[{"day":"13","language":[{"iso":"eng"}],"date_updated":"2023-05-08T11:01:18Z","oa_version":"None","issue":"22","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","pmid":1,"doi":"10.1093/jxb/erab373","month":"08","status":"public","date_published":"2021-08-13T00:00:00Z","extern":"1","publication_status":"published","abstract":[{"lang":"eng","text":"Activation of cell-surface and intracellular receptor-mediated immunity results in rapid transcriptional reprogramming that underpins disease resistance. However, the mechanisms by which co-activation of both immune systems lead to transcriptional changes are not clear. Here, we combine RNA-seq and ATAC-seq to define changes in gene expression and chromatin accessibility. Activation of cell-surface or intracellular receptor-mediated immunity, or both, increases chromatin accessibility at induced defence genes. Analysis of ATAC-seq and RNA-seq data combined with publicly available information on transcription factor DNA-binding motifs enabled comparison of individual gene regulatory networks activated by cell-surface or intracellular receptor-mediated immunity, or by both. These results and analyses reveal overlapping and conserved transcriptional regulatory mechanisms between the two immune systems."}],"type":"journal_article","publisher":"Oxford University Press","intvolume":"        72","_id":"12186","author":[{"full_name":"Ding, Pingtao","first_name":"Pingtao","last_name":"Ding"},{"full_name":"Sakai, Toshiyuki","last_name":"Sakai","first_name":"Toshiyuki"},{"full_name":"Krishna Shrestha, Ram","last_name":"Krishna Shrestha","first_name":"Ram"},{"full_name":"Manosalva Perez, Nicolas","first_name":"Nicolas","last_name":"Manosalva Perez"},{"first_name":"Wenbin","last_name":"Guo","full_name":"Guo, Wenbin"},{"full_name":"Ngou, Bruno Pok Man","first_name":"Bruno Pok Man","last_name":"Ngou"},{"full_name":"He, Shengbo","last_name":"He","first_name":"Shengbo"},{"full_name":"Liu, Chang","last_name":"Liu","first_name":"Chang"},{"orcid":"0000-0002-4008-1234","last_name":"Feng","id":"e0164712-22ee-11ed-b12a-d80fcdf35958","first_name":"Xiaoqi","full_name":"Feng, Xiaoqi"},{"full_name":"Zhang, Runxuan","last_name":"Zhang","first_name":"Runxuan"},{"full_name":"Vandepoele, Klaas","last_name":"Vandepoele","first_name":"Klaas"},{"full_name":"MacLean, Dan","last_name":"MacLean","first_name":"Dan"},{"full_name":"Jones, Jonathan D G","last_name":"Jones","first_name":"Jonathan D G"}],"keyword":["Plant Science","Physiology"],"citation":{"short":"P. Ding, T. Sakai, R. Krishna Shrestha, N. Manosalva Perez, W. Guo, B.P.M. Ngou, S. He, C. Liu, X. Feng, R. Zhang, K. Vandepoele, D. MacLean, J.D.G. Jones, Journal of Experimental Botany 72 (2021) 7927–7941.","apa":"Ding, P., Sakai, T., Krishna Shrestha, R., Manosalva Perez, N., Guo, W., Ngou, B. P. M., … Jones, J. D. G. (2021). Chromatin accessibility landscapes activated by cell-surface and intracellular immune receptors. <i>Journal of Experimental Botany</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/jxb/erab373\">https://doi.org/10.1093/jxb/erab373</a>","chicago":"Ding, Pingtao, Toshiyuki Sakai, Ram Krishna Shrestha, Nicolas Manosalva Perez, Wenbin Guo, Bruno Pok Man Ngou, Shengbo He, et al. “Chromatin Accessibility Landscapes Activated by Cell-Surface and Intracellular Immune Receptors.” <i>Journal of Experimental Botany</i>. Oxford University Press, 2021. <a href=\"https://doi.org/10.1093/jxb/erab373\">https://doi.org/10.1093/jxb/erab373</a>.","mla":"Ding, Pingtao, et al. “Chromatin Accessibility Landscapes Activated by Cell-Surface and Intracellular Immune Receptors.” <i>Journal of Experimental Botany</i>, vol. 72, no. 22, Oxford University Press, 2021, pp. 7927–41, doi:<a href=\"https://doi.org/10.1093/jxb/erab373\">10.1093/jxb/erab373</a>.","ista":"Ding P, Sakai T, Krishna Shrestha R, Manosalva Perez N, Guo W, Ngou BPM, He S, Liu C, Feng X, Zhang R, Vandepoele K, MacLean D, Jones JDG. 2021. Chromatin accessibility landscapes activated by cell-surface and intracellular immune receptors. Journal of Experimental Botany. 72(22), 7927–7941.","ieee":"P. Ding <i>et al.</i>, “Chromatin accessibility landscapes activated by cell-surface and intracellular immune receptors,” <i>Journal of Experimental Botany</i>, vol. 72, no. 22. Oxford University Press, pp. 7927–7941, 2021.","ama":"Ding P, Sakai T, Krishna Shrestha R, et al. Chromatin accessibility landscapes activated by cell-surface and intracellular immune receptors. <i>Journal of Experimental Botany</i>. 2021;72(22):7927-7941. doi:<a href=\"https://doi.org/10.1093/jxb/erab373\">10.1093/jxb/erab373</a>"},"scopus_import":"1","quality_controlled":"1","department":[{"_id":"XiFe"}],"publication_identifier":{"issn":["0022-0957","1460-2431"]},"article_processing_charge":"No","date_created":"2023-01-16T09:14:35Z","acknowledgement":"We thank the Gatsby Foundation (UK) for funding to the JDGJ laboratory. PD acknowledges support from the European Union’s Horizon 2020 Research and Innovation Program under Marie Skłodowska Curie Actions (grant agreement: 656243) and a Future Leader Fellowship from the Biotechnology and Biological Sciences Research Council (BBSRC) (grant agreement: BB/R012172/1). TS, RKS, DM, and JDGJ were supported by the Gatsby Foundation funding to the\r\nSainsbury Laboratory. NMP and KV were supported by a BOF grant from Ghent University (grant agreement: BOF24Y2019001901). WG and RZ were supported by the Scottish Government Rural and Environment Science and Analytical Services division (RESAS), and RZ also acknowledges the support from a BBSRC Bioinformatics and Biological Resources Fund (grant agreement: BB/S020160/1).BPMN was supported by the Norwich Research Park (NRP) Biosciences Doctoral Training Partnership (DTP) funded by the BBSRC (grant agreement: BB/M011216/1). SH and XF were supported by a BBSRC Responsive Mode grant (grant agreement: BB/S009620/1) and a European Research Council Starting grant ‘SexMeth’ (grant agreement: 804981). CL was supported by Deutsche Forschungsgemeinschaft (grant agreement: LI 2862/4). ","publication":"Journal of Experimental Botany","year":"2021","volume":72,"article_type":"original","external_id":{"pmid":["34387350"]},"title":"Chromatin accessibility landscapes activated by cell-surface and intracellular immune receptors","page":"7927-7941"},{"day":"01","language":[{"iso":"eng"}],"oa":1,"issue":"5","oa_version":"Published Version","date_updated":"2023-09-11T11:43:35Z","arxiv":1,"doi":"10.1109/jproc.2021.3058954","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"05","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1109/JPROC.2021.3058954"}],"date_published":"2021-05-01T00:00:00Z","status":"public","abstract":[{"lang":"eng","text":"The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, that is, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities."}],"publication_status":"published","extern":"1","type":"journal_article","intvolume":"       109","publisher":"Institute of Electrical and Electronics Engineers","author":[{"full_name":"Scholkopf, Bernhard","last_name":"Scholkopf","first_name":"Bernhard"},{"full_name":"Locatello, Francesco","first_name":"Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello"},{"full_name":"Bauer, Stefan","first_name":"Stefan","last_name":"Bauer"},{"first_name":"Nan Rosemary","last_name":"Ke","full_name":"Ke, Nan Rosemary"},{"full_name":"Kalchbrenner, Nal","last_name":"Kalchbrenner","first_name":"Nal"},{"last_name":"Goyal","first_name":"Anirudh","full_name":"Goyal, Anirudh"},{"last_name":"Bengio","first_name":"Yoshua","full_name":"Bengio, Yoshua"}],"_id":"14117","quality_controlled":"1","scopus_import":"1","citation":{"ieee":"B. Scholkopf <i>et al.</i>, “Toward causal representation learning,” <i>Proceedings of the IEEE</i>, vol. 109, no. 5. Institute of Electrical and Electronics Engineers, pp. 612–634, 2021.","mla":"Scholkopf, Bernhard, et al. “Toward Causal Representation Learning.” <i>Proceedings of the IEEE</i>, vol. 109, no. 5, Institute of Electrical and Electronics Engineers, 2021, pp. 612–34, doi:<a href=\"https://doi.org/10.1109/jproc.2021.3058954\">10.1109/jproc.2021.3058954</a>.","ista":"Scholkopf B, Locatello F, Bauer S, Ke NR, Kalchbrenner N, Goyal A, Bengio Y. 2021. Toward causal representation learning. Proceedings of the IEEE. 109(5), 612–634.","ama":"Scholkopf B, Locatello F, Bauer S, et al. Toward causal representation learning. <i>Proceedings of the IEEE</i>. 2021;109(5):612-634. doi:<a href=\"https://doi.org/10.1109/jproc.2021.3058954\">10.1109/jproc.2021.3058954</a>","apa":"Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., &#38; Bengio, Y. (2021). Toward causal representation learning. <i>Proceedings of the IEEE</i>. Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/jproc.2021.3058954\">https://doi.org/10.1109/jproc.2021.3058954</a>","short":"B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio, Proceedings of the IEEE 109 (2021) 612–634.","chicago":"Scholkopf, Bernhard, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. “Toward Causal Representation Learning.” <i>Proceedings of the IEEE</i>. Institute of Electrical and Electronics Engineers, 2021. <a href=\"https://doi.org/10.1109/jproc.2021.3058954\">https://doi.org/10.1109/jproc.2021.3058954</a>."},"keyword":["Electrical and Electronic Engineering"],"department":[{"_id":"FrLo"}],"date_created":"2023-08-21T12:19:30Z","article_processing_charge":"No","publication_identifier":{"issn":["0018-9219"],"eissn":["1558-2256"]},"external_id":{"arxiv":["2102.11107"]},"article_type":"original","title":"Toward causal representation learning","volume":109,"year":"2021","publication":"Proceedings of the IEEE","page":"612-634"},{"quality_controlled":"1","scopus_import":"1","citation":{"short":"H. Yèche, G. Dresdner, F. Locatello, M. Hüser, G. Rätsch, in:, Proceedings of 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 11964–11974.","apa":"Yèche, H., Dresdner, G., Locatello, F., Hüser, M., &#38; Rätsch, G. (2021). Neighborhood contrastive learning applied to online patient monitoring. In <i>Proceedings of 38th International Conference on Machine Learning</i> (Vol. 139, pp. 11964–11974). Virtual: ML Research Press.","chicago":"Yèche, Hugo, Gideon Dresdner, Francesco Locatello, Matthias Hüser, and Gunnar Rätsch. