[{"OA_place":"publisher","article_processing_charge":"Yes (via OA deal)","file":[{"relation":"main_file","file_id":"21888","file_size":3550462,"file_name":"2026_LiverInternational_Sin.pdf","success":1,"date_created":"2026-05-18T07:10:31Z","content_type":"application/pdf","creator":"dernst","access_level":"open_access","checksum":"fafcc0b88b8e8caed85849627305d9ba","date_updated":"2026-05-18T07:10:31Z"}],"volume":46,"publisher":"Wiley","acknowledgement":"The computational results presented were partly obtained using the CLIP cluster (https://clip.science/). The authors thank Clemens Watzenboeck from the Medical University of Vienna for the assistance in code upload and repository maintenance. The authors dedicate this work to the memory of Martin Watzenboeck, who served as first author and whose vision and scientific rigor were fundamental to the conception and completion of this study. Open Access funding provided by Medizinische Universitat Wien/KEMÖ. This work was supported by the Vienna Science and Technology Fund (WWTF) through projects VRG15-005 and NXT 19-008 granted to J.M and the Clinical Research Group MOTION, Medical University of Vienna, Vienna, Austria – a Clinical Research Group Programme project funded by the Ludwig Boltzmann Gesellschaft (Grant Nr LBG_KFG_22_32) with funds from the Fonds Zukunft Österreich.\r\n\r\nP-E.R.'s research laboratory is supported by the Fondation pour la Recherche Médicale (FRM EQU202303016287), “Institut National de la Santé et de la Recherche Médicale” (ATIP AVENIR), the “Agence Nationale de la Recherche” (ANR-18-CE14-0006-01, RHU QUID-NASH, ANR-18-IDEX-0001, ANR-22-CE14-0002) by « Émergence, Ville de Paris », by Fondation ARC, by the European Union's Horizon 2020 research and innovation programme under grant agreement No 847949 and by France 2030 RHU LIVER-TRACK.","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"last_name":"Sin","first_name":"Celine","full_name":"Sin, Celine"},{"first_name":"Martin Luther","last_name":"Watzenboeck","full_name":"Watzenboeck, Martin Luther"},{"full_name":"Iofinova, Eugenia B","last_name":"Iofinova","orcid":"0000-0002-7778-3221","first_name":"Eugenia B","id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117"},{"last_name":"Balcar","first_name":"Lorenz","full_name":"Balcar, Lorenz"},{"first_name":"Georg","last_name":"Semmler","full_name":"Semmler, Georg"},{"last_name":"Scheiner","first_name":"Bernhard","full_name":"Scheiner, Bernhard"},{"last_name":"Lampichler","first_name":"Katharina","full_name":"Lampichler, Katharina"},{"last_name":"Mandorfer","first_name":"Mattias","full_name":"Mandorfer, Mattias"},{"full_name":"Moga, Lucile","first_name":"Lucile","last_name":"Moga"},{"full_name":"Rautou, Pierre‐Emmanuel","first_name":"Pierre‐Emmanuel","last_name":"Rautou"},{"last_name":"Ronot","first_name":"Maxime","full_name":"Ronot, Maxime"},{"full_name":"Menche, Jörg","first_name":"Jörg","last_name":"Menche"},{"first_name":"Thomas","last_name":"Reiberger","full_name":"Reiberger, Thomas"},{"full_name":"Scharitzer, Martina","last_name":"Scharitzer","first_name":"Martina"}],"OA_type":"hybrid","date_updated":"2026-05-18T07:20:20Z","day":"01","ddc":["570"],"status":"public","external_id":{"pmid":["41943460"]},"oa":1,"year":"2026","scopus_import":"1","doi":"10.1111/liv.70633","type":"journal_article","language":[{"iso":"eng"}],"date_created":"2026-05-07T08:51:47Z","file_date_updated":"2026-05-18T07:10:31Z","keyword":["computed tomography","liver","portal hypertension","radiomics","spleen"],"_id":"21839","article_type":"original","publication_status":"published","abstract":[{"text":"Background & Aims: To develop and validate a CT-based radiomics model to assess HVPG and predict a composite endpoint of liver-related events (LRE: decompensation and liver-related death) in patients with cirrhosis.\r\n\r\nMethods: This retrospective study included 357 cirrhosis patients, who received invasive HVPG measurements, 120 liver-healthy controls (training cohort) and 85 and 100 cirrhosis patients (internal and external validation cohorts, respectively), and contrast-enhanced abdominal CTs. After volumetric segmentation of the liver and spleen on CT, Bayesian parameter optimization was used for selection of extracted features and hyperparameter tuning in random forest or elastic net models. Prediction accuracy was evaluated using Pearson correlation coefficients of predicted (’radio-HVPG’) and invasive HVPG. Discrimination between relevant HVPG cut-offs was determined by receiver operating characteristic (ROC) analysis. The predictive value of radio-HVPG and invasive-HVPG for LRE was compared using Cox regression models.\r\n\r\nResults: Radio-HVPG, predicted by an optimized random forest model based on 74 selected CT features, correlated with invasive-HVPG and detected clinically significant portal hypertension (CSPH: HVPG ≥ 10 mmHg) on the internal (Pearson r = 0.63, AUC 0.89 [95% CI: 0.81–0.96]) and external (Pearson r = 0.62, AUC 0.80 [95% CI: 0.64–0.91]) validation cohorts. Radio-HVPG predicted LRE when adjusting for MELD and albumin (adjusted HR: 1.14 [95% CI: 1.04–1.25], p = 0.005) and performed similarly to invasive-HVPG.\r\n\r\nConclusions: Radiomic features accurately predict HVPG in patients with cirrhosis and allow risk stratification for LRE in a radiomics-clinical signature.","lang":"eng"}],"month":"05","date_published":"2026-05-01T00:00:00Z","tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","image":"/images/cc_by_nc_nd.png","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)"},"issue":"5","article_number":"e70633","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","oa_version":"Published Version","has_accepted_license":"1","publication":"Liver International","title":"Radiomics‐based assessment of portal hypertension severity and risk stratification of cirrhotic patients using routine CT scans","pmid":1,"citation":{"ista":"Sin C, Watzenboeck ML, Iofinova EB, Balcar L, Semmler G, Scheiner B, Lampichler K, Mandorfer M, Moga L, Rautou P, Ronot M, Menche J, Reiberger T, Scharitzer M. 2026. Radiomics‐based assessment of portal hypertension severity and risk stratification of cirrhotic patients using routine CT scans. Liver International. 46(5), e70633.","mla":"Sin, Celine, et al. “Radiomics‐based Assessment of Portal Hypertension Severity and Risk Stratification of Cirrhotic Patients Using Routine CT Scans.” <i>Liver International</i>, vol. 46, no. 5, e70633, Wiley, 2026, doi:<a href=\"https://doi.org/10.1111/liv.70633\">10.1111/liv.70633</a>.","chicago":"Sin, Celine, Martin Luther Watzenboeck, Eugenia B Iofinova, Lorenz Balcar, Georg Semmler, Bernhard Scheiner, Katharina Lampichler, et al. “Radiomics‐based Assessment of Portal Hypertension Severity and Risk Stratification of Cirrhotic Patients Using Routine CT Scans.” <i>Liver International</i>. Wiley, 2026. <a href=\"https://doi.org/10.1111/liv.70633\">https://doi.org/10.1111/liv.70633</a>.","short":"C. Sin, M.L. Watzenboeck, E.B. Iofinova, L. Balcar, G. Semmler, B. Scheiner, K. Lampichler, M. Mandorfer, L. Moga, P. Rautou, M. Ronot, J. Menche, T. Reiberger, M. Scharitzer, Liver International 46 (2026).","apa":"Sin, C., Watzenboeck, M. L., Iofinova, E. B., Balcar, L., Semmler, G., Scheiner, B., … Scharitzer, M. (2026). Radiomics‐based assessment of portal hypertension severity and risk stratification of cirrhotic patients using routine CT scans. <i>Liver International</i>. Wiley. <a href=\"https://doi.org/10.1111/liv.70633\">https://doi.org/10.1111/liv.70633</a>","ieee":"C. Sin <i>et al.