Zobrazeno 1 - 10
of 2 391
pro vyhledávání: '"Khodak A."'
Disaggregated evaluation -- estimation of performance of a machine learning model on different subpopulations -- is a core task when assessing performance and group-fairness of AI systems. A key challenge is that evaluation data is scarce, and subpop
Externí odkaz:
http://arxiv.org/abs/2411.09730
Autor:
Taunay, Pierre-Yves C. R., Villafana, Willca, Vinoth, Sangeeta P., Kaganovich, Igor, Khodak, Andrei
A parametric study is conducted to quantify the effect of the keeper electrode geometry on the neutral flow quantities within orificed hollow cathodes, prior to cathode ignition. The keeper impinges directly on the flow out of the cathode orifice and
Externí odkaz:
http://arxiv.org/abs/2411.05090
Autor:
Xu, Zongzhe, Gupta, Ritvik, Cheng, Wenduo, Shen, Alexander, Shen, Junhong, Talwalkar, Ameet, Khodak, Mikhail
Following its success for vision and text, the "foundation model" (FM) paradigm -- pretraining large models on massive data, then fine-tuning on target tasks -- has rapidly expanded to domains in the sciences, engineering, healthcare, and beyond. Has
Externí odkaz:
http://arxiv.org/abs/2411.02796
Solving a linear system $Ax=b$ is a fundamental scientific computing primitive for which numerous solvers and preconditioners have been developed. These come with parameters whose optimal values depend on the system being solved and are often impossi
Externí odkaz:
http://arxiv.org/abs/2310.02246
Autor:
Khodak, Mikhail, Osadchiy, Ilya, Harris, Keegan, Balcan, Maria-Florina, Levy, Kfir Y., Meir, Ron, Wu, Zhiwei Steven
We study online meta-learning with bandit feedback, with the goal of improving performance across multiple tasks if they are similar according to some natural similarity measure. As the first to target the adversarial online-within-online partial-inf
Externí odkaz:
http://arxiv.org/abs/2307.02295
Autor:
Lin, Jingyang, Chen, Junyu, Lyu, Hanjia, Khodak, Igor, Chhabra, Divya, Richardson, Colby L Day, Prelipcean, Irina, Dylag, Andrew M, Luo, Jiebo
Diagnosis of adverse neonatal outcomes is crucial for preterm survival since it enables doctors to provide timely treatment. Machine learning (ML) algorithms have been demonstrated to be effective in predicting adverse neonatal outcomes. However, mos
Externí odkaz:
http://arxiv.org/abs/2303.15656
Autor:
Shen, Junhong, Li, Liam, Dery, Lucio M., Staten, Corey, Khodak, Mikhail, Neubig, Graham, Talwalkar, Ameet
Fine-tuning large-scale pretrained models has led to tremendous progress in well-studied modalities such as vision and NLP. However, similar gains have not been observed in many other modalities due to a lack of relevant pretrained models. In this wo
Externí odkaz:
http://arxiv.org/abs/2302.05738
Autor:
Kuo, Kevin, Thaker, Pratiksha, Khodak, Mikhail, Nguyen, John, Jiang, Daniel, Talwalkar, Ameet, Smith, Virginia
Hyperparameter tuning is critical to the success of federated learning applications. Unfortunately, appropriately selecting hyperparameters is challenging in federated networks. Issues of scale, privacy, and heterogeneity introduce noise in the tunin
Externí odkaz:
http://arxiv.org/abs/2212.08930
When applying differential privacy to sensitive data, we can often improve performance using external information such as other sensitive data, public data, or human priors. We propose to use the learning-augmented algorithms (or algorithms with pred
Externí odkaz:
http://arxiv.org/abs/2210.11222
Autor:
Harris, Keegan, Anagnostides, Ioannis, Farina, Gabriele, Khodak, Mikhail, Wu, Zhiwei Steven, Sandholm, Tuomas
In the literature on game-theoretic equilibrium finding, focus has mainly been on solving a single game in isolation. In practice, however, strategic interactions -- ranging from routing problems to online advertising auctions -- evolve dynamically,
Externí odkaz:
http://arxiv.org/abs/2209.14110