{"file_size":237111,"date_created":"2018-12-12T10:12:42Z","date_updated":"2018-12-12T10:12:42Z","creator":"system","file_id":"4961","file_name":"IST-2017-775-v1+1_main.pdf","content_type":"application/pdf","access_level":"open_access","relation":"main_file"},{"file_name":"IST-2017-775-v1+2_supplementary.pdf","content_type":"application/pdf","access_level":"open_access","relation":"main_file","file_size":185818,"date_created":"2018-12-12T10:12:43Z","date_updated":"2018-12-12T10:12:43Z","creator":"system","file_id":"4962"}],"department":[{"_id":"ChLa"}],"month":"12","quality_controlled":"1","ddc":["006"],"page":"3619-3627","type":"conference","_id":"1098","date_updated":"2021-01-12T06:48:15Z","publisher":"Neural Information Processing Systems","alternative_title":["Advances in Neural Information Processing Systems"],"acknowledgement":"This work was in parts funded by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no 308036.\r\n\r\n","date_published":"2016-12-01T00:00:00Z","conference":{"location":"Barcelona, Spain","name":"NIPS: Neural Information Processing Systems","end_date":"2016-12-10","start_date":"2016-12-05"},"ec_funded":1,"status":"public","project":[{"name":"Lifelong Learning of Visual Scene Understanding","grant_number":"308036","call_identifier":"FP7","_id":"2532554C-B435-11E9-9278-68D0E5697425"}],"publist_id":"6277","year":"2016"},{"ddc":["006"],"page":"127","publisher":"Institute of Science and Technology Austria","doi":"10.15479/AT:ISTA:TH_776","alternative_title":["ISTA Thesis"],"article_processing_charge":"No","type":"dissertation","date_updated":"2023-09-07T11:52:03Z","_id":"1126","project":[{"_id":"2532554C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"308036","name":"Lifelong Learning of Visual Scene Understanding"}],"status":"public","degree_awarded":"PhD","acknowledgement":"First and foremost I would like to express my gratitude to my supervisor, Christoph\r\nLampert. Thank you for your patience in teaching me all aspects of doing research\r\n(including English grammar), for your trust in my capabilities and endless support. Thank\r\nyou for granting me freedom in my research and, at the same time, having time and\r\nhelping me cope with the consequences whenever I needed it. Thank you for creating\r\nan excellent atmosphere in the group, it was a great pleasure and honor to be a part of\r\nit. There could not have been a better and more inspiring adviser and mentor.\r\nI thank Shai Ben-David for welcoming me into his group at the University of Waterloo,\r\nfor inspiring discussions and support. It was a great pleasure to work together. I am\r\nalso thankful to Ruth Urner for hosting me at the Max-Planck Institute Tübingen, for the\r\nfruitful collaboration and for taking care of me during that not-so-sunny month of May.\r\nI thank Jan Maas for kindly joining my thesis committee despite the short notice and\r\nproviding me with insightful comments.\r\nI would like to thank my colleagues for their support, entertaining conversations and\r\nendless table soccer games we shared together: Georg, Jan, Amelie and Emilie, Michal\r\nand Alex, Alex K. and Alex Z., Thomas, Sameh, Vlad, Mayu, Nathaniel, Silvester, Neel,\r\nCsaba, Vladimir, Morten. Thank you, Mabel and Ram, for the wonderful time we spent\r\ntogether. I am thankful to Shrinu and Samira for taking care of me during my stay at the\r\nUniversity of Waterloo. Special thanks to Viktoriia for her never-ending optimism and for\r\nbeing so inspiring and supportive, especially at the beginning of my PhD journey.\r\nThanks to IST administration, in particular, Vlad and Elisabeth for shielding me from\r\nmost of the bureaucratic paperwork.\r\n\r\nThis dissertation would not have been possible without funding from the European\r\nResearch Council under the European Union's Seventh Framework Programme\r\n(FP7/2007-2013)/ERC grant agreement no 308036.","date_published":"2016-11-01T00:00:00Z","ec_funded":1,"year":"2016","publist_id":"6234","abstract":[{"lang":"eng","text":"Traditionally machine learning has been focusing on the problem of solving a single\r\ntask in isolation. While being quite well understood, this approach disregards an\r\nimportant aspect of human learning: when facing a new problem, humans are able to\r\nexploit knowledge acquired from previously learned tasks. Intuitively, access to several\r\nproblems simultaneously or sequentially could also be advantageous for a machine\r\nlearning system, especially if these tasks are closely related. Indeed, results of many\r\nempirical studies have provided justification for this intuition. However, theoretical\r\njustifications of this idea are rather limited.