</i>, “Radiomics‐based assessment of portal hypertension severity and risk stratification of cirrhotic patients using routine CT scans,” <i>Liver International</i>, vol. 46, no. 5. Wiley, 2026.","ama":"Sin C, Watzenboeck ML, Iofinova EB, et al. Radiomics‐based assessment of portal hypertension severity and risk stratification of cirrhotic patients using routine CT scans. <i>Liver International</i>. 2026;46(5). doi:<a href=\"https://doi.org/10.1111/liv.70633\">10.1111/liv.70633</a>"},"quality_controlled":"1","publication_identifier":{"issn":["1478-3223"],"eissn":["1478-3231"]},"intvolume":"        46"},{"publication_identifier":{"issn":["2663-337X"]},"citation":{"short":"E.B. Iofinova, On the Utility and Effects of Efficiency in Artificial Neural Networks, Institute of Science and Technology Austria, 2026.","apa":"Iofinova, E. B. (2026). <i>On the utility and effects of efficiency in artificial neural networks</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT-ISTA-21854\">https://doi.org/10.15479/AT-ISTA-21854</a>","ieee":"E. B. Iofinova, “On the utility and effects of efficiency in artificial neural networks,” Institute of Science and Technology Austria, 2026.","ama":"Iofinova EB. On the utility and effects of efficiency in artificial neural networks. 2026. doi:<a href=\"https://doi.org/10.15479/AT-ISTA-21854\">10.15479/AT-ISTA-21854</a>","ista":"Iofinova EB. 2026. On the utility and effects of efficiency in artificial neural networks. Institute of Science and Technology Austria.","chicago":"Iofinova, Eugenia B. “On the Utility and Effects of Efficiency in Artificial Neural Networks.” Institute of Science and Technology Austria, 2026. <a href=\"https://doi.org/10.15479/AT-ISTA-21854\">https://doi.org/10.15479/AT-ISTA-21854</a>.","mla":"Iofinova, Eugenia B. <i>On the Utility and Effects of Efficiency in Artificial Neural Networks</i>. Institute of Science and Technology Austria, 2026, doi:<a href=\"https://doi.org/10.15479/AT-ISTA-21854\">10.15479/AT-ISTA-21854</a>."},"acknowledged_ssus":[{"_id":"ScienComp"}],"oa_version":"Published Version","title":"On the utility and effects of efficiency in artificial neural networks","corr_author":"1","has_accepted_license":"1","project":[{"name":"Vienna Graduate School on Computational Optimization","_id":"9B9290DE-BA93-11EA-9121-9846C619BF3A","grant_number":"W1260-N35"}],"month":"05","date_published":"2026-05-11T00:00:00Z","department":[{"_id":"GradSch"},{"_id":"DaAl"}],"related_material":{"record":[{"relation":"part_of_dissertation","status":"public","id":"14771"},{"relation":"part_of_dissertation","status":"public","id":"18121"},{"status":"public","relation":"part_of_dissertation","id":"21858"},{"id":"21859","relation":"part_of_dissertation","status":"public"},{"id":"21857","relation":"part_of_dissertation","status":"public"}]},"abstract":[{"lang":"eng","text":"As neural-network-based models grow both in size and popularity, interest has grown in making the models smaller and more efficient to train. To that end, many methods have been proposed to prune models by reducing their number of nonzero parameters. Additionally, parameter-efficient fine-tuning, in which a much smaller number of parameters than the total contained in the model is updated during training, has become very popular, especially in the space of Large Language Models. At the same time, the increasingly routine deployment of machine learning in real-world applications has spurred a drive to make them more trustworthy - in the sense of, among other things, being unbiased, interpretable, and editable. In this thesis, we examine the interplay between efficiency and trustworthiness.\r\n\r\nFirst, we analyze the effects of model pruning on bias in computer vision models, demonstrating that increased sparsity leads to greater bias, largely as a function of increased model uncertainty in marginal cases. Based on this observation, we propose several bias mitigation techniques. Then, we demonstrate that example-specific model pruning can improve model interpretation methods while improving pruning efficiency to make example-specific model pruning feasible in real time. Then, we investigate the effectiveness of parameter-efficient and data-efficient model personalization via fine-tuning, demonstrating that it is highly feasible with very small computational and data resources. Finally, we consider efficiency in editing model knowledge using a custom synthetic data framework, demonstrating that parameter-efficient, low-rank fine-tuning frequently outperforms full-rank fine-tuning, and, additionally, that restricting which model blocks are fine-tuned frequently improves results. Together, the results in this thesis provide new insights and techniques for combining trustworthiness and efficiency during neural network inference and training.\r\n\r\n-----------------“In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of [name of university or educational entity]’s products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink. If applicable, University Microfilms and/or ProQuest Library, or the Archives of Canada may supply single copies of the dissertation.”"}],"_id":"21854","publication_status":"published","supervisor":[{"first_name":"Dan-Adrian","orcid":"0000-0003-3650-940X","last_name":"Alistarh","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian"}],"language":[{"iso":"eng"}],"alternative_title":["ISTA Thesis"],"file_date_updated":"2026-05-13T13:10:48Z","date_created":"2026-05-11T08:43:22Z","doi":"10.15479/AT-ISTA-21854","status":"public","oa":1,"year":"2026","degree_awarded":"PhD","page":"237","type":"dissertation","acknowledgement":"The research in this Ph.D. was funded in whole\r\nor in part by the Austrian Science Fund (FWF) W1260-N35 (Vienna Graduate School for\r\nComputational Optimization). For open access purposes the author has applied a CC BY\r\npublic copyright license to any author accepted manuscript version arising from this submission\r\nwherever possible. Additionally, I am grateful to Alois Schlögl, Waleed Khalid, and the rest of\r\nthe ISTA Scientific Computing team for building and maintaining the infrastructure I used\r\nto run experiments. I’m also deeply grateful to the Alistarh group’s administrative assistant,\r\nChristine Francois, who always deals with our nonsense with common sense and a smile.\r\n","publisher":"Institute of Science and Technology Austria","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","day":"11","date_updated":"2026-05-19T11:20:28Z","ddc":["000"],"author":[{"first_name":"Eugenia B","orcid":"0000-0002-7778-3221","last_name":"Iofinova","id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117","full_name":"Iofinova, Eugenia B"}],"article_processing_charge":"No","file":[{"file_size":28479571,"relation":"source_file","file_id":"21856","creator":"eiofinov","content_type":"application/zip","date_created":"2026-05-11T08:36:01Z","date_updated":"2026-05-11T08:36:01Z","checksum":"2e148dad920e3f9b7c32796e0ba2e5f7","access_level":"closed","file_name":"EIofinova_thesis_FinalVersion.zip"},{"success":1,"file_name":"2026_Iofinova_Eugenia_Thesis.pdf","creator":"eiofinov","content_type":"application/pdf","date_created":"2026-05-13T13:10:48Z","date_updated":"2026-05-13T13:10:48Z","access_level":"open_access","checksum":"b10c2933f386f532b2dbf28b19c5525c","relation":"main_file","file_id":"21877","file_size":18137757}],"OA_place":"publisher"},{"tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"department":[{"_id":"GradSch"},{"_id":"DaAl"}],"related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"21854"}]},"abstract":[{"text":"The availability of powerful open-source large language models (LLMs) opens exciting