Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Karlas, Bojan"'
Autor:
Oala, Luis, Maskey, Manil, Bat-Leah, Lilith, Parrish, Alicia, Gürel, Nezihe Merve, Kuo, Tzu-Sheng, Liu, Yang, Dror, Rotem, Brajovic, Danilo, Yao, Xiaozhe, Bartolo, Max, Rojas, William A Gaviria, Hileman, Ryan, Aliment, Rainier, Mahoney, Michael W., Risdal, Meg, Lease, Matthew, Samek, Wojciech, Dutta, Debojyoti, Northcutt, Curtis G, Coleman, Cody, Hancock, Braden, Koch, Bernard, Tadesse, Girmaw Abebe, Karlaš, Bojan, Alaa, Ahmed, Dieng, Adji Bousso, Noy, Natasha, Reddi, Vijay Janapa, Zou, James, Paritosh, Praveen, van der Schaar, Mihaela, Bollacker, Kurt, Aroyo, Lora, Zhang, Ce, Vanschoren, Joaquin, Guyon, Isabelle, Mattson, Peter
Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets that will adva
Externí odkaz:
http://arxiv.org/abs/2311.13028
Autor:
Mazumder, Mark, Banbury, Colby, Yao, Xiaozhe, Karlaš, Bojan, Rojas, William Gaviria, Diamos, Sudnya, Diamos, Greg, He, Lynn, Parrish, Alicia, Kirk, Hannah Rose, Quaye, Jessica, Rastogi, Charvi, Kiela, Douwe, Jurado, David, Kanter, David, Mosquera, Rafael, Ciro, Juan, Aroyo, Lora, Acun, Bilge, Chen, Lingjiao, Raje, Mehul Smriti, Bartolo, Max, Eyuboglu, Sabri, Ghorbani, Amirata, Goodman, Emmett, Inel, Oana, Kane, Tariq, Kirkpatrick, Christine R., Kuo, Tzu-Sheng, Mueller, Jonas, Thrush, Tristan, Vanschoren, Joaquin, Warren, Margaret, Williams, Adina, Yeung, Serena, Ardalani, Newsha, Paritosh, Praveen, Bat-Leah, Lilith, Zhang, Ce, Zou, James, Wu, Carole-Jean, Coleman, Cody, Ng, Andrew, Mattson, Peter, Reddi, Vijay Janapa
Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems. Neglecting the fundamental importan
Externí odkaz:
http://arxiv.org/abs/2207.10062
Autor:
Karlaš, Bojan, Dao, David, Interlandi, Matteo, Li, Bo, Schelter, Sebastian, Wu, Wentao, Zhang, Ce
Developing modern machine learning (ML) applications is data-centric, of which one fundamental challenge is to understand the influence of data quality to ML training -- "Which training examples are 'guilty' in making the trained ML model predictions
Externí odkaz:
http://arxiv.org/abs/2204.11131
Developing machine learning models can be seen as a process similar to the one established for traditional software development. A key difference between the two lies in the strong dependency between the quality of a machine learning model and the qu
Externí odkaz:
http://arxiv.org/abs/2102.07750
Autor:
Karimi, Mohammad Reza, Gürel, Nezihe Merve, Karlaš, Bojan, Rausch, Johannes, Zhang, Ce, Krause, Andreas
Given $k$ pre-trained classifiers and a stream of unlabeled data examples, how can we actively decide when to query a label so that we can distinguish the best model from the rest while making a small number of queries? Answering this question has a
Externí odkaz:
http://arxiv.org/abs/2010.09818
Machine learning (ML) applications have been thriving recently, largely attributed to the increasing availability of data. However, inconsistency and incomplete information are ubiquitous in real-world datasets, and their impact on ML applications re
Externí odkaz:
http://arxiv.org/abs/2005.05117
Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks, including evasion and backdoor (poisoning) attacks. On the defense side, there have been intensive efforts on improving both empirical and provable robu
Externí odkaz:
http://arxiv.org/abs/2003.08904
Autor:
Yang, Zhuolin, Zhao, Zhikuan, Wang, Boxin, Zhang, Jiawei, Li, Linyi, Pei, Hengzhi, Karlas, Bojan, Liu, Ji, Guo, Heng, Zhang, Ce, Li, Bo
Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently. However, current robustness certification methods are only able to certify under a limited perturbation radius. G
Externí odkaz:
http://arxiv.org/abs/2003.00120
Autor:
Psallidas, Fotis, Zhu, Yiwen, Karlas, Bojan, Interlandi, Matteo, Floratou, Avrilia, Karanasos, Konstantinos, Wu, Wentao, Zhang, Ce, Krishnan, Subru, Curino, Carlo, Weimer, Markus
The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by an ever-growing community of data science (DS) practitioners. This qu
Externí odkaz:
http://arxiv.org/abs/1912.09536
Autor:
Renggli, Cedric, Karlaš, Bojan, Ding, Bolin, Liu, Feng, Schawinski, Kevin, Wu, Wentao, Zhang, Ce
Continuous integration is an indispensable step of modern software engineering practices to systematically manage the life cycles of system development. Developing a machine learning model is no difference - it is an engineering process with a life c
Externí odkaz:
http://arxiv.org/abs/1903.00278