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.” In <i>Proceedings of 38th International Conference on Machine Learning</i>, 139:11964–74. ML Research Press, 2021.","ieee":"H. Yèche, G. Dresdner, F. Locatello, M. Hüser, and G. Rätsch, “Neighborhood contrastive learning applied to online patient monitoring,” in <i>Proceedings of 38th International Conference on Machine Learning</i>, Virtual, 2021, vol. 139, pp. 11964–11974.","ista":"Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. 2021. Neighborhood contrastive learning applied to online patient monitoring. Proceedings of 38th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 139, 11964–11974.","mla":"Yèche, Hugo, et al. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.” <i>Proceedings of 38th International Conference on Machine Learning</i>, vol. 139, ML Research Press, 2021, pp. 11964–74.","ama":"Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. Neighborhood contrastive learning applied to online patient monitoring. In: <i>Proceedings of 38th International Conference on Machine Learning</i>. Vol 139. ML Research Press; 2021:11964-11974."},"author":[{"full_name":"Yèche, Hugo","last_name":"Yèche","first_name":"Hugo"},{"first_name":"Gideon","last_name":"Dresdner","full_name":"Dresdner, Gideon"},{"full_name":"Locatello, Francesco","first_name":"Francesco","orcid":"0000-0002-4850-0683","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"},{"last_name":"Hüser","first_name":"Matthias","full_name":"Hüser, Matthias"},{"full_name":"Rätsch, Gunnar","first_name":"Gunnar","last_name":"Rätsch"}],"_id":"14176","intvolume":"       139","publisher":"ML Research Press","type":"conference","page":"11964-11974","external_id":{"arxiv":["2106.05142"]},"title":"Neighborhood contrastive learning applied to online patient monitoring","volume":139,"year":"2021","publication":"Proceedings of 38th International Conference on Machine Learning","date_created":"2023-08-22T14:03:04Z","article_processing_charge":"No","conference":{"start_date":"2021-07-18","end_date":"2021-07-24","location":"Virtual","name":"International Conference on Machine Learning"},"department":[{"_id":"FrLo"}],"alternative_title":["PMLR"],"oa_version":"Preprint","date_updated":"2023-09-11T10:16:55Z","arxiv":1,"language":[{"iso":"eng"}],"oa":1,"day":"01","publication_status":"published","abstract":[{"text":"Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monitoring is often formulated as a supervised learning problem. Recently, contrastive learning approaches have demonstrated promising improvements over competitive supervised benchmarks. These methods rely on well-understood data augmentation techniques developed for image data which do not apply to online monitoring. In this work, we overcome this limitation by\r\nsupplementing time-series data augmentation techniques with a novel contrastive\r\nlearning objective which we call neighborhood contrastive learning (NCL). Our objective explicitly groups together contiguous time segments from each patient while maintaining state-specific information. Our experiments demonstrate a marked improvement over existing work applying contrastive methods to medical time-series.","lang":"eng"}],"extern":"1","date_published":"2021-08-01T00:00:00Z","status":"public","month":"08","main_file_link":[{"url":"https://arxiv.org/abs/2106.05142","open_access":"1"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"department":[{"_id":"FrLo"}],"conference":{"name":"ICML: International Conference on Machine Learning","location":"Virtual","start_date":"2021-07-18","end_date":"2021-07-24"},"article_processing_charge":"No","date_created":"2023-08-22T14:03:47Z","publication":"Proceedings of the 38th International Conference on Machine Learning","year":"2021","volume":139,"title":"On disentangled representations learned from correlated data","external_id":{"arxiv":["2006.07886"]},"page":"10401-10412","type":"conference","publisher":"ML Research Press","intvolume":"       139","_id":"14177","author":[{"full_name":"Träuble, Frederik","first_name":"Frederik","last_name":"Träuble"},{"last_name":"Creager","first_name":"Elliot","full_name":"Creager, Elliot"},{"full_name":"Kilbertus, Niki","first_name":"Niki","last_name":"Kilbertus"},{"full_name":"Locatello, Francesco","first_name":"Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello"},{"full_name":"Dittadi, Andrea","last_name":"Dittadi","first_name":"Andrea"},{"full_name":"Goyal, Anirudh","last_name":"Goyal","first_name":"Anirudh"},{"full_name":"Schölkopf, Bernhard","last_name":"Schölkopf","first_name":"Bernhard"},{"first_name":"Stefan","last_name":"Bauer","full_name":"Bauer, Stefan"}],"citation":{"short":"F. Träuble, E. Creager, N. Kilbertus, F. Locatello, A. Dittadi, A. Goyal, B. Schölkopf, S. Bauer, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 10401–10412.","apa":"Träuble, F., Creager, E., Kilbertus, N., Locatello, F., Dittadi, A., Goyal, A., … Bauer, S. (2021). On disentangled representations learned from correlated data. In <i>Proceedings of the 38th International Conference on Machine Learning</i> (Vol. 139, pp. 10401–10412). Virtual: ML Research Press.","chicago":"Träuble, Frederik, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, and Stefan Bauer. “On Disentangled Representations Learned from Correlated Data.” In <i>Proceedings of the 38th International Conference on Machine Learning</i>, 139:10401–12. ML Research Press, 2021.","mla":"Träuble, Frederik, et al. “On Disentangled Representations Learned from Correlated Data.” <i>Proceedings of the 38th International Conference on Machine Learning</i>, vol. 139, ML Research Press, 2021, pp. 10401–12.","ieee":"F. Träuble <i>et al.</i>, “On disentangled representations learned from correlated data,” in <i>Proceedings of the 38th International Conference on Machine Learning</i>, Virtual, 2021, vol. 139, pp. 10401–10412.","ista":"Träuble F, Creager E, Kilbertus N, Locatello F, Dittadi A, Goyal A, Schölkopf B, Bauer S. 2021. On disentangled representations learned from correlated data. Proceedings of the 38th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 139, 10401–10412.","ama":"Träuble F, Creager E, Kilbertus N, et al. On disentangled representations learned from correlated data. In: <i>Proceedings of the 38th International Conference on Machine Learning</i>. Vol 139. ML Research Press; 2021:10401-10412."},"scopus_import":"1","quality_controlled":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2006.07886"}],"month":"08","status":"public","date_published":"2021-08-01T00:00:00Z","extern":"1","abstract":[{"text":"The focus of disentanglement approaches has been on identifying independent factors of variation in data. However, the causal variables underlying real-world observations are often not statistically independent. In this work, we bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data in a large-scale empirical study (including 4260 models). We show and quantify that systematically induced correlations in the dataset are being learned and reflected in the latent representations, which has implications for downstream applications of disentanglement such as fairness. We also demonstrate how to resolve these latent correlations, either using weak supervision during\r\ntraining or by post-hoc correcting a pre-trained model with a small number of labels.","lang":"eng"}],"publication_status":"published","day":"01","oa":1,"language":[{"iso":"eng"}],"arxiv":1,"oa_version":"Published Version","alternative_title":["PMLR"],"date_updated":"2023-09-11T10:18:48Z"},{"date_created":"2023-08-22T14:04:16Z","article_processing_charge":"No","month":"05","main_file_link":[{"url":"https://arxiv.org/abs/2010.14407","open_access":"1"}],"conference":{"location":"Virtual","end_date":"2021-05-07","start_date":"2021-05-03","name":"ICLR: International Conference on Learning Representations"},"department":[{"_id":"FrLo"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","abstract":[{"lang":"eng","text":"Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning. While disentangled representations were found to be useful for diverse tasks such as abstract reasoning and fair classification, their scalability and real-world impact remain questionable. We introduce a new high-resolution dataset with 1M simulated images and over 1,800 annotated real-world images of the same setup. In contrast to previous work, this new dataset exhibits correlations, a complex underlying structure, and allows to evaluate transfer to unseen simulated and real-world settings where the encoder i) remains in distribution or ii) is out of distribution. We propose new architectures in order to scale disentangled representation learning to realistic high-resolution settings and conduct a large-scale empirical study of disentangled representations on this dataset. We observe that disentanglement is a good predictor for out-of-distribution (OOD) task performance."}],"extern":"1","external_id":{"arxiv":["2010.14407"]},"title":"On the transfer of disentangled representations in realistic settings","date_published":"2021-05-04T00:00:00Z","year":"2021","status":"public","publication":"The Ninth International Conference on Learning Representations","day":"04","type":"conference","date_updated":"2023-09-11T10:55:30Z","oa_version":"Preprint","quality_controlled":"1","citation":{"ista":"Dittadi A, Träuble F, Locatello F, Wüthrich M, Agrawal V, Winther O, Bauer S, Schölkopf B. 2021. On the transfer of disentangled representations in realistic settings. The Ninth International Conference on Learning Representations. ICLR: International Conference on Learning Representations.","mla":"Dittadi, Andrea, et al. “On the Transfer of Disentangled Representations in Realistic Settings.” <i>The Ninth International Conference on Learning Representations</i>, 2021.","ieee":"A. Dittadi <i>et al.