Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Trinh, Khoa"'
Knowledge distillation with unlabeled examples is a powerful training paradigm for generating compact and lightweight student models in applications where the amount of labeled data is limited but one has access to a large pool of unlabeled data. In
Externí odkaz:
http://arxiv.org/abs/2302.03806
The current best approximation algorithms for $k$-median rely on first obtaining a structured fractional solution known as a bi-point solution, and then rounding it to an integer solution. We improve this second step by unifying and refining previous
Externí odkaz:
http://arxiv.org/abs/2210.13395
Distillation with unlabeled examples is a popular and powerful method for training deep neural networks in settings where the amount of labeled data is limited: A large ''teacher'' neural network is trained on the labeled data available, and then it
Externí odkaz:
http://arxiv.org/abs/2210.06711
Distilling knowledge from a large teacher model to a lightweight one is a widely successful approach for generating compact, powerful models in the semi-supervised learning setting where a limited amount of labeled data is available. In large-scale a
Externí odkaz:
http://arxiv.org/abs/2210.01213
Publikováno v:
Journal of Machine Learning Research 20(153), pp. 1-33 (2019)
We consider stochastic settings for clustering, and develop provably-good approximation algorithms for a number of these notions. These algorithms yield better approximation ratios compared to the usual deterministic clustering setting. Additionally,
Externí odkaz:
http://arxiv.org/abs/1809.02271
In this paper, we give tight approximation algorithms for the $k$-center and matroid center problems with outliers. Unfairness arises naturally in this setting: certain clients could always be considered as outliers. To address this issue, we introdu
Externí odkaz:
http://arxiv.org/abs/1710.00287
Publikováno v:
Journal of Machine Learning Research 23(81), pp. 1-41 (2022)
Clustering problems are fundamental to unsupervised learning. There is an increased emphasis on fairness in machine learning and AI; one representative notion of fairness is that no single demographic group should be over-represented among the cluste
Externí odkaz:
http://arxiv.org/abs/1709.06995
We consider an issue of much current concern: could fairness, an issue that is already difficult to guarantee, worsen when algorithms run much of our lives? We consider this in the context of resource-allocation problems, we show that algorithms can
Externí odkaz:
http://arxiv.org/abs/1704.06528
Publikováno v:
Proceedings of ACM-Siam Symposium on Discrete Algorithms (SODA), pages 737-756, 2015
Dependent rounding is a useful technique for optimization problems with hard budget constraints. This framework naturally leads to \emph{negative correlation} properties. However, what if an application naturally calls for dependent rounding on the o
Externí odkaz:
http://arxiv.org/abs/1406.2951
Autor:
Ramakrishnan, Naren, Butler, Patrick, Muthiah, Sathappan, Self, Nathan, Khandpur, Rupinder, Saraf, Parang, Wang, Wei, Cadena, Jose, Vullikanti, Anil, Korkmaz, Gizem, Kuhlman, Chris, Marathe, Achla, Zhao, Liang, Hua, Ting, Chen, Feng, Lu, Chang-Tien, Huang, Bert, Srinivasan, Aravind, Trinh, Khoa, Getoor, Lise, Katz, Graham, Doyle, Andy, Ackermann, Chris, Zavorin, Ilya, Ford, Jim, Summers, Kristen, Fayed, Youssef, Arredondo, Jaime, Gupta, Dipak, Mares, David
We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indica
Externí odkaz:
http://arxiv.org/abs/1402.7035