{"alternative_title":["PMLR"],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2503.08849"}],"page":"4591-4599","month":"05","intvolume":"       258","citation":{"ieee":"T. Wegel, F. Kovačević, A. Ţifrea, and F. Yang, “Learning Pareto manifolds in high dimensions: How can regularization help?,” in <i>The 28th International Conference on Artificial Intelligence and Statistics</i>, Mai Khao, Thailand, 2025, vol. 258, pp. 4591–4599.","apa":"Wegel, T., Kovačević, F., Ţifrea, A., &#38; Yang, F. (2025). Learning Pareto manifolds in high dimensions: How can regularization help? In <i>The 28th International Conference on Artificial Intelligence and Statistics</i> (Vol. 258, pp. 4591–4599). Mai Khao, Thailand: ML Research Press.","chicago":"Wegel, Tobias, Filip Kovačević, Alexandru Ţifrea, and Fanny Yang. “Learning Pareto Manifolds in High Dimensions: How Can Regularization Help?” In <i>The 28th International Conference on Artificial Intelligence and Statistics</i>, 258:4591–99. ML Research Press, 2025.","mla":"Wegel, Tobias, et al. “Learning Pareto Manifolds in High Dimensions: How Can Regularization Help?” <i>The 28th International Conference on Artificial Intelligence and Statistics</i>, vol. 258, ML Research Press, 2025, pp. 4591–99.","ama":"Wegel T, Kovačević F, Ţifrea A, Yang F. Learning Pareto manifolds in high dimensions: How can regularization help? In: <i>The 28th International Conference on Artificial Intelligence and Statistics</i>. Vol 258. ML Research Press; 2025:4591-4599.","short":"T. Wegel, F. Kovačević, A. Ţifrea, F. Yang, in:, The 28th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2025, pp. 4591–4599.","ista":"Wegel T, Kovačević F, Ţifrea A, Yang F. 2025. Learning Pareto manifolds in high dimensions: How can regularization help? The 28th International Conference on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and Statistics, PMLR, vol. 258, 4591–4599."},"conference":{"start_date":"2025-05-03","location":"Mai Khao, Thailand","name":"AISTATS: Conference on Artificial Intelligence and Statistics","end_date":"2025-05-05"},"language":[{"iso":"eng"}],"_id":"20300","year":"2025","publication_identifier":{"eissn":["2640-3498"]},"publication":"The 28th International Conference on Artificial Intelligence and Statistics","external_id":{"arxiv":["2503.08849"]},"oa_version":"Preprint","article_processing_charge":"No","department":[{"_id":"MaMo"}],"abstract":[{"lang":"eng","text":"Simultaneously addressing multiple objectives is becoming increasingly important in modern machine learning. At the same time, data is often high-dimensional and costly to label. For a single objective such as prediction risk, conventional regularization techniques are known to improve generalization when the data exhibits low-dimensional structure like sparsity. However, it is largely unexplored how to leverage this structure in the context of multi-objective learning (MOL) with multiple competing objectives. In this work, we discuss how the application of vanilla regularization approaches can fail, and propose a two-stage MOL framework that can successfully leverage low-dimensional structure. We demonstrate its effectiveness experimentally for multi-distribution learning and fairness-risk trade-offs."}],"publisher":"ML Research Press","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","scopus_import":"1","date_created":"2025-09-07T22:01:35Z","acknowledgement":"We thank Junhyung Park for valuable feedback on the manuscript. AT was supported by a PhD fellowship from the Swiss Data Science Center. TW was supported by the SNF Grant 204439. This work was done in part while TW and FY were visiting the Simons Institute for the Theory of\r\nComputing.","quality_controlled":"1","date_published":"2025-05-01T00:00:00Z","volume":258,"OA_place":"repository","date_updated":"2025-09-09T07:00:34Z","author":[{"full_name":"Wegel, Tobias","last_name":"Wegel","first_name":"Tobias"},{"id":"d0258e7b-50b8-11ef-ad56-8b9f537b6b1b","full_name":"Kovačević, Filip","first_name":"Filip","last_name":"Kovačević"},{"full_name":"Ţifrea, Alexandru","last_name":"Ţifrea","first_name":"Alexandru"},{"first_name":"Fanny","last_name":"Yang","full_name":"Yang, Fanny"}],"day":"01","oa":1,"status":"public","arxiv":1,"type":"conference","title":"Learning Pareto manifolds in high dimensions: How can regularization help?","OA_type":"green"}