</i>, “On the transfer of disentangled representations in realistic settings,” in <i>The Ninth International Conference on Learning Representations</i>, Virtual, 2021.","ama":"Dittadi A, Träuble F, Locatello F, et al. On the transfer of disentangled representations in realistic settings. In: <i>The Ninth International Conference on Learning Representations</i>. ; 2021.","short":"A. Dittadi, F. Träuble, F. Locatello, M. Wüthrich, V. Agrawal, O. Winther, S. Bauer, B. Schölkopf, in:, The Ninth International Conference on Learning Representations, 2021.","apa":"Dittadi, A., Träuble, F., Locatello, F., Wüthrich, M., Agrawal, V., Winther, O., … Schölkopf, B. (2021). On the transfer of disentangled representations in realistic settings. In <i>The Ninth International Conference on Learning Representations</i>. Virtual.","chicago":"Dittadi, Andrea, Frederik Träuble, Francesco Locatello, Manuel Wüthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, and Bernhard Schölkopf. “On the Transfer of Disentangled Representations in Realistic Settings.” In <i>The Ninth International Conference on Learning Representations</i>, 2021."},"arxiv":1,"author":[{"full_name":"Dittadi, Andrea","first_name":"Andrea","last_name":"Dittadi"},{"full_name":"Träuble, Frederik","first_name":"Frederik","last_name":"Träuble"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","orcid":"0000-0002-4850-0683","first_name":"Francesco","full_name":"Locatello, Francesco"},{"first_name":"Manuel","last_name":"Wüthrich","full_name":"Wüthrich, Manuel"},{"first_name":"Vaibhav","last_name":"Agrawal","full_name":"Agrawal, Vaibhav"},{"last_name":"Winther","first_name":"Ole","full_name":"Winther, Ole"},{"first_name":"Stefan","last_name":"Bauer","full_name":"Bauer, Stefan"},{"last_name":"Schölkopf","first_name":"Bernhard","full_name":"Schölkopf, Bernhard"}],"language":[{"iso":"eng"}],"_id":"14178","oa":1},{"quality_controlled":"1","citation":{"mla":"Kügelgen, Julius von, et al. “Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.” <i>Advances in Neural Information Processing Systems</i>, vol. 34, 2021, pp. 16451–67.","ista":"Kügelgen J von, Sharma Y, Gresele L, Brendel W, Schölkopf B, Besserve M, Locatello F. 2021. Self-supervised learning with data augmentations provably isolates content from style. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 16451–16467.","ieee":"J. von Kügelgen <i>et al.</i>, “Self-supervised learning with data augmentations provably isolates content from style,” in <i>Advances in Neural Information Processing Systems</i>, Virtual, 2021, vol. 34, pp. 16451–16467.","ama":"Kügelgen J von, Sharma Y, Gresele L, et al. Self-supervised learning with data augmentations provably isolates content from style. In: <i>Advances in Neural Information Processing Systems</i>. Vol 34. ; 2021:16451-16467.","apa":"Kügelgen, J. von, Sharma, Y., Gresele, L., Brendel, W., Schölkopf, B., Besserve, M., &#38; Locatello, F. (2021). Self-supervised learning with data augmentations provably isolates content from style. In <i>Advances in Neural Information Processing Systems</i> (Vol. 34, pp. 16451–16467). Virtual.","short":"J. von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve, F. Locatello, in:, Advances in Neural Information Processing Systems, 2021, pp. 16451–16467.","chicago":"Kügelgen, Julius von, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, and Francesco Locatello. “Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.” In <i>Advances in Neural Information Processing Systems</i>, 34:16451–67, 2021."},"author":[{"first_name":"Julius von","last_name":"Kügelgen","full_name":"Kügelgen, Julius von"},{"full_name":"Sharma, Yash","last_name":"Sharma","first_name":"Yash"},{"full_name":"Gresele, Luigi","first_name":"Luigi","last_name":"Gresele"},{"full_name":"Brendel, Wieland","last_name":"Brendel","first_name":"Wieland"},{"last_name":"Schölkopf","first_name":"Bernhard","full_name":"Schölkopf, Bernhard"},{"first_name":"Michel","last_name":"Besserve","full_name":"Besserve, Michel"},{"full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco"}],"_id":"14179","intvolume":"        34","type":"conference","page":"16451-16467","title":"Self-supervised learning with data augmentations provably isolates content from style","external_id":{"arxiv":["2106.04619"]},"volume":34,"year":"2021","publication":"Advances in Neural Information Processing Systems","date_created":"2023-08-22T14:04:36Z","article_processing_charge":"No","publication_identifier":{"isbn":["9781713845393"]},"conference":{"location":"Virtual","start_date":"2021-12-07","end_date":"2021-12-10","name":"NeurIPS: Neural Information Processing Systems"},"department":[{"_id":"FrLo"}],"date_updated":"2023-09-11T10:33:19Z","oa_version":"Preprint","arxiv":1,"language":[{"iso":"eng"}],"oa":1,"day":"08","abstract":[{"text":"Self-supervised representation learning has shown remarkable success in a number of domains. A common practice is to perform data augmentation via hand-crafted transformations intended to leave the semantics of the data invariant. We seek to understand the empirical success of this approach from a theoretical perspective. We formulate the augmentation process as a latent variable model by postulating a partition of the latent representation into a content component, which is assumed invariant to augmentation, and a style component, which is allowed to change. Unlike prior work on disentanglement and independent component analysis, we allow for both nontrivial statistical and causal dependencies in the latent space. We study the identifiability of the latent representation based on pairs of views of the observations and prove sufficient conditions that allow us to identify the invariant content partition up to an invertible mapping in both generative and discriminative settings. We find numerical simulations with dependent latent variables are consistent with our theory. Lastly, we introduce Causal3DIdent, a dataset of high-dimensional, visually complex images with rich causal dependencies, which we use to study the effect of data augmentations performed in practice.","lang":"eng"}],"publication_status":"published","extern":"1","date_published":"2021-06-08T00:00:00Z","status":"public","month":"06","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2106.04619"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"date_updated":"2024-10-14T12:27:25Z","oa_version":"Preprint","arxiv":1,"language":[{"iso":"eng"}],"oa":1,"day":"12","extern":"1","abstract":[{"text":"Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution. However, they are less capable of systematic generalization to data drawn from unseen but related distributions, a feat that is hypothesized to require compositional reasoning and reuse of knowledge. In this work, we present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules, which we call \\emph{functions}. Inputs to the model are routed through a sequence of functions in a way that is end-to-end learned. The proposed architecture can flexibly compose computation along width and depth, and lends itself well to capacity extension after training. To demonstrate the versatility of Neural Interpreters, we evaluate it in two distinct settings: image classification and visual abstract reasoning on Raven Progressive Matrices. In the former, we show that Neural Interpreters perform on par with the vision transformer using fewer parameters, while being transferrable to a new task in a sample efficient manner. In the latter, we find that Neural Interpreters are competitive with respect to the state-of-the-art in terms of systematic generalization. ","lang":"eng"}],"publication_status":"published","date_published":"2021-10-12T00:00:00Z","status":"public","month":"10","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2110.06399"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","quality_controlled":"1","citation":{"ieee":"N. Rahaman <i>et al.</i>, “Dynamic inference with neural interpreters,” in <i>Advances in Neural Information Processing Systems</i>, Virtual, 2021, vol. 34, pp. 10985–10998.","ista":"Rahaman N, Gondal MW, Joshi S, Gehler P, Bengio Y, Locatello F, Schölkopf B. 2021. Dynamic inference with neural interpreters. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 10985–10998.","mla":"Rahaman, Nasim, et al. “Dynamic Inference with Neural Interpreters.” <i>Advances in Neural Information Processing Systems</i>, vol. 34, 2021, pp. 10985–98.","ama":"Rahaman N, Gondal MW, Joshi S, et al. Dynamic inference with neural interpreters. In: <i>Advances in Neural Information Processing Systems</i>. Vol 34. ; 2021:10985-10998.","apa":"Rahaman, N., Gondal, M. W., Joshi, S., Gehler, P., Bengio, Y., Locatello, F., &#38; Schölkopf, B. (2021). Dynamic inference with neural interpreters. In <i>Advances in Neural Information Processing Systems</i> (Vol. 34, pp. 10985–10998). Virtual.","short":"N. Rahaman, M.W. Gondal, S. Joshi, P. Gehler, Y. Bengio, F. Locatello, B. Schölkopf, in:, Advances in Neural Information Processing Systems, 2021, pp. 10985–10998.","chicago":"Rahaman, Nasim, Muhammad Waleed Gondal, Shruti Joshi, Peter Gehler, Yoshua Bengio, Francesco Locatello, and Bernhard Schölkopf. “Dynamic Inference with Neural Interpreters.” In <i>Advances in Neural Information Processing Systems</i>, 34:10985–98, 2021."