\r\nThe focus of this thesis is to expand the understanding of potential benefits of information\r\ntransfer between several related learning problems. We provide theoretical\r\nanalysis for three scenarios of multi-task learning - multiple kernel learning, sequential\r\nlearning and active task selection. We also provide a PAC-Bayesian perspective on\r\nlifelong learning and investigate how the task generation process influences the generalization\r\nguarantees in this scenario. In addition, we show how some of the obtained\r\ntheoretical results can be used to derive principled multi-task and lifelong learning\r\nalgorithms and illustrate their performance on various synthetic and real-world datasets."}],"has_accepted_license":"1","publication_status":"published","publication_identifier":{"issn":["2663-337X"]},"file_date_updated":"2018-12-12T10:14:07Z","title":"Theoretical foundations of multi-task lifelong learning","oa_version":"Published Version","author":[{"last_name":"Pentina","full_name":"Pentina, Anastasia","id":"42E87FC6-F248-11E8-B48F-1D18A9856A87","first_name":"Anastasia"}],"day":"01","date_created":"2018-12-11T11:50:17Z","language":[{"iso":"eng"}],"pubrep_id":"776","oa":1,"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","citation":{"ama":"Pentina A. Theoretical foundations of multi-task lifelong learning. 2016. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:TH_776\">10.15479/AT:ISTA:TH_776</a>","short":"A. Pentina, Theoretical Foundations of Multi-Task Lifelong Learning, Institute of Science and Technology Austria, 2016.","ieee":"A. Pentina, “Theoretical foundations of multi-task lifelong learning,” Institute of Science and Technology Austria, 2016.","chicago":"Pentina, Anastasia. “Theoretical Foundations of Multi-Task Lifelong Learning.” Institute of Science and Technology Austria, 2016. <a href=\"https://doi.org/10.15479/AT:ISTA:TH_776\">https://doi.org/10.15479/AT:ISTA:TH_776</a>.","ista":"Pentina A. 2016. Theoretical foundations of multi-task lifelong learning. Institute of Science and Technology Austria.","mla":"Pentina, Anastasia. <i>Theoretical Foundations of Multi-Task Lifelong Learning</i>. Institute of Science and Technology Austria, 2016, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:TH_776\">10.15479/AT:ISTA:TH_776</a>.","apa":"Pentina, A. (2016). <i>Theoretical foundations of multi-task lifelong learning</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:TH_776\">https://doi.org/10.15479/AT:ISTA:TH_776</a>"},"month":"11","supervisor":[{"first_name":"Christoph","orcid":"0000-0001-8622-7887","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph"}],"file":[{"relation":"main_file","file_name":"IST-2017-776-v1+1_Pentina_Thesis_2016.pdf","access_level":"open_access","content_type":"application/pdf","file_id":"5056","file_size":2140062,"date_created":"2018-12-12T10:14:07Z","creator":"system","date_updated":"2018-12-12T10:14:07Z"}],"department":[{"_id":"ChLa"}]},{"language":[{"iso":"eng"}],"oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Pentina, Anastasia, and Shai Ben David. “Multi-Task and Lifelong Learning of Kernels,” 9355:194–208. Springer, 2015. <a href=\"https://doi.org/10.1007/978-3-319-24486-0_13\">https://doi.org/10.1007/978-3-319-24486-0_13</a>.","ista":"Pentina A, Ben David S. 2015. Multi-task and lifelong learning of kernels. ALT: Algorithmic Learning Theory, LNCS, vol. 9355, 194–208.","apa":"Pentina, A., &#38; Ben David, S. (2015). Multi-task and lifelong learning of kernels (Vol. 9355, pp. 194–208). Presented at the ALT: Algorithmic Learning Theory, Banff, AB, Canada: Springer. <a href=\"https://doi.org/10.1007/978-3-319-24486-0_13\">https://doi.org/10.1007/978-3-319-24486-0_13</a>","mla":"Pentina, Anastasia, and Shai Ben David. <i>Multi-Task and Lifelong Learning of Kernels</i>. Vol. 9355, Springer, 2015, pp. 194–208, doi:<a href=\"https://doi.org/10.1007/978-3-319-24486-0_13\">10.1007/978-3-319-24486-0_13</a>.","ama":"Pentina A, Ben David S. Multi-task and lifelong learning of kernels. In: Vol 9355. Springer; 2015:194-208. doi:<a href=\"https://doi.org/10.1007/978-3-319-24486-0_13\">10.1007/978-3-319-24486-0_13</a>","ieee":"A. Pentina and S. Ben David, “Multi-task and lifelong learning of kernels,” presented at the ALT: Algorithmic Learning Theory, Banff, AB, Canada, 2015, vol. 9355, pp. 194–208.","short":"A. Pentina, S. Ben David, in:, Springer, 2015, pp. 194–208."