use cases, such as using personal data to fine-tune these models to imitate a user’s unique writing style. Two key requirements for this functionality are personalization–in the sense that the output should recognizably reflect the user’s own writing style—and privacy–users may justifiably be wary of uploading extremely personal data, such as their email archive, to a third-party service. In this paper, we demonstrate the feasibility of training and running such an assistant, which we call Panza, on commodity hardware, for the specific use case of email generation. Panza’s personalization features are based on a combination of parameter-efficient fine-tuning using a variant of the Reverse Instructions technique [1] and Retrieval-Augmented Generation (RAG) [2]. We demonstrate that this combination allows us to fine-tune an LLM to reflect a user’s writing style using limited data, while executing on extremely limited resources, e.g. on a free Google Colab instance. Our key methodological contribution is the first detailed study of evaluation metrics for this task, and\r\nof how different choices of system components–the use of RAG and of different fine-tuning approaches–impact the system’s performance. Additionally, we demonstrate that very little data - under 100 email samples - are sufficient to create models that convincingly imitate humans, showcasing a previously unknown attack vector in language models. We are releasing the full Panza code as well as three new email datasets licensed for research use.","lang":"eng"}],"date_published":"2026-03-06T00:00:00Z","month":"03","publication_status":"published","_id":"21857","citation":{"mla":"Nicolicioiu, Armand, et al. “Panza: Investigating the Feasibility of Fully-Local Personalized Text Generation.” <i>Third Conference on Parsimony and Learning (Proceedings Track)</i>, 81, OpenReview, 2026.","chicago":"Nicolicioiu, Armand, Eugenia B Iofinova, Andrej Jovanovic, Eldar Kurtic, Mahdi Nikdan, Andrei Panferov, Ilia Markov, Nir Shavit, and Dan-Adrian Alistarh. <i>Panza: Investigating the Feasibility of Fully-Local Personalized Text Generation</i>. <i>Third Conference on Parsimony and Learning (Proceedings Track)</i>. OpenReview, 2026.","ista":"Nicolicioiu A, Iofinova EB, Jovanovic A, Kurtic E, Nikdan M, Panferov A, Markov I, Shavit N, Alistarh D-A. 2026. Panza: Investigating the feasibility of fully-local personalized text generation, OpenReview,p.","apa":"Nicolicioiu, A., Iofinova, E. B., Jovanovic, A., Kurtic, E., Nikdan, M., Panferov, A., … Alistarh, D.-A. (2026). <i>Panza: Investigating the feasibility of fully-local personalized text generation</i>. <i>Third Conference on Parsimony and Learning (Proceedings Track)</i>. Tübíngen, Germany: OpenReview.","ieee":"A. Nicolicioiu <i>et al.</i>, <i>Panza: Investigating the feasibility of fully-local personalized text generation</i>. OpenReview, 2026.","ama":"Nicolicioiu A, Iofinova EB, Jovanovic A, et al. <i>Panza: Investigating the Feasibility of Fully-Local Personalized Text Generation</i>. OpenReview; 2026.","short":"A. Nicolicioiu, E.B. Iofinova, A. Jovanovic, E. Kurtic, M. Nikdan, A. Panferov, I. Markov, N. Shavit, D.-A. Alistarh, Panza: Investigating the Feasibility of Fully-Local Personalized Text Generation, OpenReview, 2026."},"quality_controlled":"1","publication":"Third Conference on Parsimony and Learning (Proceedings Track)","title":"Panza: Investigating the feasibility of fully-local personalized text generation","corr_author":"1","article_number":"81","license":"https://creativecommons.org/licenses/by/4.0/","oa_version":"Accepted Version","author":[{"full_name":"Nicolicioiu, Armand","first_name":"Armand","last_name":"Nicolicioiu"},{"first_name":"Eugenia B","orcid":"0000-0002-7778-3221","last_name":"Iofinova","id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117","full_name":"Iofinova, Eugenia B"},{"first_name":"Andrej","last_name":"Jovanovic","full_name":"Jovanovic, Andrej"},{"id":"47beb3a5-07b5-11eb-9b87-b108ec578218","last_name":"Kurtic","first_name":"Eldar","full_name":"Kurtic, Eldar"},{"full_name":"Nikdan, Mahdi","id":"66374281-f394-11eb-9cf6-869147deecc0","last_name":"Nikdan","first_name":"Mahdi"},{"full_name":"Panferov, Andrei","last_name":"Panferov","first_name":"Andrei","id":"2c18daae-4dbe-11ef-8491-98ce2d960f09"},{"id":"D0CF4148-C985-11E9-8066-0BDEE5697425","first_name":"Ilia","last_name":"Markov","full_name":"Markov, Ilia"},{"full_name":"Shavit, Nir","last_name":"Shavit","first_name":"Nir"},{"orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian","last_name":"Alistarh","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian"}],"OA_type":"green","day":"06","date_updated":"2026-05-19T11:20:27Z","publisher":"OpenReview","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","main_file_link":[{"open_access":"1","url":"https://openreview.net/pdf?id=soFWnTqd23"}],"OA_place":"publisher","article_processing_charge":"No","date_created":"2026-05-11T08:50:28Z","keyword":["LLMs","PEFT","LoRA","personalization","efficient ML"],"language":[{"iso":"eng"}],"type":"conference_poster","status":"public","year":"2026","oa":1,"conference":{"end_date":"2026-03-26","start_date":"2026-03-23","name":"CPAL: Conference on Parsimony and Learning","location":"Tübíngen, Germany"}},{"type":"preprint","external_id":{"arxiv":["2601.23153"]},"status":"public","oa":1,"year":"2026","doi":"10.48550/arXiv.2601.23153","date_created":"2026-05-11T08:58:07Z","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2601.23153"}],"OA_place":"repository","article_processing_charge":"No","author":[{"id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117","orcid":"0000-0002-7778-3221","first_name":"Eugenia B","last_name":"Iofinova","full_name":"Iofinova, Eugenia B"},{"full_name":"Alistarh, Dan-Adrian","first_name":"Dan-Adrian","orcid":"0000-0003-3650-940X","last_name":"Alistarh","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87"}],"OA_type":"green","date_updated":"2026-05-19T11:20:27Z","day":"30","acknowledgement":"EI thanks Weiwei Yang, Janardhan Kulkani, and Kate Lytvynets for their advice and support in\r\ndeveloping an earlier version of the Behemoth library. This research was supported by the Scientific\r\nService Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp).\r\nEI was supported in part by the FWF DK VGSCO, grant agreement number W1260-N35.\r\n","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","publication":"arXiv","arxiv":1,"title":"Behemoth: Benchmarking unlearning in LLMs using fully synthetic data","corr_author":"1","oa_version":"Preprint","acknowledged_ssus":[{"_id":"ScienComp"}],"citation":{"ista":"Iofinova EB, Alistarh D-A. Behemoth: Benchmarking unlearning in LLMs using fully synthetic data. arXiv, <a href=\"https://doi.org/10.48550/arXiv.2601.23153\">10.48550/arXiv.2601.23153</a>.","chicago":"Iofinova, Eugenia B, and Dan-Adrian Alistarh. “Behemoth: Benchmarking Unlearning in LLMs Using Fully Synthetic Data.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2601.23153\">https://doi.org/10.48550/arXiv.2601.23153</a>.","mla":"Iofinova, Eugenia B., and Dan-Adrian Alistarh. “Behemoth: Benchmarking Unlearning in LLMs Using Fully Synthetic Data.” <i>ArXiv</i>, doi:<a href=\"https://doi.org/10.48550/arXiv.2601.23153\">10.48550/arXiv.2601.23153</a>.","short":"E.B. Iofinova, D.-A. Alistarh, ArXiv (n.d.).","apa":"Iofinova, E. B., &#38; Alistarh, D.-A. (n.d.). Behemoth: Benchmarking unlearning in LLMs using fully synthetic data. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2601.23153\">https://doi.org/10.48550/arXiv.2601.23153</a>","ieee":"E. B. Iofinova and D.