},"_id":"14180","author":[{"last_name":"Rahaman","first_name":"Nasim","full_name":"Rahaman, Nasim"},{"first_name":"Muhammad Waleed","last_name":"Gondal","full_name":"Gondal, Muhammad Waleed"},{"full_name":"Joshi, Shruti","first_name":"Shruti","last_name":"Joshi"},{"full_name":"Gehler, Peter","last_name":"Gehler","first_name":"Peter"},{"full_name":"Bengio, Yoshua","last_name":"Bengio","first_name":"Yoshua"},{"orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","first_name":"Francesco","full_name":"Locatello, Francesco"},{"full_name":"Schölkopf, Bernhard","first_name":"Bernhard","last_name":"Schölkopf"}],"intvolume":"        34","type":"conference","page":"10985-10998","volume":34,"external_id":{"arxiv":["2110.06399"]},"title":"Dynamic inference with neural interpreters","publication":"Advances in Neural Information Processing Systems","year":"2021","article_processing_charge":"No","date_created":"2023-08-22T14:04:55Z","publication_identifier":{"isbn":["9781713845393"]},"conference":{"end_date":"2021-12-10","start_date":"2021-12-07","location":"Virtual","name":"NeurIPS: Neural Information Processing Systems"},"department":[{"_id":"FrLo"}]},{"publisher":"International Joint Conferences on Artificial Intelligence","type":"conference","citation":{"chicago":"Dresdner, Gideon, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, and Gunnar Rätsch. “Boosting Variational Inference with Locally Adaptive Step-Sizes.” In <i>Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence</i>, 2337–43. International Joint Conferences on Artificial Intelligence, 2021. <a href=\"https://doi.org/10.24963/ijcai.2021/322\">https://doi.org/10.24963/ijcai.2021/322</a>.","apa":"Dresdner, G., Shekhar, S., Pedregosa, F., Locatello, F., &#38; Rätsch, G. (2021). Boosting variational inference with locally adaptive step-sizes. In <i>Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence</i> (pp. 2337–2343). Montreal, Canada: International Joint Conferences on Artificial Intelligence. <a href=\"https://doi.org/10.24963/ijcai.2021/322\">https://doi.org/10.24963/ijcai.2021/322</a>","short":"G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, G. Rätsch, in:, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 2337–2343.","ama":"Dresdner G, Shekhar S, Pedregosa F, Locatello F, Rätsch G. Boosting variational inference with locally adaptive step-sizes. In: <i>Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence</i>. International Joint Conferences on Artificial Intelligence; 2021:2337-2343. doi:<a href=\"https://doi.org/10.24963/ijcai.2021/322\">10.24963/ijcai.2021/322</a>","ieee":"G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, and G. Rätsch, “Boosting variational inference with locally adaptive step-sizes,” in <i>Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence</i>, Montreal, Canada, 2021, pp. 2337–2343.","mla":"Dresdner, Gideon, et al. “Boosting Variational Inference with Locally Adaptive Step-Sizes.” <i>Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence</i>, International Joint Conferences on Artificial Intelligence, 2021, pp. 2337–43, doi:<a href=\"https://doi.org/10.24963/ijcai.2021/322\">10.24963/ijcai.2021/322</a>.","ista":"Dresdner G, Shekhar S, Pedregosa F, Locatello F, Rätsch G. 2021. Boosting variational inference with locally adaptive step-sizes. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. IJCAI: International Joint Conference on Artificial Intelligence, 2337–2343."},"quality_controlled":"1","author":[{"full_name":"Dresdner, Gideon","last_name":"Dresdner","first_name":"Gideon"},{"full_name":"Shekhar, Saurav","last_name":"Shekhar","first_name":"Saurav"},{"last_name":"Pedregosa","first_name":"Fabian","full_name":"Pedregosa, Fabian"},{"full_name":"Locatello, Francesco","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","first_name":"Francesco"},{"last_name":"Rätsch","first_name":"Gunnar","full_name":"Rätsch, Gunnar"}],"_id":"14181","publication_identifier":{"eisbn":["9780999241196"]},"date_created":"2023-08-22T14:05:14Z","article_processing_charge":"No","department":[{"_id":"FrLo"}],"conference":{"name":"IJCAI: International Joint Conference on Artificial Intelligence","location":"Montreal, Canada","end_date":"2021-08-27","start_date":"2021-08-19"},"page":"2337-2343","year":"2021","publication":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","external_id":{"arxiv":["2105.09240"]},"title":"Boosting variational inference with locally adaptive step-sizes","day":"19","arxiv":1,"oa_version":"Published Version","date_updated":"2023-09-11T11:14:30Z","oa":1,"language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2105.09240"}],"month":"05","doi":"10.24963/ijcai.2021/322","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","abstract":[{"lang":"eng","text":"Variational Inference makes a trade-off between the capacity of the variational family and the tractability of finding an approximate posterior distribution. Instead, Boosting Variational Inference allows practitioners to obtain increasingly good posterior approximations by spending more compute. The main obstacle to widespread adoption of Boosting Variational Inference is the amount of resources necessary to improve over a strong Variational Inference baseline. In our work, we trace this limitation back to the global curvature of the KL-divergence. We characterize how the global curvature impacts time and memory consumption, address the problem with the notion of local curvature, and provide a novel approximate backtracking algorithm for estimating local curvature. We give new theoretical convergence rates for our algorithms and provide experimental validation on synthetic and real-world datasets."}],"extern":"1","status":"public","date_published":"2021-05-19T00:00:00Z"},{"main_file_link":[{"url":"https://arxiv.org/abs/2107.01057","open_access":"1"}],"month":"07","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","abstract":[{"text":"When machine learning systems meet real world applications, accuracy is only\r\none of several requirements. In this paper, we assay a complementary\r\nperspective originating from the increasing availability of pre-trained and\r\nregularly improving state-of-the-art models. While new improved models develop\r\nat a fast pace, downstream tasks vary more slowly or stay constant. Assume that\r\nwe have a large unlabelled data set for which we want to maintain accurate\r\npredictions. Whenever a new and presumably better ML models becomes available,\r\nwe encounter two problems: (i) given a limited budget, which data points should\r\nbe re-evaluated using the new model?; and (ii) if the new predictions differ\r\nfrom the current ones, should we update? Problem (i) is about compute cost,\r\nwhich matters for very large data sets and models. Problem (ii) is about\r\nmaintaining consistency of the predictions, which can be highly relevant for\r\ndownstream applications; our demand is to avoid negative flips, i.e., changing\r\ncorrect to incorrect predictions. In this paper, we formalize the Prediction\r\nUpdate Problem and present an efficient probabilistic approach as answer to the\r\nabove questions. In extensive experiments on standard classification benchmark\r\ndata sets, we show that our method outperforms alternative strategies along key\r\nmetrics for backward-compatible prediction updates.","lang":"eng"}],"extern":"1","status":"public","date_published":"2021-07-02T00:00:00Z","day":"02","arxiv":1,"oa_version":"Preprint","date_updated":"2023-09-11T11:31:59Z","oa":1,"language":[{"iso":"eng"}],"publication_identifier":{"isbn":["9781713845393"]},"date_created":"2023-08-22T14:05:41Z","article_processing_charge":"No","department":[{"_id":"FrLo"}],"conference":{"location":"Virtual","start_date":"2021-12-07","end_date":"2021-12-10","name":"NeurIPS: Neural Information Processing Systems"},"page":"116-128","year":"2021","publication":"35th Conference on Neural Information Processing Systems","external_id":{"arxiv":["2107.01057"]},"title":"Backward-compatible prediction updates: A probabilistic approach","volume":34,"intvolume":"        34","type":"conference","citation":{"chicago":"Träuble, Frederik, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, and Peter Gehler. “Backward-Compatible Prediction Updates: A Probabilistic Approach.” In <i>35th Conference on Neural Information Processing Systems</i>, 34:116–28, 2021.","apa":"Träuble, F., Kügelgen, J. von, Kleindessner, M., Locatello, F., Schölkopf, B., &#38; Gehler, P. (2021). Backward-compatible prediction updates: A probabilistic approach. In <i>35th Conference on Neural Information Processing Systems</i> (Vol. 34, pp. 116–128). Virtual.","short":"F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, P. Gehler, in:, 35th Conference on Neural Information Processing Systems, 2021, pp. 116–128.","ama":"Träuble F, Kügelgen J von, Kleindessner M, Locatello F, Schölkopf B, Gehler P. Backward-compatible prediction updates: A probabilistic approach. In: <i>35th Conference on Neural Information Processing Systems</i>. Vol 34. ; 2021:116-128.","ieee":"F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, and P. Gehler, “Backward-compatible prediction updates: A probabilistic approach,” in <i>35th Conference on Neural Information Processing Systems</i>, Virtual, 2021, vol. 34, pp. 116–128.","ista":"Träuble F, Kügelgen J von, Kleindessner M, Locatello F, Schölkopf B, Gehler P. 2021. Backward-compatible prediction updates: A probabilistic approach. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 116–128.","mla":"Träuble, Frederik, et al. “Backward-Compatible Prediction Updates: A Probabilistic Approach.” <i>35th Conference on Neural Information Processing Systems</i>, vol. 34, 2021, pp. 116–28."},"quality_controlled":"1","author":[{"full_name":"Träuble, Frederik","first_name":"Frederik","last_name":"Träuble"},{"full_name":"Kügelgen, Julius von","last_name":"Kügelgen","first_name":"Julius von"},{"last_name":"Kleindessner","first_name":"Matthäus","full_name":"Kleindessner, Matthäus"},{"first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco"},{"first_name":"Bernhard","last_name":"Schölkopf","full_name":"Schölkopf, Bernhard"},{"last_name":"Gehler","first_name":"Peter","full_name":"Gehler, Peter"}],"_id":"14182"},{"OA_place":"repository","abstract":[{"text":"A method involves receiving a perceptual representation including a plurality of feature vectors, and initializing a plurality of slot vectors represented by a neural network memory unit. Each respective slot vector is configured to represent a corresponding entity in the perceptual representation. The method also involves determining an attention matrix based on a product of the plurality of feature vectors transformed by a key function and the plurality of slot vectors transformed by a query function. Each respective value of a plurality of values along each respective dimension of the attention matrix is normalized with respect to the plurality of values. The method additionally involves determining an update matrix based on the plurality of feature vectors transformed by a value function and the attention matrix, and updating the plurality of slot vectors based on the update matrix by way of the neural network memory unit.","lang":"eng"}],"extern":"1","status":"public","year":"2021","applicant":["Google LLC"],"title":"Object-centric learning with slot attention","external_id":{"arxiv":["2006.15055"]},"date_published":"2021-12-09T00:00:00Z","main_file_link":[{"url":"https://patents.google.com/patent/US20210383199A1/en","open_access":"1"}],"date_created":"2023-08-22T14:07:06Z","application_date":"2020-07-13","month":"12","article_processing_charge":"No","department":[{"_id":"FrLo"}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","citation":{"chicago":"Weissenborn, Dirk, Jakob Uszkoreit, Thomas Unterthiner, Aravindh Mahendran, Francesco Locatello, Thomas Kipf, Georg Heigold, and Alexey Dosovitskiy. “Object-Centric Learning with Slot Attention,” 2021.","short":"D. Weissenborn, J. Uszkoreit, T. Unterthiner, A. Mahendran, F. Locatello, T. Kipf, G. Heigold, A. Dosovitskiy, (2021).","apa":"Weissenborn, D., Uszkoreit, J., Unterthiner, T., Mahendran, A., Locatello, F., Kipf, T., … Dosovitskiy, A. (2021). Object-centric learning with slot attention.","ama":"Weissenborn D, Uszkoreit J, Unterthiner T, et al. Object-centric learning with slot attention. 