},"month":"01","department":[{"_id":"ChLa"}],"intvolume":"      9355","abstract":[{"lang":"eng","text":"We consider a problem of learning kernels for use in SVM classification in the multi-task and lifelong scenarios and provide generalization bounds on the error of a large margin classifier. Our results show that, under mild conditions on the family of kernels used for learning, solving several related tasks simultaneously is beneficial over single task learning. In particular, as the number of observed tasks grows, assuming that in the considered family of kernels there exists one that yields low approximation error on all tasks, the overhead associated with learning such a kernel vanishes and the complexity converges to that of learning when this good kernel is given to the learner."}],"publication_status":"published","title":"Multi-task and lifelong learning of kernels","oa_version":"Preprint","day":"01","scopus_import":1,"author":[{"last_name":"Pentina","id":"42E87FC6-F248-11E8-B48F-1D18A9856A87","full_name":"Pentina, Anastasia","first_name":"Anastasia"},{"first_name":"Shai","last_name":"Ben David","full_name":"Ben David, Shai"}],"date_created":"2018-12-11T11:53:35Z","volume":9355,"status":"public","project":[{"call_identifier":"FP7","grant_number":"308036","name":"Lifelong Learning of Visual Scene Understanding","_id":"2532554C-B435-11E9-9278-68D0E5697425"}],"date_published":"2015-01-01T00:00:00Z","conference":{"end_date":"2015-10-06","start_date":"2015-10-04","name":"ALT: Algorithmic Learning Theory","location":"Banff, AB, Canada"},"ec_funded":1,"year":"2015","publist_id":"5430","page":"194 - 208","quality_controlled":"1","main_file_link":[{"url":"http://arxiv.org/abs/1602.06531","open_access":"1"}],"publisher":"Springer","alternative_title":["LNCS"],"doi":"10.1007/978-3-319-24486-0_13","type":"conference","_id":"1706","date_updated":"2021-01-12T06:52:39Z"},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2015-06-01T00:00:00Z","conference":{"location":"Boston, MA, United States","start_date":"2015-06-07","end_date":"2015-06-12","name":"CVPR: Computer Vision and Pattern Recognition"},"citation":{"ista":"Pentina A, Sharmanska V, Lampert C. 2015. Curriculum learning of multiple tasks. CVPR: Computer Vision and Pattern Recognition, 5492–5500.","chicago":"Pentina, Anastasia, Viktoriia Sharmanska, and Christoph Lampert. “Curriculum Learning of Multiple Tasks,” 5492–5500. IEEE, 2015. <a href=\"https://doi.org/10.1109/CVPR.2015.7299188\">https://doi.org/10.1109/CVPR.2015.7299188</a>.","mla":"Pentina, Anastasia, et al. <i>Curriculum Learning of Multiple Tasks</i>. IEEE, 2015, pp. 5492–500, doi:<a href=\"https://doi.org/10.1109/CVPR.2015.7299188\">10.1109/CVPR.2015.7299188</a>.","apa":"Pentina, A., Sharmanska, V., &#38; Lampert, C. (2015). Curriculum learning of multiple tasks (pp. 5492–5500). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE. <a href=\"https://doi.org/10.1109/CVPR.2015.7299188\">https://doi.org/10.1109/CVPR.2015.7299188</a>","ama":"Pentina A, Sharmanska V, Lampert C. Curriculum learning of multiple tasks. In: IEEE; 2015:5492-5500. doi:<a href=\"https://doi.org/10.1109/CVPR.2015.7299188\">10.1109/CVPR.2015.7299188</a>","short":"A. Pentina, V. Sharmanska, C. Lampert, in:, IEEE, 2015, pp. 5492–5500.","ieee":"A. Pentina, V. Sharmanska, and C. Lampert, “Curriculum learning of multiple tasks,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States, 2015, pp. 5492–5500."