-A. Alistarh, “Behemoth: Benchmarking unlearning in LLMs using fully synthetic data,” <i>arXiv</i>. .","ama":"Iofinova EB, Alistarh D-A. Behemoth: Benchmarking unlearning in LLMs using fully synthetic data. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2601.23153\">10.48550/arXiv.2601.23153</a>"},"publication_status":"draft","_id":"21859","department":[{"_id":"GradSch"},{"_id":"DaAl"}],"related_material":{"record":[{"id":"21854","relation":"dissertation_contains","status":"public"}]},"abstract":[{"text":"As artificial neural networks, and specifically large language models, have improved rapidly in capabilities and quality, they have increasingly been deployed in real-world applications, from customer service to Google search, despite the fact that they frequently make factually incorrect or undesirable statements. This trend has inspired practical and academic interest in model editing, that is, in adjusting the weights of the model to modify its likely outputs for queries relating to a specific fact or set of facts. This may be done either to amend a fact or set of facts, for instance, to fix a frequent error in the training data, or to suppress a fact or set of facts entirely, for instance, in case of dangerous knowledge. Multiple methods have been proposed to do such edits. However, at the same time, it has been shown that such model editing can be brittle and incomplete. Moreover the effectiveness of any model editing method necessarily depends on the data on which the model is trained, and, therefore, a good understanding of the interaction of the training data distribution and the way it is stored in the network is necessary and helpful to reliably perform model editing. However, working with large language models trained on real-world data does not allow us to understand this relationship or fully measure the effects of model editing. We therefore propose Behemoth, a fully synthetic data generation framework. To demonstrate the practical insights from the framework, we explore model editing in the context of simple tabular data, demonstrating surprising findings that, in some cases, echo real-world results, for instance, that in some cases restricting the update rank results in a more effective update.","lang":"eng"}],"project":[{"grant_number":"W1260-N35","_id":"9B9290DE-BA93-11EA-9121-9846C619BF3A","name":"Vienna Graduate School on Computational Optimization"}],"month":"01","date_published":"2026-01-30T00:00:00Z"},{"oa_version":"Preprint","title":"Position: It's time to act on the risk of efficient personalized text generation","arxiv":1,"corr_author":"1","publication":"arXiv","citation":{"mla":"Iofinova, Eugenia B., et al. “Position: It’s Time to Act on the Risk of Efficient Personalized Text Generation.” <i>ArXiv</i>, doi:<a href=\"https://doi.org/10.48550/arXiv.2502.06560\">10.48550/arXiv.2502.06560</a>.","chicago":"Iofinova, Eugenia B, Andrej Jovanovic, and Dan-Adrian Alistarh. “Position: It’s Time to Act on the Risk of Efficient Personalized Text Generation.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2502.06560\">https://doi.org/10.48550/arXiv.2502.06560</a>.","ista":"Iofinova EB, Jovanovic A, Alistarh D-A. Position: It’s time to act on the risk of efficient personalized text generation. arXiv, <a href=\"https://doi.org/10.48550/arXiv.2502.06560\">10.48550/arXiv.2502.06560</a>.","ama":"Iofinova EB, Jovanovic A, Alistarh D-A. Position: It’s time to act on the risk of efficient personalized text generation. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2502.06560\">10.48550/arXiv.2502.06560</a>","apa":"Iofinova, E. B., Jovanovic, A., &#38; Alistarh, D.-A. (n.d.). Position: It’s time to act on the risk of efficient personalized text generation. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2502.06560\">https://doi.org/10.48550/arXiv.2502.06560</a>","ieee":"E. B. Iofinova, A. Jovanovic, and D.-A. Alistarh, “Position: It’s time to act on the risk of efficient personalized text generation,” <i>arXiv</i>. .","short":"E.B. Iofinova, A. Jovanovic, D.-A. Alistarh, ArXiv (n.d.)."},"_id":"21858","publication_status":"draft","project":[{"grant_number":"101158077","_id":"8e35c14b-16d5-11f0-9cad-a3fc35339161","name":"FastML: Efficient and Cost-Effective Distributed Machine Learning"},{"_id":"9B9290DE-BA93-11EA-9121-9846C619BF3A","grant_number":"W1260-N35","name":"Vienna Graduate School on Computational Optimization"}],"date_published":"2025-06-02T00:00:00Z","month":"06","department":[{"_id":"GradSch"},{"_id":"DaAl"}],"related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"21854"}]},"abstract":[{"text":"The recent surge in high-quality open-source Generative AI text models (colloquially: LLMs), as well as efficient finetuning techniques, have opened the possibility of creating high-quality personalized models that generate text attuned to a specific individual’s needs and are capable of credibly imitating their writing style by refining an open-source model with that person’s own data. The technology to create such models is accessible to private individuals, and training and running such models can be done cheaply on consumer-grade hardware. While these advancements are a huge gain for usability and privacy, this position paper argues that the practical feasibility of impersonating specific individuals also introduces novel safety risks. For instance, this technology enables the creation of phishing emails\r\nor fraudulent social media accounts, based on small amounts of publicly available text, or by the individuals themselves to escape AI text detection. We further argue that these risks are complementary to—and distinct from—the much-discussed risks of other impersonation attacks such as image, voice, or video deepfakes, and are not adequately addressed by the larger research community, or the current generation of open- and closed-source models.","lang":"eng"}],"doi":"10.48550/arXiv.2502.06560","external_id":{"arxiv":["2502.06560"]},"status":"public","oa":1,"year":"2025","type":"preprint","language":[{"iso":"eng"}],"date_created":"2026-05-11T08:55:23Z","article_processing_charge":"No","OA_place":"repository","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2502.06560"}],"acknowledgement":"This research was supported by the Scientific Service Units (SSU) of IST Austria through resources\r\nprovided by Scientific Computing (SciComp). EI was supported in part by the FWF DK VGSCO,\r\ngrant agreement number W1260-N35. AJ was supported in part by ERC Proof-of-Concept Grant\r\nFastML, grant agreement 101158077.","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","date_updated":"2026-05-19T11:20:27Z","day":"02","OA_type":"green","author":[{"first_name":"Eugenia B","orcid":"0000-0002-7778-3221","last_name":"Iofinova","id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117","full_name":"Iofinova, Eugenia B"},{"full_name":"Jovanovic, Andrej","first_name":"Andrej","last_name":"Jovanovic"},{"full_name":"Alistarh, Dan-Adrian","last_name":"Alistarh","first_name":"Dan-Adrian","orcid":"0000-0003-3650-940X","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87"}]},{"related_material":{"link":[{"url":"https://github.com/IST-DASLab/SPADE","relation":"software"}],"record":[{"status":"public","relation":"dissertation_contains","id":"21854"}]},"abstract":[{"text":"It is known that sparsity can improve interpretability for deep neural networks. However, existing methods in the area either require networks that are pre-trained with sparsity constraints, or impose sparsity after the fact, altering the network’s general behavior. In this paper, we demonstrate, for the first time, that