2021.","mla":"Weissenborn, Dirk, et al. <i>Object-Centric Learning with Slot Attention</i>. 2021.","ista":"Weissenborn D, Uszkoreit J, Unterthiner T, Mahendran A, Locatello F, Kipf T, Heigold G, Dosovitskiy A. 2021. Object-centric learning with slot attention.","ieee":"D. Weissenborn <i>et al.</i>, “Object-centric learning with slot attention.” 2021."},"publication_date":"2021-12-09","arxiv":1,"oa_version":"Published Version","date_updated":"2025-01-31T11:35:46Z","ipn":"US20210383199A1","author":[{"first_name":"Dirk","last_name":"Weissenborn","full_name":"Weissenborn, Dirk"},{"first_name":"Jakob","last_name":"Uszkoreit","full_name":"Uszkoreit, Jakob"},{"first_name":"Thomas","last_name":"Unterthiner","full_name":"Unterthiner, Thomas"},{"full_name":"Mahendran, Aravindh","first_name":"Aravindh","last_name":"Mahendran"},{"full_name":"Locatello, Francesco","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","first_name":"Francesco"},{"full_name":"Kipf, Thomas","first_name":"Thomas","last_name":"Kipf"},{"last_name":"Heigold","first_name":"Georg","full_name":"Heigold, Georg"},{"last_name":"Dosovitskiy","first_name":"Alexey","full_name":"Dosovitskiy, Alexey"}],"oa":1,"_id":"14185","day":"09","type":"patent","ipc":"G06N 3/063 ; G06N 3/08 ; G06F 17/16","application_number":"16 / 927,018 "},{"title":"Enforcing and discovering structure in machine learning","external_id":{"arxiv":["2111.13693"]},"date_published":"2021-11-26T00:00:00Z","year":"2021","status":"public","publication":"arXiv","abstract":[{"lang":"eng","text":"The world is structured in countless ways. It may be prudent to enforce corresponding structural properties to a learning algorithm's solution, such as incorporating prior beliefs, natural constraints, or causal structures. Doing so may translate to faster, more accurate, and more flexible models, which may directly relate to real-world impact. In this dissertation, we consider two different research areas that concern structuring a learning algorithm's solution: when the structure is known and when it has to be discovered."}],"publication_status":"submitted","extern":"1","doi":"10.48550/arXiv.2111.13693","department":[{"_id":"FrLo"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2023-08-22T14:23:35Z","month":"11","article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2111.13693"}],"author":[{"orcid":"0000-0002-4850-0683","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","full_name":"Locatello, Francesco"}],"language":[{"iso":"eng"}],"_id":"14221","oa":1,"article_number":"2111.13693","oa_version":"Preprint","date_updated":"2024-10-14T12:27:49Z","citation":{"ama":"Locatello F. Enforcing and discovering structure in machine learning. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2111.13693\">10.48550/arXiv.2111.13693</a>","mla":"Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.” <i>ArXiv</i>, 2111.13693, doi:<a href=\"https://doi.org/10.48550/arXiv.2111.13693\">10.48550/arXiv.2111.13693</a>.","ieee":"F. Locatello, “Enforcing and discovering structure in machine learning,” <i>arXiv</i>. .","ista":"Locatello F. Enforcing and discovering structure in machine learning. arXiv, 2111.13693.","chicago":"Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2111.13693\">https://doi.org/10.48550/arXiv.2111.13693</a>.","short":"F. Locatello, ArXiv (n.d.).","apa":"Locatello, F. (n.d.). Enforcing and discovering structure in machine learning. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2111.13693\">https://doi.org/10.48550/arXiv.2111.13693</a>"},"arxiv":1,"type":"preprint","day":"26"},{"extern":"1","abstract":[{"lang":"eng","text":"Learning data representations that are useful for various downstream tasks is a cornerstone of artificial intelligence. While existing methods are typically evaluated on downstream tasks such as classification or generative image quality, we propose to assess representations through their usefulness in downstream control tasks, such as reaching or pushing objects. By training over 10,000 reinforcement learning policies, we extensively evaluate to what extent different representation properties affect out-of-distribution (OOD) generalization. Finally, we demonstrate zero-shot transfer of these policies from simulation to the real world, without any domain randomization or fine-tuning. This paper aims to establish the first systematic characterization of the usefulness of learned representations for real-world OOD downstream tasks."}],"publication_status":"published","publication":"ICML 2021 Workshop on Unsupervised Reinforcement Learning","status":"public","year":"2021","date_published":"2021-07-23T00:00:00Z","title":"Representation learning for out-of-distribution generalization in reinforcement learning","article_processing_charge":"No","month":"07","date_created":"2023-09-13T12:43:14Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"FrLo"}],"conference":{"name":"ICML: International Conference on Machine Learning","location":"Virtual","end_date":"2021-07-23","start_date":"2021-07-23"},"citation":{"short":"F. Träuble, A. Dittadi, M. Wuthrich, F. Widmaier, P.V. Gehler, O. Winther, F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021.","apa":"Träuble, F., Dittadi, A., Wuthrich, M., Widmaier, F., Gehler, P. V., Winther, O., … Bauer, S. (2021). Representation learning for out-of-distribution generalization in reinforcement learning. In <i>ICML 2021 Workshop on Unsupervised Reinforcement Learning</i>. Virtual.","chicago":"Träuble, Frederik, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer. “Representation Learning for Out-of-Distribution Generalization in Reinforcement Learning.” In <i>ICML 2021 Workshop on Unsupervised Reinforcement Learning</i>, 2021.","ieee":"F. Träuble <i>et al.</i>, “Representation learning for out-of-distribution generalization in reinforcement learning,” in <i>ICML 2021 Workshop on Unsupervised Reinforcement Learning</i>, Virtual, 2021.","ista":"Träuble F, Dittadi A, Wuthrich M, Widmaier F, Gehler PV, Winther O, Locatello F, Bachem O, Schölkopf B, Bauer S. 2021. Representation learning for out-of-distribution generalization in reinforcement learning. ICML 2021 Workshop on Unsupervised Reinforcement Learning. ICML: International Conference on Machine Learning.","mla":"Träuble, Frederik, et al. “Representation Learning for Out-of-Distribution Generalization in Reinforcement Learning.” <i>ICML 2021 Workshop on Unsupervised Reinforcement Learning</i>, 2021.","ama":"Träuble F, Dittadi A, Wuthrich M, et al. Representation learning for out-of-distribution generalization in reinforcement learning. In: <i>ICML 2021 Workshop on Unsupervised Reinforcement Learning</i>. ; 2021."},"date_updated":"2023-09-13T12:44:00Z","quality_controlled":"1","oa_version":"None","language":[{"iso":"eng"}],"_id":"14332","author":[{"full_name":"Träuble, Frederik","last_name":"Träuble","first_name":"Frederik"},{"first_name":"Andrea","last_name":"Dittadi","full_name":"Dittadi, Andrea"},{"first_name":"Manuel","last_name":"Wuthrich","full_name":"Wuthrich, Manuel"},{"full_name":"Widmaier, Felix","first_name":"Felix","last_name":"Widmaier"},{"full_name":"Gehler, Peter Vincent","last_name":"Gehler","first_name":"Peter Vincent"},{"first_name":"Ole","last_name":"Winther","full_name":"Winther, Ole"},{"first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco"},{"full_name":"Bachem, Olivier","last_name":"Bachem","first_name":"Olivier"},{"first_name":"Bernhard","last_name":"Schölkopf","full_name":"Schölkopf, Bernhard"},{"full_name":"Bauer, Stefan","last_name":"Bauer","first_name":"Stefan"}],"day":"23","type":"conference"},{"status":"public","date_published":"2021-03-21T00:00:00Z","publication_status":"published","abstract":[{"lang":"eng","text":"Several problems in planning and reactive synthesis can be reduced to the analysis of two-player quantitative graph games. Optimization is one form of analysis. We argue that in many cases it may be better to replace the optimization problem with the satisficing problem, where instead of searching for optimal solutions, the goal is to search for solutions that adhere to a given threshold bound.\r\nThis work defines and investigates the satisficing problem on a two-player graph game with the discounted-sum cost model. We show that while the satisficing problem can be solved using numerical methods just like the optimization problem, this approach does not render compelling benefits over optimization. When the discount factor is, however, an integer, we present another approach to satisficing, which is purely based on automata methods. We show that this approach is algorithmically more performant – both theoretically and empirically – and demonstrates the broader applicability of satisficing over optimization."