},"status":"public","language":[{"iso":"eng"}],"oa":1,"publist_id":"5243","department":[{"_id":"ChLa"}],"month":"06","year":"2015","quality_controlled":"1","publication_status":"published","main_file_link":[{"url":"http://arxiv.org/abs/1412.1353","open_access":"1"}],"abstract":[{"lang":"eng","text":"Sharing information between multiple tasks enables algorithms to achieve good generalization performance even from small amounts of training data. However, in a realistic scenario of multi-task learning not all tasks are equally related to each other, hence it could be advantageous to transfer information only between the most related tasks. In this work we propose an approach that processes multiple tasks in a sequence with sharing between subsequent tasks instead of solving all tasks jointly. Subsequently, we address the question of curriculum learning of tasks, i.e. finding the best order of tasks to be learned. Our approach is based on a generalization bound criterion for choosing the task order that optimizes the average expected classification performance over all tasks. Our experimental results show that learning multiple related tasks sequentially can be more effective than learning them jointly, the order in which tasks are being solved affects the overall performance, and that our model is able to automatically discover the favourable order of tasks. "}],"page":"5492 - 5500","date_created":"2018-12-11T11:54:23Z","type":"conference","_id":"1857","date_updated":"2023-02-23T10:17:31Z","oa_version":"Preprint","title":"Curriculum learning of multiple tasks","publisher":"IEEE","scopus_import":1,"day":"01","doi":"10.1109/CVPR.2015.7299188","author":[{"id":"42E87FC6-F248-11E8-B48F-1D18A9856A87","full_name":"Pentina, Anastasia","last_name":"Pentina","first_name":"Anastasia"},{"orcid":"0000-0003-0192-9308","first_name":"Viktoriia","last_name":"Sharmanska","id":"2EA6D09E-F248-11E8-B48F-1D18A9856A87","full_name":"Sharmanska, Viktoriia"},{"first_name":"Christoph","orcid":"0000-0001-8622-7887","last_name":"Lampert","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}]},{"scopus_import":1,"day":"01","author":[{"last_name":"Pentina","id":"42E87FC6-F248-11E8-B48F-1D18A9856A87","full_name":"Pentina, Anastasia","first_name":"Anastasia"},{"first_name":"Christoph","orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert"}],"title":"Lifelong learning with non-i.i.d. tasks","oa_version":"None","volume":2015,"date_created":"2018-12-11T11:51:57Z","abstract":[{"text":"In this work we aim at extending the theoretical foundations of lifelong learning. Previous work analyzing this scenario is based on the assumption that learning tasks are sampled i.i.d. from a task environment or limited to strongly constrained data distributions. Instead, we study two scenarios when lifelong learning is possible, even though the observed tasks do not form an i.i.d. sample: first, when they are sampled from the same environment, but possibly with dependencies, and second, when the task environment is allowed to change over time in a consistent way. In the first case we prove a PAC-Bayesian theorem that can be seen as a direct generalization of the analogous previous result for the i.i.d. case. For the second scenario we propose to learn an inductive bias in form of a transfer procedure. We present a generalization bound and show on a toy example how it can be used to identify a beneficial transfer algorithm.","lang":"eng"}],"intvolume":"      2015","publication_status":"published","month":"01","department":[{"_id":"ChLa"}],"oa":1,"language":[{"iso":"eng"}],"citation":{"ama":"Pentina A, Lampert C. Lifelong learning with non-i.i.d. tasks. In: Vol 2015. Neural Information Processing Systems; 2015:1540-1548.","short":"A. Pentina, C. Lampert, in:, Neural Information Processing Systems, 2015, pp. 1540–1548.","ieee":"A. Pentina and C. Lampert, “Lifelong learning with non-i.i.d. tasks,” presented at the NIPS: Neural Information Processing Systems, Montreal, Canada, 2015, vol. 2015, pp. 1540–1548.","chicago":"Pentina, Anastasia, and Christoph Lampert. “Lifelong Learning with Non-i.i.d. Tasks,” 2015:1540–48. Neural Information Processing Systems, 2015.","ista":"Pentina A, Lampert C. 2015. Lifelong learning with non-i.i.d. tasks. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 2015, 1540–1548.","mla":"Pentina, Anastasia, and Christoph Lampert. <i>Lifelong Learning with Non-i.i.d. Tasks</i>. Vol. 2015, Neural Information Processing Systems, 2015, pp. 1540–48.","apa":"Pentina, A., &#38; Lampert, C. (2015). Lifelong learning with non-i.i.d. tasks (Vol. 2015, pp. 1540–1548). Presented at the NIPS: Neural Information Processing Systems, Montreal, Canada: Neural Information Processing Systems."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","alternative_title":["Advances in Neural Information Processing Systems"],"publisher":"Neural Information Processing Systems","_id":"1425","date_updated":"2021-01-12T06:50:39Z","type":"conference","page":"1540 - 1548","main_file_link":[{"url":"http://papers.nips.cc/paper/6007-lifelong-learning-with-non-iid-tasks","open_access":"1"}],"quality_controlled":"1","year":"2015","publist_id":"5781","status":"public","project":[{"grant_number":"308036","name":"Lifelong Learning of Visual Scene Understanding","call_identifier":"FP7","_id":"2532554C-B435-11E9-9278-68D0E5697425"}],"ec_funded":1,"date_published":"2015-01-01T00:00:00Z","conference":{"name":"NIPS: Neural Information Processing Systems","end_date":"2015-12-12","start_date":"2015-12-07","location":"Montreal, Canada"}},{"status":"public","language":[{"iso":"eng"}],"oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","conference":{"start_date":"2014-06-21","end_date":"2014-06-26","name":"ICML: International Conference on Machine Learning","location":"Beijing, China"},"date_published":"2014-05-10T00:00:00Z","citation":{"mla":"Pentina, Anastasia, and Christoph Lampert. <i>A PAC-Bayesian Bound for Lifelong Learning</i>. Vol. 32, ML Research Press, 2014, pp. 991–99.","apa":"Pentina, A., &#38; Lampert, C. (2014). A PAC-Bayesian bound for Lifelong Learning (Vol. 32, pp. 991–999). Presented at the ICML: International Conference on Machine Learning, Beijing, China: ML Research Press.","ista":"Pentina A, Lampert C. 2014. A PAC-Bayesian bound for Lifelong Learning. ICML: International Conference on Machine Learning vol. 32, 991–999.","chicago":"Pentina, Anastasia, and Christoph Lampert. “A PAC-Bayesian Bound for Lifelong Learning,” 32:991–99. ML Research Press, 2014.","short":"A. Pentina, C. Lampert, in:, ML Research Press, 2014, pp. 991–999.","ieee":"A. Pentina and C. Lampert, “A PAC-Bayesian bound for Lifelong Learning,” presented at the ICML: International Conference on Machine Learning, Beijing, China, 2014, vol. 32, pp. 991–999.","ama":"Pentina A, Lampert C. A PAC-Bayesian bound for Lifelong Learning. In: Vol 32. ML Research Press; 2014:991-999."},"month":"05","year":"2014","publist_id":"4844","department":[{"_id":"ChLa"}],"abstract":[{"lang":"eng","text":"Transfer learning has received a lot of attention in the machine learning community over the last years, and several effective algorithms have been developed. However, relatively little is known about their theoretical properties, especially in the setting of lifelong learning, where the goal is to transfer information to tasks for which no data have been observed so far. In this work we study lifelong learning from a theoretical perspective. Our main result is a PAC-Bayesian generalization bound that offers a unified view on existing paradigms for transfer learning, such as the transfer of parameters or the transfer of low-dimensional representations. We also use the bound to derive two principled lifelong learning algorithms, and we show that these yield results comparable with existing methods."}],"intvolume":"        32","page":"991 - 999","quality_controlled":"1","publication_status":"published","main_file_link":[{"url":"https://dl.acm.org/citation.cfm?id=3045003","open_access":"1"}],"title":"A PAC-Bayesian bound for Lifelong Learning","oa_version":"Submitted Version","publisher":"ML Research Press","scopus_import":"1","day":"10","article_processing_charge":"No","author":[{"first_name":"Anastasia","full_name":"Pentina, Anastasia","id":"42E87FC6-F248-11E8-B48F-1D18A9856A87","last_name":"Pentina"},{"first_name":"Christoph","orcid":"0000-0001-8622-7887","last_name":"Lampert","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"date_created":"2018-12-11T11:56:03Z","type":"conference","volume":32,"_id":"2160","date_updated":"2023-10-17T11:54:24Z"}]