sparsity can instead be incorporated into the interpretation process itself, as a sample-specific preprocessing step. Unlike previous work, this approach, which we call SPADE, does not place constraints on the trained model and does not affect its behavior during inference on the sample. Given a trained model and a target sample, SPADE uses sample-targeted pruning to provide a \"trace\" of the network’s execution on the sample, reducing the network to the most important connections prior to computing an interpretation. We demonstrate that preprocessing with SPADE significantly increases the accuracy of image saliency maps across several interpretability methods. Additionally, SPADE improves the usefulness of neuron visualizations, aiding humans in reasoning about network behavior. Our code is available at https://github.com/IST-DASLab/SPADE.","lang":"eng"}],"department":[{"_id":"DaAl"}],"month":"09","date_published":"2024-09-01T00:00:00Z","project":[{"_id":"9B9290DE-BA93-11EA-9121-9846C619BF3A","grant_number":"W1260-N35","name":"Vienna Graduate School on Computational Optimization"}],"_id":"18121","publication_status":"published","quality_controlled":"1","citation":{"short":"A.S. Moakhar, E.B. Iofinova, E. Frantar, D.-A. Alistarh, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 45955–45987.","ieee":"A. S. Moakhar, E. B. Iofinova, E. Frantar, and D.-A. Alistarh, “SPADE: Sparsity-guided debugging for deep neural networks,” in <i>Proceedings of the 41st International Conference on Machine Learning</i>, Vienna, Austria, 2024, vol. 235, pp. 45955–45987.","apa":"Moakhar, A. S., Iofinova, E. B., Frantar, E., &#38; Alistarh, D.-A. (2024). SPADE: Sparsity-guided debugging for deep neural networks. In <i>Proceedings of the 41st International Conference on Machine Learning</i> (Vol. 235, pp. 45955–45987). Vienna, Austria: ML Research Press.","ama":"Moakhar AS, Iofinova EB, Frantar E, Alistarh D-A. SPADE: Sparsity-guided debugging for deep neural networks. In: <i>Proceedings of the 41st International Conference on Machine Learning</i>. Vol 235. ML Research Press; 2024:45955-45987.","ista":"Moakhar AS, Iofinova EB, Frantar E, Alistarh D-A. 2024. SPADE: Sparsity-guided debugging for deep neural networks. Proceedings of the 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 45955–45987.","mla":"Moakhar, Arshia Soltani, et al. “SPADE: Sparsity-Guided Debugging for Deep Neural Networks.” <i>Proceedings of the 41st International Conference on Machine Learning</i>, vol. 235, ML Research Press, 2024, pp. 45955–87.","chicago":"Moakhar, Arshia Soltani, Eugenia B Iofinova, Elias Frantar, and Dan-Adrian Alistarh. “SPADE: Sparsity-Guided Debugging for Deep Neural Networks.” In <i>Proceedings of the 41st International Conference on Machine Learning</i>, 235:45955–87. ML Research Press, 2024."},"intvolume":"       235","publication_identifier":{"eissn":["2640-3498"]},"acknowledged_ssus":[{"_id":"ScienComp"}],"oa_version":"Preprint","publication":"Proceedings of the 41st International Conference on Machine Learning","corr_author":"1","arxiv":1,"title":"SPADE: Sparsity-guided debugging for deep neural networks","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","acknowledgement":"The authors would like to thank Stephen Casper and Tony Wang for their feedback on this work, and Eldar Kurtic for his advice on aspects of the project. This research was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp). EI was supported in part by the FWF DK VGSCO, grant agreement number W1260-N35.","publisher":"ML Research Press","author":[{"first_name":"Arshia Soltani","last_name":"Moakhar","full_name":"Moakhar, Arshia Soltani"},{"id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117","orcid":"0000-0002-7778-3221","first_name":"Eugenia B","last_name":"Iofinova","full_name":"Iofinova, Eugenia B"},{"last_name":"Frantar","first_name":"Elias","id":"09a8f98d-ec99-11ea-ae11-c063a7b7fe5f","full_name":"Frantar, Elias"},{"id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh","first_name":"Dan-Adrian","orcid":"0000-0003-3650-940X","full_name":"Alistarh, Dan-Adrian"}],"day":"01","date_updated":"2026-05-19T11:20:27Z","article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2310.04519"}],"volume":235,"alternative_title":["PMLR"],"language":[{"iso":"eng"}],"date_created":"2024-09-22T22:01:46Z","conference":{"start_date":"2024-07-21","name":"ICML: International Conference on Machine Learning","location":"Vienna, Austria","end_date":"2024-07-27"},"oa":1,"year":"2024","status":"public","external_id":{"arxiv":["2310.04519"]},"scopus_import":"1","page":"45955-45987","type":"conference"},{"volume":202,"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2302.04852"}],"article_processing_charge":"No","author":[{"first_name":"Mahdi","last_name":"Nikdan","id":"66374281-f394-11eb-9cf6-869147deecc0","full_name":"Nikdan, Mahdi"},{"last_name":"Pegolotti","first_name":"Tommaso","full_name":"Pegolotti, Tommaso"},{"full_name":"Iofinova, Eugenia B","last_name":"Iofinova","orcid":"0000-0002-7778-3221","first_name":"Eugenia B","id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117"},{"last_name":"Kurtic","first_name":"Eldar","id":"47beb3a5-07b5-11eb-9b87-b108ec578218","full_name":"Kurtic, Eldar"},{"full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","orcid":"0000-0003-3650-940X","last_name":"Alistarh"}],"day":"30","date_updated":"2025-04-14T07:49:12Z","acknowledgement":"We would like to thank Elias Frantar for his valuable assistance and support at the outset of this project, and the anonymous ICML and SNN reviewers for very constructive feedback. EI was supported in part by the FWF DK VGSCO, grant agreement number W1260-N35. DA acknowledges generous ERC support, via Starting Grant 805223 ScaleML. ","publisher":"ML Research Press","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","page":"26215-26227","type":"conference","status":"public","external_id":{"arxiv":["2302.04852"]},"conference":{"name":"ICML: International Conference on Machine Learning","location":"Honolulu, Hawaii, HI, United States","start_date":"2023-07-23","end_date":"2023-07-29"},"year":"2023","oa":1,"scopus_import":"1","date_created":"2023-10-29T23:01:17Z","language":[{"iso":"eng"}],"alternative_title":["PMLR"],"publication_status":"published","ec_funded":1,"_id":"14460","department":[{"_id":"DaAl"}],"abstract":[{"text":"We provide an efficient implementation of the backpropagation algorithm, specialized to the case where the weights of the neural network being trained are sparse. Our algorithm is general, as it applies to arbitrary (unstructured) sparsity and common layer types (e.g., convolutional or linear). We provide a fast vectorized implementation on commodity CPUs, and show that it can yield speedups in end-to-end runtime experiments, both in transfer learning using already-sparsified networks, and in training sparse networks from scratch. Thus, our results provide the first support for sparse training on commodity hardware.","lang":"eng"}],"project":[{"name":"Elastic Coordination for Scalable Machine Learning","_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223","call_identifier":"H2020"}],"date_published":"2023-07-30T00:00:00Z","month":"07","publication":"Proceedings of the 40th International Conference on Machine Learning","arxiv":1,"title":"SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge","corr_author":"1","oa_version":"Preprint","citation":{"ista":"Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. 2023. SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 26215–26227.","chicago":"Nikdan, Mahdi, Tommaso Pegolotti, Eugenia B Iofinova, Eldar Kurtic, and Dan-Adrian Alistarh. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” In <i>Proceedings of the 40th International Conference on Machine Learning</i>, 202:26215–27. ML Research Press, 2023.","mla":"Nikdan, Mahdi, et al. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” <i>Proceedings of the 40th International Conference on Machine Learning</i>, vol. 202, ML Research Press, 2023, pp. 26215–27.","short":"M. Nikdan, T. Pegolotti, E.B. Iofinova, E. Kurtic, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 26215–26227.","apa":"Nikdan, M., Pegolotti, T., Iofinova, E. B., Kurtic, E., &#38; Alistarh, D.-A. (2023). SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In <i>Proceedings of the 40th International Conference on Machine Learning</i> (Vol. 202, pp. 26215–26227). Honolulu, Hawaii, HI, United States: ML Research Press.","ieee":"M. Nikdan, T. Pegolotti, E. B. Iofinova, E. Kurtic, and D.-A. Alistarh, “SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge,” in <i>Proceedings of the 40th International Conference on Machine Learning</i>, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 26215–26227.","ama":"Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In: <i>Proceedings of the 40th International Conference on Machine Learning</i>. Vol 202. ML Research Press; 2023:26215-26227."},"quality_controlled":"1","publication_identifier":{"eissn":["2640-3498"]},"intvolume":"       202"},{"type":"conference","page":"24364-24373","doi":"10.1109/cvpr52729.2023.02334","status":"public","external_id":{"isi":["001062531308068"],"arxiv":["2304.12622"]},"year":"2023","conference":{"name":"CVPR: Conference on Computer Vision and Pattern Recognition","location":"Vancouver, BC, Canada","start_date":"2023-06-17","end_date":"2023-06-24"},"oa":1,"date_created":"2024-01-10T08:42:40Z","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2304.12622"}],"article_processing_charge":"No","day":"22","date_updated":"2026-05-19T11:20:27Z","author":[{"last_name":"Iofinova","orcid":"0000-0002-7778-3221","first_name":"Eugenia B","id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117","full_name":"Iofinova, Eugenia B"},{"full_name":"Peste, Elena-Alexandra","first_name":"Elena-Alexandra","last_name":"Peste","id":"32D78294-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian","last_name":"Alistarh"}],"acknowledgement":"The authors would like to sincerely thank Sara Hooker for her feedback during the development of this work. EI was supported in part by the FWF DK VGSCO, grant agreement number W1260-N35. AP and DA acknowledge generous ERC support, via Starting Grant 805223 ScaleML.","publisher":"IEEE","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","arxiv":1,"title":"Bias in pruned vision models: In-depth analysis and countermeasures","corr_author":"1","publication":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition","oa_version":"Preprint","publication_identifier":{"eisbn":["9798350301298"],"eissn":["2575-7075"]},"citation":{"ista":"Iofinova EB, Krumes A, Alistarh D-A. 2023. Bias in pruned vision models: In-depth analysis and countermeasures. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 24364–24373.","mla":"Iofinova, Eugenia B., et al. “Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures.” <i>2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, IEEE, 2023, pp. 24364–73, doi:<a href=\"https://doi.org/10.1109/cvpr52729.2023.02334\">10.1109/cvpr52729.2023.02334</a>.","chicago":"Iofinova, Eugenia B, Alexandra Krumes, and Dan-Adrian Alistarh. “Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures.” In <i>2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, 24364–73. IEEE, 2023. <a href=\"https://doi.org/10.1109/cvpr52729.2023.02334\">https://doi.org/10.1109/cvpr52729.2023.02334</a>.","short":"E.B. Iofinova, A. Krumes, D.-A. Alistarh, in:, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2023, pp. 24364–24373.","apa":"Iofinova, E. B., Krumes, A., &#38; Alistarh, D.-A. (2023). Bias in pruned vision models: In-depth analysis and countermeasures. In <i>2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i> (pp. 24364–24373). Vancouver, BC, Canada: IEEE. <a href=\"https://doi.org/10.1109/cvpr52729.2023.02334\">https://doi.org/10.1109/cvpr52729.2023.02334</a>","ieee":"E. B. Iofinova, A. Krumes, and D.-A. Alistarh, “Bias in pruned vision models: In-depth analysis and countermeasures,” in <i>2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, Vancouver, BC, Canada, 2023, pp. 24364–24373.","ama":"Iofinova EB, Krumes A, Alistarh D-A. Bias in pruned vision models: In-depth analysis and countermeasures. In: <i>2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>. IEEE; 2023:24364-24373. doi:<a href=\"https://doi.org/10.1109/cvpr52729.2023.02334\">10.1109/cvpr52729.2023.02334</a>"},"isi":1,"quality_controlled":"1","publication_status":"published","_id":"14771","ec_funded":1,"project":[{"grant_number":"W1260-N35","_id":"9B9290DE-BA93-11EA-9121-9846C619BF3A","name":"Vienna Graduate School on Computational Optimization"},{"name":"Elastic Coordination for Scalable Machine Learning","call_identifier":"H2020","_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223"}],"date_published":"2023-08-22T00:00:00Z","month":"08","department":[{"_id":"DaAl"},{"_id":"ChLa"}],"related_material":{"link":[{"relation":"software","url":"https://github.com/IST-DASLab/pruned-vision-model-bias"}],"record":[{"id":"21854","status":"public","relation":"dissertation_contains"}]},"abstract":[{"lang":"eng","text":"Pruning—that is, setting a significant subset of the parameters of a neural network to zero—is one of the most popular methods of model compression. Yet, several recent works have raised the issue that pruning may induce or exacerbate bias in the output of the compressed model. Despite existing evidence for this phenomenon, the relationship between neural network pruning and induced bias is not well-understood. In this work, we systematically investigate and characterize this phenomenon in Convolutional Neural Networks for computer vision. First, we show that it is in fact possible to obtain highly-sparse models, e.g. with less than 10% remaining weights, which do not decrease in accuracy nor substantially increase in bias when compared to dense models. At the same time, we also find that, at higher sparsities, pruned models exhibit higher uncertainty in their outputs, as well as increased correlations, which we directly link to increased bias. We propose easy-to-use criteria which, based only on the uncompressed model, establish whether bias will increase with pruning, and identify the samples most susceptible to biased predictions post-compression. Our code can be found at https://github.com/IST-DASLab/pruned-vision-model-bias."