}],"ddc":["000"],"doi":"10.1007/978-3-030-72016-2_2","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"month":"03","ec_funded":1,"oa":1,"language":[{"iso":"eng"}],"arxiv":1,"date_updated":"2025-07-10T13:18:02Z","alternative_title":["LNCS"],"oa_version":"Published Version","file":[{"relation":"main_file","content_type":"application/pdf","access_level":"open_access","creator":"dernst","date_updated":"2023-03-28T11:00:33Z","file_name":"2021_LNCS_Bansal.pdf","file_size":747418,"success":1,"checksum":"b020b78b23587ce7610b1aafb4e63438","file_id":"12777","date_created":"2023-03-28T11:00:33Z"}],"license":"https://creativecommons.org/licenses/by/4.0/","day":"21","year":"2021","project":[{"call_identifier":"H2020","grant_number":"863818","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","name":"Formal Methods for Stochastic Models: Algorithms and Applications"}],"publication":"27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems","external_id":{"arxiv":["2101.02594"]},"title":"On satisficing in quantitative games","volume":12651,"page":"20-37","department":[{"_id":"KrCh"}],"conference":{"name":"TACAS: Tools and Algorithms for the Construction and Analysis of Systems","location":"Luxembourg City, Luxembourg","end_date":"2021-04-01","start_date":"2021-03-27"},"publication_identifier":{"issn":["0302-9743"],"eissn":["1611-3349"],"isbn":["9783030720155"]},"date_created":"2023-03-26T22:01:09Z","acknowledgement":"We thank anonymous reviewers for valuable inputs. This work is supported in part by NSF grant 2030859 to the CRA for the CIFellows Project, NSF grants IIS-1527668, CCF-1704883, IIS-1830549, the ERC CoG 863818 (ForM-SMArt), and an award from the Maryland Procurement Office.","article_processing_charge":"No","author":[{"full_name":"Bansal, Suguman","first_name":"Suguman","last_name":"Bansal"},{"full_name":"Chatterjee, Krishnendu","orcid":"0000-0002-4561-241X","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","last_name":"Chatterjee","first_name":"Krishnendu"},{"full_name":"Vardi, Moshe Y.","first_name":"Moshe Y.","last_name":"Vardi"}],"_id":"12767","file_date_updated":"2023-03-28T11:00:33Z","citation":{"mla":"Bansal, Suguman, et al. “On Satisficing in Quantitative Games.” <i>27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems</i>, vol. 12651, Springer Nature, 2021, pp. 20–37, doi:<a href=\"https://doi.org/10.1007/978-3-030-72016-2_2\">10.1007/978-3-030-72016-2_2</a>.","ieee":"S. Bansal, K. Chatterjee, and M. Y. Vardi, “On satisficing in quantitative games,” in <i>27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems</i>, Luxembourg City, Luxembourg, 2021, vol. 12651, pp. 20–37.","ista":"Bansal S, Chatterjee K, Vardi MY. 2021. On satisficing in quantitative games. 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems. TACAS: Tools and Algorithms for the Construction and Analysis of Systems, LNCS, vol. 12651, 20–37.","ama":"Bansal S, Chatterjee K, Vardi MY. On satisficing in quantitative games. In: <i>27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems</i>. Vol 12651. Springer Nature; 2021:20-37. doi:<a href=\"https://doi.org/10.1007/978-3-030-72016-2_2\">10.1007/978-3-030-72016-2_2</a>","apa":"Bansal, S., Chatterjee, K., &#38; Vardi, M. Y. (2021). On satisficing in quantitative games. In <i>27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems</i> (Vol. 12651, pp. 20–37). Luxembourg City, Luxembourg: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-030-72016-2_2\">https://doi.org/10.1007/978-3-030-72016-2_2</a>","short":"S. Bansal, K. Chatterjee, M.Y. Vardi, in:, 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, Springer Nature, 2021, pp. 20–37.","chicago":"Bansal, Suguman, Krishnendu Chatterjee, and Moshe Y. Vardi. “On Satisficing in Quantitative Games.” In <i>27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems</i>, 12651:20–37. Springer Nature, 2021. <a href=\"https://doi.org/10.1007/978-3-030-72016-2_2\">https://doi.org/10.1007/978-3-030-72016-2_2</a>."},"has_accepted_license":"1","scopus_import":"1","quality_controlled":"1","type":"conference","publisher":"Springer Nature","intvolume":"     12651"},{"date_published":"2021-08-25T00:00:00Z","title":"Source data for the manuscript \"Theory of branching morphogenesis by local interactions and global guidance\"","status":"public","year":"2021","ddc":["570"],"abstract":[{"lang":"eng","text":"The zip file includes source data used in the main text of the manuscript \"Theory of branching morphogenesis by local interactions and global guidance\", as well as a representative Jupyter notebook to reproduce the main figures. A sample script for the simulations of branching and annihilating random walks is also included (Sample_script_for_simulations_of_BARWs.ipynb) to generate exemplary branched networks under external guidance. A detailed description of the simulation setup is provided in the supplementary information of the manuscipt."}],"related_material":{"record":[{"status":"public","id":"10402","relation":"used_in_publication"}]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","doi":"10.5281/ZENODO.5257160","department":[{"_id":"EdHa"}],"article_processing_charge":"No","month":"08","date_created":"2023-05-23T13:46:34Z","tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"main_file_link":[{"url":"https://doi.org/10.5281/zenodo.5257161","open_access":"1"}],"oa":1,"_id":"13058","author":[{"full_name":"Ucar, Mehmet C","first_name":"Mehmet C","orcid":"0000-0003-0506-4217","id":"50B2A802-6007-11E9-A42B-EB23E6697425","last_name":"Ucar"}],"date_updated":"2025-04-15T06:54:54Z","oa_version":"Published Version","citation":{"chicago":"Ucar, Mehmet C. “Source Data for the Manuscript ‘Theory of Branching Morphogenesis by Local Interactions and Global Guidance.’” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5257160\">https://doi.org/10.5281/ZENODO.5257160</a>.","short":"M.C. Ucar, (2021).","apa":"Ucar, M. C. (2021). Source data for the manuscript “Theory of branching morphogenesis by local interactions and global guidance.” Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5257160\">https://doi.org/10.5281/ZENODO.5257160</a>","ama":"Ucar MC. Source data for the manuscript “Theory of branching morphogenesis by local interactions and global guidance.” 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5257160\">10.5281/ZENODO.5257160</a>","mla":"Ucar, Mehmet C. <i>Source Data for the Manuscript “Theory of Branching Morphogenesis by Local Interactions and Global Guidance.”</i> Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5257160\">10.5281/ZENODO.5257160</a>.","ista":"Ucar MC. 2021. Source data for the manuscript ‘Theory of branching morphogenesis by local interactions and global guidance’, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5257160\">10.5281/ZENODO.5257160</a>.","ieee":"M. C. Ucar, “Source data for the manuscript ‘Theory of branching morphogenesis by local interactions and global guidance.’” Zenodo, 2021."},"corr_author":"1","type":"research_data_reference","day":"25","publisher":"Zenodo"},{"license":"https://creativecommons.org/publicdomain/zero/1.0/","corr_author":"1","type":"research_data_reference","day":"29","publisher":"Dryad","author":[{"full_name":"Casillas Perez, Barbara E","last_name":"Casillas Perez","id":"351ED2AA-F248-11E8-B48F-1D18A9856A87","first_name":"Barbara E"},{"full_name":"Pull, Christopher","orcid":"0000-0003-1122-3982","last_name":"Pull","id":"3C7F4840-F248-11E8-B48F-1D18A9856A87","first_name":"Christopher"},{"first_name":"Filip","last_name":"Naiser","full_name":"Naiser, Filip"},{"full_name":"Naderlinger, Elisabeth","first_name":"Elisabeth","last_name":"Naderlinger"},{"first_name":"Jiri","last_name":"Matas","full_name":"Matas, Jiri"},{"full_name":"Cremer, Sylvia","first_name":"Sylvia","orcid":"0000-0002-2193-3868","last_name":"Cremer","id":"2F64EC8C-F248-11E8-B48F-1D18A9856A87"}],"_id":"13061","oa":1,"ec_funded":1,"date_updated":"2025-04-14T13:55:31Z","oa_version":"Published Version","citation":{"chicago":"Casillas Perez, Barbara E, Christopher Pull, Filip Naiser, Elisabeth Naderlinger, Jiri Matas, and Sylvia Cremer. “Early Queen Infection Shapes Developmental Dynamics and Induces Long-Term Disease Protection in Incipient Ant Colonies.” Dryad, 2021. <a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">https://doi.org/10.5061/DRYAD.7PVMCVDTJ</a>.","apa":"Casillas Perez, B. E., Pull, C., Naiser, F., Naderlinger, E., Matas, J., &#38; Cremer, S. (2021). Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">https://doi.org/10.5061/DRYAD.7PVMCVDTJ</a>","short":"B.E. Casillas Perez, C. Pull, F. Naiser, E. Naderlinger, J. Matas, S. Cremer, (2021).","ama":"Casillas Perez BE, Pull C, Naiser F, Naderlinger E, Matas J, Cremer S. Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies. 2021. doi:<a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">10.5061/DRYAD.7PVMCVDTJ</a>","ista":"Casillas Perez BE, Pull C, Naiser F, Naderlinger E, Matas J, Cremer S. 2021. Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">10.5061/DRYAD.7PVMCVDTJ</a>.","mla":"Casillas Perez, Barbara E., et al. <i>Early Queen Infection Shapes Developmental Dynamics and Induces Long-Term Disease Protection in Incipient Ant Colonies</i>. Dryad, 2021, doi:<a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">10.5061/DRYAD.7PVMCVDTJ</a>.","ieee":"B. E. Casillas Perez, C. Pull, F. Naiser, E. Naderlinger, J. Matas, and S. Cremer, “Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies.” Dryad, 2021."},"department":[{"_id":"SyCr"}],"doi":"10.5061/DRYAD.7PVMCVDTJ","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2023-05-23T16:14:35Z","tmp":{"legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","name":"Creative Commons Public Domain Dedication (CC0 1.0)","image":"/images/cc_0.png","short":"CC0 (1.0)"},"article_processing_charge":"No","month":"10","main_file_link":[{"url":"https://doi.org/10.5061/dryad.7pvmcvdtj","open_access":"1"}],"title":"Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies","date_published":"2021-10-29T00:00:00Z","status":"public","year":"2021","project":[{"_id":"2649B4DE-B435-11E9-9278-68D0E5697425","name":"Epidemics in ant societies on a chip","grant_number":"771402","call_identifier":"H2020"}],"abstract":[{"text":"Infections early in life can have enduring effects on an organism’s development and immunity. In this study, we show that this equally applies to developing “superorganisms” – incipient social insect colonies. When we exposed newly mated Lasius niger ant queens to a low pathogen dose, their colonies grew more slowly than controls before winter, but reached similar sizes afterwards. Independent of exposure, queen hibernation survival improved when the ratio of pupae to workers was small. Queens that reared fewer pupae before worker emergence exhibited lower pathogen levels, indicating that high brood rearing efforts interfere with the ability of the queen’s immune system to suppress pathogen proliferation. Early-life queen pathogen-exposure also improved the immunocompetence of her worker offspring, as demonstrated by challenging the workers to the same pathogen a year later. Transgenerational transfer of the queen’s pathogen experience to her workforce can hence durably reduce the disease susceptibility of the whole superorganism.","lang":"eng"}],"ddc":["570"],"related_material":{"record":[{"id":"10284","status":"public","relation":"used_in_publication"}]}},{"date_updated":"2025-06-12T06:35:39Z","oa_version":"Published Version","citation":{"chicago":"Szep, Eniko, Himani Sachdeva, and Nicholas H Barton. “Supplementary Code for: Polygenic Local Adaptation in Metapopulations: A Stochastic Eco-Evolutionary Model.” Dryad, 2021. <a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">https://doi.org/10.5061/DRYAD.8GTHT76P1</a>.","apa":"Szep, E., Sachdeva, H., &#38; Barton, N. H. (2021). Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">https://doi.org/10.5061/DRYAD.8GTHT76P1</a>","short":"E. Szep, H. Sachdeva, N.H. Barton, (2021).","ama":"Szep E, Sachdeva H, Barton NH. Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model. 2021. doi:<a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">10.5061/DRYAD.8GTHT76P1</a>","ieee":"E. Szep, H. Sachdeva, and N. H. Barton, “Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model.” Dryad, 2021.","ista":"Szep E, Sachdeva H, Barton NH. 2021. Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">10.5061/DRYAD.8GTHT76P1</a>.","mla":"Szep, Eniko, et al. <i>Supplementary Code for: Polygenic Local Adaptation in Metapopulations: A Stochastic Eco-Evolutionary Model</i>. Dryad, 2021, doi:<a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">10.5061/DRYAD.8GTHT76P1</a>."},"author":[{"full_name":"Szep, Eniko","first_name":"Eniko","id":"485BB5A4-F248-11E8-B48F-1D18A9856A87","last_name":"Szep"},{"full_name":"Sachdeva, Himani","first_name":"Himani","id":"42377A0A-F248-11E8-B48F-1D18A9856A87","last_name":"Sachdeva"},{"first_name":"Nicholas H","orcid":"0000-0002-8548-5240","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","last_name":"Barton","full_name":"Barton, Nicholas H"}],"oa":1,"_id":"13062","day":"02","publisher":"Dryad","corr_author":"1","type":"research_data_reference","abstract":[{"text":"This paper analyzes the conditions for local adaptation in a metapopulation with infinitely many islands under a model of hard selection, where population size depends on local fitness. Each island belongs to one of two distinct ecological niches or habitats. Fitness is influenced by an additive trait which is under habitat-dependent directional selection. Our analysis is based on the diffusion approximation and  accounts for both genetic drift and demographic stochasticity. By neglecting linkage disequilibria, it yields the joint distribution of allele frequencies and population size on each island. We find that under hard selection, the conditions for local adaptation in a rare habitat are more restrictive for more polygenic traits: even moderate migration load per locus at very many loci is sufficient for population sizes to decline. This further reduces the efficacy of selection at individual loci due to increased drift and because smaller populations are more prone to swamping due to migration, causing a positive feedback between increasing maladaptation and declining population sizes. Our analysis also highlights the importance of demographic stochasticity, which  exacerbates the decline in numbers of maladapted populations, leading to population collapse in the rare habitat at significantly lower migration than predicted by deterministic arguments.","lang":"eng"}],"ddc":["570"],"related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"9252"}]},"title":"Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model","date_published":"2021-03-02T00:00:00Z","year":"2021","status":"public","date_created":"2023-05-23T16:17:02Z","tmp":{"legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","name":"Creative Commons Public Domain Dedication (CC0 1.0)","image":"/images/cc_0.png","short":"CC0 (1.0)"},"article_processing_charge":"No","month":"03","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5061/dryad.8gtht76p1"}],"doi":"10.5061/DRYAD.8GTHT76P1","department":[{"_id":"NiBa"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"corr_author":"1","type":"research_data_reference","day":"04","publisher":"Dryad","oa":1,"_id":"13063","author":[{"full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","first_name":"Matthew Richard"}],"date_updated":"2025-06-12T06:54:51Z","oa_version":"Published Version","citation":{"ieee":"M. R. Robinson, “Probabilistic inference of the genetic architecture of functional enrichment of complex traits.” Dryad, 2021.","mla":"Robinson, Matthew Richard. <i>Probabilistic Inference of the Genetic Architecture of Functional Enrichment of Complex Traits</i>. Dryad, 2021, doi:<a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>.","ista":"Robinson MR. 2021. Probabilistic inference of the genetic architecture of functional enrichment of complex traits, Dryad, <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>.","ama":"Robinson MR. Probabilistic inference of the genetic architecture of functional enrichment of complex traits. 2021. doi:<a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>","apa":"Robinson, M. R. (2021). Probabilistic inference of the genetic architecture of functional enrichment of complex traits. Dryad. <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">https://doi.org/10.5061/dryad.sqv9s4n51</a>","short":"M.R. Robinson, (2021).","chicago":"Robinson, Matthew Richard. “Probabilistic Inference of the Genetic Architecture of Functional Enrichment of Complex Traits.” Dryad, 2021. <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">https://doi.org/10.5061/dryad.sqv9s4n51</a>."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"MaRo"}],"doi":"10.5061/dryad.sqv9s4n51","article_processing_charge":"No","month":"11","date_created":"2023-05-23T16:20:16Z","tmp":{"legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","name":"Creative Commons Public Domain Dedication (CC0 1.0)","image":"/images/cc_0.png","short":"CC0 (1.0)"},"main_file_link":[{"url":"https://doi.org/10.5061/dryad.sqv9s4n51","open_access":"1"}],"date_published":"2021-11-04T00:00:00Z","title":"Probabilistic inference of the genetic architecture of functional enrichment of complex traits","year":"2021","status":"public","ddc":["570"],"abstract":[{"lang":"eng","text":"We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only $\\leq$ 10\\% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32-44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having &gt;95% probability of contributing &gt;0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data."}],"related_material":{"link":[{"relation":"software","url":"https://github.com/medical-genomics-group/gmrm"}],"record":[{"id":"8429","status":"public","relation":"used_in_publication"}]}},{"doi":"10.5281/ZENODO.5148117","department":[{"_id":"EdHa"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2023-05-23T16:39:24Z","tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"month":"07","article_processing_charge":"No","main_file_link":[{"url":"https://doi.org/10.5281/zenodo.6577226","open_access":"1"}],"title":"Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids","date_published":"2021-07-30T00:00:00Z","status":"public","year":"2021","abstract":[{"text":"Source data and source code for the graphs in \"Spatiotemporal dynamics of self-organized branching pancreatic cancer-derived organoids\".","lang":"eng"}],"ddc":["570"],"related_material":{"record":[{"id":"12217","status":"public","relation":"used_in_publication"}]},"type":"research_data_reference","day":"30","publisher":"Zenodo","author":[{"full_name":"Randriamanantsoa, Samuel","last_name":"Randriamanantsoa","first_name":"Samuel"},{"full_name":"Papargyriou, Aristeidis","first_name":"Aristeidis","last_name":"Papargyriou"},{"full_name":"Maurer, Carlo","first_name":"Carlo","last_name":"Maurer"},{"full_name":"Peschke, Katja","last_name":"Peschke","first_name":"Katja"},{"last_name":"Schuster","first_name":"Maximilian","full_name":"Schuster, Maximilian"},{"full_name":"Zecchin, Giulia","last_name":"Zecchin","first_name":"Giulia"},{"full_name":"Steiger, Katja","first_name":"Katja","last_name":"Steiger"},{"first_name":"Rupert","last_name":"Öllinger","full_name":"Öllinger, Rupert"},{"last_name":"Saur","first_name":"Dieter","full_name":"Saur, Dieter"},{"last_name":"Scheel","first_name":"Christina","full_name":"Scheel, Christina"},{"full_name":"Rad, Roland","last_name":"Rad","first_name":"Roland"},{"full_name":"Hannezo, Edouard B","orcid":"0000-0001-6005-1561","id":"3A9DB764-F248-11E8-B48F-1D18A9856A87","last_name":"Hannezo","first_name":"Edouard B"},{"first_name":"Maximilian","last_name":"Reichert","full_name":"Reichert, Maximilian"},{"full_name":"Bausch, Andreas R.","last_name":"Bausch","first_name":"Andreas R."}],"_id":"13068","oa":1,"oa_version":"Published Version","date_updated":"2025-06-11T13:53:54Z","citation":{"chicago":"Randriamanantsoa, Samuel, Aristeidis Papargyriou, Carlo Maurer, Katja Peschke, Maximilian Schuster, Giulia Zecchin, Katja Steiger, et al. “Spatiotemporal Dynamics of Self-Organized Branching in Pancreas-Derived Organoids.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5148117\">https://doi.org/10.5281/ZENODO.5148117</a>.","apa":"Randriamanantsoa, S., Papargyriou, A., Maurer, C., Peschke, K., Schuster, M., Zecchin, G., … Bausch, A. R. (2021). Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5148117\">https://doi.org/10.5281/ZENODO.5148117</a>","short":"S. Randriamanantsoa, A. Papargyriou, C. Maurer, K. Peschke, M. Schuster, G. Zecchin, K. Steiger, R. Öllinger, D. Saur, C. Scheel, R. Rad, E.B. Hannezo, M. Reichert, A.R. Bausch, (2021).","ama":"Randriamanantsoa S, Papargyriou A, Maurer C, et al. Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5148117\">10.5281/ZENODO.5148117</a>","mla":"Randriamanantsoa, Samuel, et al. <i>Spatiotemporal Dynamics of Self-Organized Branching in Pancreas-Derived Organoids</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5148117\">10.5281/ZENODO.5148117</a>.","ista":"Randriamanantsoa S, Papargyriou A, Maurer C, Peschke K, Schuster M, Zecchin G, Steiger K, Öllinger R, Saur D, Scheel C, Rad R, Hannezo EB, Reichert M, Bausch AR. 2021. Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5148117\">10.5281/ZENODO.5148117</a>.","ieee":"S. Randriamanantsoa <i>et al.</i>, “Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids.” Zenodo, 2021."}},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"MaDe"}],"doi":"10.5281/ZENODO.5519410","main_file_link":[{"url":"https://doi.org/10.5281/zenodo.5547464","open_access":"1"}],"article_processing_charge":"No","month":"12","tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_created":"2023-05-23T16:40:56Z","status":"public","year":"2021","date_published":"2021-12-25T00:00:00Z","title":"Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans","related_material":{"record":[{"id":"10322","status":"public","relation":"used_in_publication"}]},"ddc":["570"],"abstract":[{"text":"To survive elevated temperatures, ectotherms adjust the fluidity of membranes by fine-tuning lipid desaturation levels in a process previously described to be cell-autonomous. We have discovered that, in Caenorhabditis elegans, neuronal Heat shock Factor 1 (HSF-1), the conserved master regulator of the heat shock response (HSR)- causes extensive fat remodelling in peripheral tissues. These changes include a decrease in fat desaturase and acid lipase expression in the intestine, and a global shift in the saturation levels of plasma membrane’s phospholipids. The observed remodelling of plasma membrane is in line with ectothermic adaptive responses and gives worms a cumulative advantage to warm temperatures. We have determined that at least six TAX-2/TAX-4 cGMP gated channel expressing sensory neurons and TGF-β/BMP are required for signalling across tissues to modulate fat desaturation. We also find neuronal hsf-1  is not only sufficient but also partially necessary to control the fat remodelling response and for survival at warm temperatures. This is the first study to show that a thermostat-based mechanism can cell non-autonomously coordinate membrane saturation and composition across tissues in a multicellular animal.","lang":"eng"}],"type":"research_data_reference","publisher":"Zenodo","day":"25","_id":"13069","oa":1,"author":[{"full_name":"Chauve, Laetitia","last_name":"Chauve","first_name":"Laetitia"},{"first_name":"Francesca","last_name":"Hodge","full_name":"Hodge, Francesca"},{"full_name":"Murdoch, Sharlene","first_name":"Sharlene","last_name":"Murdoch"},{"first_name":"Fatemah","last_name":"Masoudzadeh","full_name":"Masoudzadeh, Fatemah"},{"first_name":"Harry-Jack","last_name":"Mann","full_name":"Mann, Harry-Jack"},{"first_name":"Andrea","last_name":"Lopez-Clavijo","full_name":"Lopez-Clavijo, Andrea"},{"full_name":"Okkenhaug, Hanneke","last_name":"Okkenhaug","first_name":"Hanneke"},{"full_name":"West, Greg","last_name":"West","first_name":"Greg"},{"full_name":"Sousa, Bebiana C.","first_name":"Bebiana C.","last_name":"Sousa"},{"full_name":"Segonds-Pichon, Anne","first_name":"Anne","last_name":"Segonds-Pichon"},{"full_name":"Li, Cheryl","first_name":"Cheryl","last_name":"Li"},{"first_name":"Steven","last_name":"Wingett","full_name":"Wingett, Steven"},{"full_name":"Kienberger, Hermine","first_name":"Hermine","last_name":"Kienberger"},{"full_name":"Kleigrewe, Karin","last_name":"Kleigrewe","first_name":"Karin"},{"first_name":"Mario","id":"4E3FF80E-F248-11E8-B48F-1D18A9856A87","last_name":"de Bono","orcid":"0000-0001-8347-0443","full_name":"de Bono, Mario"},{"last_name":"Wakelam","first_name":"Michael","full_name":"Wakelam, Michael"},{"full_name":"Casanueva, Olivia","last_name":"Casanueva","first_name":"Olivia"}],"citation":{"mla":"Chauve, Laetitia, et al. <i>Neuronal HSF-1 Coordinates the Propagation of Fat Desaturation across Tissues to Enable Adaptation to High Temperatures in C. Elegans</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5519410\">10.5281/ZENODO.5519410</a>.","ista":"Chauve L, Hodge F, Murdoch S, Masoudzadeh F, Mann H-J, Lopez-Clavijo A, Okkenhaug H, West G, Sousa BC, Segonds-Pichon A, Li C, Wingett S, Kienberger H, Kleigrewe K, de Bono M, Wakelam M, Casanueva O. 2021. Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5519410\">10.5281/ZENODO.5519410</a>.","ieee":"L. Chauve <i>et al.</i>, “Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans.” Zenodo, 2021.","ama":"Chauve L, Hodge F, Murdoch S, et al. Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5519410\">10.5281/ZENODO.5519410</a>","short":"L. Chauve, F. Hodge, S. Murdoch, F. Masoudzadeh, H.-J. Mann, A. Lopez-Clavijo, H. Okkenhaug, G. West, B.C. Sousa, A. Segonds-Pichon, C. Li, S. Wingett, H. Kienberger, K. Kleigrewe, M. de Bono, M. Wakelam, O. Casanueva, (2021).","apa":"Chauve, L., Hodge, F., Murdoch, S., Masoudzadeh, F., Mann, H.-J., Lopez-Clavijo, A., … Casanueva, O. (2021). Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5519410\">https://doi.org/10.5281/ZENODO.5519410</a>","chicago":"Chauve, Laetitia, Francesca Hodge, Sharlene Murdoch, Fatemah Masoudzadeh, Harry-Jack Mann, Andrea Lopez-Clavijo, Hanneke Okkenhaug, et al. “Neuronal HSF-1 Coordinates the Propagation of Fat Desaturation across Tissues to Enable Adaptation to High Temperatures in C. Elegans.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5519410\">https://doi.org/10.5281/ZENODO.5519410</a>."},"oa_version":"Published Version","date_updated":"2023-08-14T11:53:26Z"},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","doi":"10.5281/ZENODO.5794028","department":[{"_id":"MaRo"}],"article_processing_charge":"No","month":"12","date_created":"2023-05-23T16:46:20Z","tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"main_file_link":[{"url":"https://doi.org/10.5281/zenodo.5794029","open_access":"1"}],"date_published":"2021-12-20T00:00:00Z","title":"Blood-based epigenome-wide analyses of cognitive abilities","year":"2021","status":"public","ddc":["570"],"abstract":[{"lang":"eng","text":"CpGs and corresponding mean weights for DNAm-based prediction of cognitive abilities (6 traits)"}],"related_material":{"record":[{"status":"public","id":"10702","relation":"used_in_publication"}]},"type":"research_data_reference","day":"20","publisher":"Zenodo","_id":"13072","oa":1,"author":[{"full_name":"McCartney, Daniel L","last_name":"McCartney","first_name":"Daniel L"},{"full_name":"Hillary, Robert F","last_name":"Hillary","first_name":"Robert F"},{"first_name":"Eleanor LS","last_name":"Conole","full_name":"Conole, Eleanor LS"},{"first_name":"Daniel","last_name":"Trejo Banos","full_name":"Trejo Banos, Daniel"},{"last_name":"Gadd","first_name":"Danni A","full_name":"Gadd, Danni A"},{"full_name":"Walker, Rosie M","first_name":"Rosie M","last_name":"Walker"},{"first_name":"Cliff","last_name":"Nangle","full_name":"Nangle, Cliff"},{"full_name":"Flaig, Robin","last_name":"Flaig","first_name":"Robin"},{"last_name":"Campbell","first_name":"Archie","full_name":"Campbell, Archie"},{"last_name":"Murray","first_name":"Alison D","full_name":"Murray, Alison D"},{"full_name":"Munoz Maniega, Susana","first_name":"Susana","last_name":"Munoz Maniega"},{"first_name":"Maria","last_name":"del C Valdes-Hernandez","full_name":"del C Valdes-Hernandez, Maria"},{"full_name":"Harris, Mathew A","first_name":"Mathew A","last_name":"Harris"},{"first_name":"Mark E","last_name":"Bastin","full_name":"Bastin, Mark E"},{"first_name":"Joanna M","last_name":"Wardlaw","full_name":"Wardlaw, Joanna M"},{"full_name":"Harris, Sarah E","last_name":"Harris","first_name":"Sarah E"},{"first_name":"David J","last_name":"Porteous","full_name":"Porteous, David J"},{"full_name":"Tucker-Drob, Elliot M","last_name":"Tucker-Drob","first_name":"Elliot M"},{"first_name":"Andrew M","last_name":"McIntosh","full_name":"McIntosh, Andrew M"},{"last_name":"Evans","first_name":"Kathryn L","full_name":"Evans, Kathryn L"},{"last_name":"Deary","first_name":"Ian J","full_name":"Deary, Ian J"},{"full_name":"Cox, Simon R","last_name":"Cox","first_name":"Simon R"},{"full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","orcid":"0000-0001-8982-8813","first_name":"Matthew Richard"},{"first_name":"Riccardo E","last_name":"Marioni","full_name":"Marioni, Riccardo E"}],"oa_version":"Published Version","date_updated":"2025-06-11T13:54:53Z","citation":{"mla":"McCartney, Daniel L., et al. <i>Blood-Based Epigenome-Wide Analyses of Cognitive Abilities</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5794028\">10.5281/ZENODO.5794028</a>.","ista":"McCartney DL, Hillary RF, Conole EL, Trejo Banos D, Gadd DA, Walker RM, Nangle C, Flaig R, Campbell A, Murray AD, Munoz Maniega S, del C Valdes-Hernandez M, Harris MA, Bastin ME, Wardlaw JM, Harris SE, Porteous DJ, Tucker-Drob EM, McIntosh AM, Evans KL, Deary IJ, Cox SR, Robinson MR, Marioni RE. 2021. Blood-based epigenome-wide analyses of cognitive abilities, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5794028\">10.5281/ZENODO.5794028</a>.","ieee":"D. L. McCartney <i>et al.</i>, “Blood-based epigenome-wide analyses of cognitive abilities.” Zenodo, 2021.","ama":"McCartney DL, Hillary RF, Conole EL, et al. Blood-based epigenome-wide analyses of cognitive abilities. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5794028\">10.5281/ZENODO.5794028</a>","apa":"McCartney, D. L., Hillary, R. F., Conole, E. L., Trejo Banos, D., Gadd, D. A., Walker, R. M., … Marioni, R. E. (2021). Blood-based epigenome-wide analyses of cognitive abilities. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5794028\">https://doi.org/10.5281/ZENODO.5794028</a>","short":"D.L. McCartney, R.F. Hillary, E.L. Conole, D. Trejo Banos, D.A. Gadd, R.M. Walker, C. Nangle, R. Flaig, A. Campbell, A.D. Murray, S. Munoz Maniega, M. del C Valdes-Hernandez, M.A. Harris, M.E. Bastin, J.M. Wardlaw, S.E. Harris, D.J. Porteous, E.M. Tucker-Drob, A.M. McIntosh, K.L. Evans, I.J. Deary, S.R. Cox, M.R. Robinson, R.E. Marioni, (2021).","chicago":"McCartney, Daniel L, Robert F Hillary, Eleanor LS Conole, Daniel Trejo Banos, Danni A Gadd, Rosie M Walker, Cliff Nangle, et al. “Blood-Based Epigenome-Wide Analyses of Cognitive Abilities.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5794028\">https://doi.org/10.5281/ZENODO.5794028</a>."}}]