}]},{"page":"12256-12266","type":"conference","external_id":{"isi":["000870759105034"],"arxiv":["2111.13445"]},"status":"public","year":"2022","oa":1,"conference":{"end_date":"2022-06-24","start_date":"2022-06-18","name":"CVPR: Computer Vision and Pattern Recognition","location":"New Orleans, LA, United States"},"scopus_import":"1","doi":"10.1109/cvpr52688.2022.01195","date_created":"2023-01-16T10:06:00Z","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2111.13445","open_access":"1"}],"article_processing_charge":"No","author":[{"orcid":"0000-0002-7778-3221","first_name":"Eugenia B","last_name":"Iofinova","id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117","full_name":"Iofinova, Eugenia B"},{"first_name":"Elena-Alexandra","last_name":"Peste","id":"32D78294-F248-11E8-B48F-1D18A9856A87","full_name":"Peste, Elena-Alexandra"},{"full_name":"Kurtz, Mark","first_name":"Mark","last_name":"Kurtz"},{"full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian","last_name":"Alistarh"}],"date_updated":"2026-04-07T13:30:19Z","day":"27","publisher":"Institute of Electrical and Electronics Engineers","acknowledgement":"he authors would like to sincerely thank Christoph Lampert and Nir Shavit for fruitful discussions during the development of this work, and Eldar Kurtic for experimental support. EI was supported in part by the FWF DK VGSCO, grant agreement number W1260-N35, while AP and DA acknowledge generous support by the ERC, via Starting Grant 805223 ScaleML.","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publication":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition","title":"How well do sparse ImageNet models transfer?","arxiv":1,"corr_author":"1","oa_version":"Preprint","citation":{"short":"E.B. Iofinova, A. Krumes, M. Kurtz, D.-A. Alistarh, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 12256–12266.","ieee":"E. B. Iofinova, A. Krumes, M. Kurtz, and D.-A. Alistarh, “How well do sparse ImageNet models transfer?,” in <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, New Orleans, LA, United States, 2022, pp. 12256–12266.","apa":"Iofinova, E. B., Krumes, A., Kurtz, M., &#38; Alistarh, D.-A. (2022). How well do sparse ImageNet models transfer? In <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i> (pp. 12256–12266). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/cvpr52688.2022.01195\">https://doi.org/10.1109/cvpr52688.2022.01195</a>","ama":"Iofinova EB, Krumes A, Kurtz M, Alistarh D-A. How well do sparse ImageNet models transfer? In: <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>. Institute of Electrical and Electronics Engineers; 2022:12256-12266. doi:<a href=\"https://doi.org/10.1109/cvpr52688.2022.01195\">10.1109/cvpr52688.2022.01195</a>","ista":"Iofinova EB, Krumes A, Kurtz M, Alistarh D-A. 2022. How well do sparse ImageNet models transfer? 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR: Computer Vision and Pattern Recognition, 12256–12266.","mla":"Iofinova, Eugenia B., et al. “How Well Do Sparse ImageNet Models Transfer?” <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, Institute of Electrical and Electronics Engineers, 2022, pp. 12256–66, doi:<a href=\"https://doi.org/10.1109/cvpr52688.2022.01195\">10.1109/cvpr52688.2022.01195</a>.","chicago":"Iofinova, Eugenia B, Alexandra Krumes, Mark Kurtz, and Dan-Adrian Alistarh. “How Well Do Sparse ImageNet Models Transfer?” In <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, 12256–66. Institute of Electrical and Electronics Engineers, 2022. <a href=\"https://doi.org/10.1109/cvpr52688.2022.01195\">https://doi.org/10.1109/cvpr52688.2022.01195</a>."},"isi":1,"quality_controlled":"1","publication_identifier":{"eissn":["2575-7075"]},"publication_status":"published","ec_funded":1,"_id":"12299","department":[{"_id":"DaAl"},{"_id":"ChLa"}],"abstract":[{"text":"Transfer learning is a classic paradigm by which models pretrained on large “upstream” datasets are adapted to yield good results on “downstream” specialized datasets. Generally, more accurate models on the “upstream” dataset tend to provide better transfer accuracy “downstream”. In this work, we perform an in-depth investigation of this phenomenon in the context of convolutional neural networks (CNNs) trained on the ImageNet dataset, which have been pruned-that is, compressed by sparsifiying their connections. We consider transfer using unstructured pruned models obtained by applying several state-of-the-art pruning methods, including magnitude-based, second-order, regrowth, lottery-ticket, and regularization approaches, in the context of twelve standard transfer tasks. In a nutshell, our study shows that sparse models can match or even outperform the transfer performance of dense models, even at high sparsities, and, while doing so, can lead to significant inference and even training speedups. At the same time, we observe and analyze significant differences in the behaviour of different pruning methods. The code is available at: https://github.com/IST-DASLab/sparse-imagenet-transfer.","lang":"eng"}],"related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"13074"}]},"project":[{"grant_number":"W1260-N35","_id":"9B9290DE-BA93-11EA-9121-9846C619BF3A","name":"Vienna Graduate School on Computational Optimization"},{"_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223","call_identifier":"H2020","name":"Elastic Coordination for Scalable Machine Learning"}],"month":"09","date_published":"2022-09-27T00:00:00Z"},{"year":"2022","oa":1,"status":"public","external_id":{"arxiv":["2106.11732"]},"type":"journal_article","language":[{"iso":"eng"}],"file_date_updated":"2023-02-23T10:30:04Z","date_created":"2023-02-02T20:29:57Z","file":[{"date_updated":"2023-02-23T10:30:04Z","access_level":"open_access","checksum":"97c8a8470759cab597abb973ca137a3b","creator":"dernst","date_created":"2023-02-23T10:30:04Z","content_type":"application/pdf","success":1,"file_name":"2022_TMLR_Iofinova.pdf","file_size":1948063,"relation":"main_file","file_id":"12673"}],"article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"https://openreview.net/forum?id=XsPopigZXV"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","acknowledgement":"The authors would like to thank Bernd Prach, Elias Frantar, Alexandra Peste, Mahdi Nikdan, and Peter Súkeník for their helpful feedback. This research was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp). This publication was made possible by an ETH AI Center postdoctoral fellowship granted to Nikola Konstantinov. Eugenia Iofinova was supported in part by the FWF DK VGSCO, grant agreement number W1260-N35. ","publisher":"ML Research Press","ddc":["000"],"date_updated":"2025-12-30T11:04:31Z","day":"22","author":[{"full_name":"Iofinova, Eugenia B","id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117","orcid":"0000-0002-7778-3221","first_name":"Eugenia B","last_name":"Iofinova"},{"full_name":"Konstantinov, Nikola H","first_name":"Nikola H","orcid":"0009-0009-5204-7621","last_name":"Konstantinov","id":"4B9D76E4-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","first_name":"Christoph","last_name":"Lampert"}],"oa_version":"Published Version","corr_author":"1","arxiv":1,"title":"FLEA: Provably robust fair multisource learning from unreliable training data","publication":"Transactions on Machine Learning Research","has_accepted_license":"1","publication_identifier":{"issn":["2835-8856"]},"quality_controlled":"1","citation":{"short":"E.B. Iofinova, N.H. Konstantinov, C. Lampert, Transactions on Machine Learning Research (2022).","ama":"Iofinova EB, Konstantinov NH, Lampert C. FLEA: Provably robust fair multisource learning from unreliable training data. <i>Transactions on Machine Learning Research</i>. 2022.","apa":"Iofinova, E. B., Konstantinov, N. H., &#38; Lampert, C. (2022). FLEA: Provably robust fair multisource learning from unreliable training data. <i>Transactions on Machine Learning Research</i>. ML Research Press.","ieee":"E. B. Iofinova, N. H. Konstantinov, and C. Lampert, “FLEA: Provably robust fair multisource learning from unreliable training data,” <i>Transactions on Machine Learning Research</i>. ML Research Press, 2022.","ista":"Iofinova EB, Konstantinov NH, Lampert C. 2022. FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research.","mla":"Iofinova, Eugenia B., et al. “FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data.” <i>Transactions on Machine Learning Research</i>, ML Research Press, 2022.","chicago":"Iofinova, Eugenia B, Nikola H Konstantinov, and Christoph Lampert. “FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data.” <i>Transactions on Machine Learning Research</i>. ML Research Press, 2022."},"acknowledged_ssus":[{"_id":"ScienComp"}],"article_type":"original","_id":"12495","publication_status":"published","month":"12","date_published":"2022-12-22T00:00:00Z","project":[{"name":"Vienna Graduate School on Computational Optimization","_id":"9B9290DE-BA93-11EA-9121-9846C619BF3A","grant_number":"W1260-N35"}],"abstract":[{"text":"Fairness-aware learning aims at constructing classifiers that not only make accurate predictions, but also do not discriminate against specific groups. It is a fast-growing area of\r\nmachine learning with far-reaching societal impact. However, existing fair learning methods\r\nare vulnerable to accidental or malicious artifacts in the training data, which can cause\r\nthem to unknowingly produce unfair classifiers. In this work we address the problem of\r\nfair learning from unreliable training data in the robust multisource setting, where the\r\navailable training data comes from multiple sources, a fraction of which might not be representative of the true data distribution. We introduce FLEA, a filtering-based algorithm\r\nthat identifies and suppresses those data sources that would have a negative impact on\r\nfairness or accuracy if they were used for training. As such, FLEA is not a replacement of\r\nprior fairness-aware learning methods but rather an augmentation that makes any of them\r\nrobust against unreliable training data. We show the effectiveness of our approach by a\r\ndiverse range of experiments on multiple datasets. Additionally, we prove formally that\r\n–given enough data– FLEA protects the learner against corruptions as long as the fraction of\r\naffected data sources is less than half. Our source code and documentation are available at\r\nhttps://github.com/ISTAustria-CVML/FLEA.","lang":"eng"}],"related_material":{"link":[{"url":"https://github.com/ISTAustria-CVML/FLEA","relation":"software","description":"source code"}]},"department":[{"_id":"ChLa"}],"tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"}},{"article_processing_charge":"No","main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2021/file/48000647b315f6f00f913caa757a70b3-Paper.pdf","open_access":"1"}],"volume":34,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Neural Information Processing Systems Foundation","acknowledgement":"This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML), and a CNRS PEPS grant. This research was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp). We would also like to thank Christoph Lampert for his feedback on an earlier version of this work, as well as for providing hardware for the Transformer-XL experiments.","day":"06","date_updated":"2026-06-18T17:18:20Z","ddc":["000"],"author":[{"full_name":"Peste, Elena-Alexandra","id":"32D78294-F248-11E8-B48F-1D18A9856A87","first_name":"Elena-Alexandra","last_name":"Peste"},{"id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117","orcid":"0000-0002-7778-3221","first_name":"Eugenia B","last_name":"Iofinova","full_name":"Iofinova, Eugenia B"},{"full_name":"Vladu, Adrian","first_name":"Adrian","last_name":"Vladu"},{"full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh","first_name":"Dan-Adrian","orcid":"0000-0003-3650-940X"}],"scopus_import":"1","oa":1,"conference":{"end_date":"2021-12-14","start_date":"2021-12-06","location":"Virtual, Online","name":"NeurIPS: Neural Information Processing Systems"},"year":"2021","status":"public","external_id":{"arxiv":["2106.12379"]},"type":"conference","page":"8557-8570","alternative_title":["Advances in Neural Information Processing Systems"],"language":[{"iso":"eng"}],"date_created":"2022-06-20T12:11:53Z","_id":"11458","ec_funded":1,"publication_status":"published","month":"12","date_published":"2021-12-06T00:00:00Z","project":[{"call_identifier":"H2020","grant_number":"805223","_id":"268A44D6-B435-11E9-9278-68D0E5697425","name":"Elastic Coordination for Scalable Machine Learning"}],"abstract":[{"lang":"eng","text":"The increasing computational requirements of deep neural networks (DNNs) have led to significant interest in obtaining DNN models that are sparse, yet accurate. Recent work has investigated the even harder case of sparse training, where the DNN weights are, for as much as possible, already sparse to reduce computational costs during training. Existing sparse training methods are often empirical and can have lower accuracy relative to the dense baseline. In this paper, we present a general approach called Alternating Compressed/DeCompressed (AC/DC) training of DNNs, demonstrate convergence for a variant of the algorithm, and show that AC/DC outperforms existing sparse training methods in accuracy at similar computational budgets; at high sparsity levels, AC/DC even outperforms existing methods that rely on accurate pre-trained dense models. An important property of AC/DC is that it allows co-training of dense and sparse models, yielding accurate sparse–dense model pairs at the end of the training process. This is useful in practice, where compressed variants may be desirable for deployment in resource-constrained settings without re-doing the entire training flow, and also provides us with insights into the accuracy gap between dense and compressed models. The code is available at: https://github.com/IST-DASLab/ACDC."}],"related_material":{"record":[{"id":"13074","status":"public","relation":"dissertation_contains"}]},"department":[{"_id":"GradSch"},{"_id":"DaAl"}],"oa_version":"Published Version","corr_author":"1","arxiv":1,"title":"AC/DC: Alternating Compressed/DeCompressed training of deep neural networks","publication":"35th Conference on Neural Information Processing Systems","intvolume":"        34","publication_identifier":{"issn":["1049-5258"],"isbn":["9781713845393"]},"quality_controlled":"1","citation":{"chicago":"Krumes, Alexandra, Eugenia B Iofinova, Adrian Vladu, and Dan-Adrian Alistarh. “AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks.” In <i>35th Conference on Neural Information Processing Systems</i>, 34:8557–70. Neural Information Processing Systems Foundation, 2021.","mla":"Krumes, Alexandra, et al. “AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks.” <i>35th Conference on Neural Information Processing Systems</i>, vol. 34, Neural Information Processing Systems Foundation, 2021, pp. 8557–70.","ista":"Krumes A, Iofinova EB, Vladu A, Alistarh D-A. 2021. AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 34, 8557–8570.","ama":"Krumes A, Iofinova EB, Vladu A, Alistarh D-A. AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In: <i>35th Conference on Neural Information Processing Systems</i>. Vol 34. Neural Information Processing Systems Foundation; 2021:8557-8570.","apa":"Krumes, A., Iofinova, E. B., Vladu, A., &#38; Alistarh, D.-A. (2021). AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In <i>35th Conference on Neural Information Processing Systems</i> (Vol. 34, pp. 8557–8570). Virtual, Online: Neural Information Processing Systems Foundation.","ieee":"A. Krumes, E. B. Iofinova, A. Vladu, and D.-A. Alistarh, “AC/DC: Alternating Compressed/DeCompressed training of deep neural networks,” in <i>35th Conference on Neural Information Processing Systems</i>, Virtual, Online, 2021, vol. 34, pp. 8557–8570.","short":"A. Krumes, E.B. Iofinova, A. Vladu, D.-A. Alistarh, in:, 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021, pp. 8557–8570."},"acknowledged_ssus":[{"_id":"ScienComp"}]}]