受强制性开放获取政策约束的文章 - Alkis Kalavasis了解详情
可在其他位置公开访问的文章：7 篇
Statistical indistinguishability of learning algorithms
A Kalavasis, A Karbasi, S Moran, G Velegkas
International Conference on Machine Learning 40, 2023
强制性开放获取政策： US National Science Foundation, US Department of Defense, European Commission
Optimal Learners for Realizable Regression: PAC Learning and Online Learning
I Attias, S Hanneke, A Kalavasis, A Karbasi, G Velegkas
Advances in Neural Information Processing Systems 36, 2023
强制性开放获取政策： US National Science Foundation, US Department of Defense
Efficient algorithms for learning from coarse labels
D Fotakis, A Kalavasis, V Kontonis, C Tzamos
Conference on Learning Theory, 2060-2079, 2021
强制性开放获取政策： US National Science Foundation
Differentially private regression with unbounded covariates
J Milionis, A Kalavasis, D Fotakis, S Ioannidis
International Conference on Artificial Intelligence and Statistics, 3242-3273, 2022
强制性开放获取政策： US National Science Foundation
Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes
A Kalavasis, G Velegkas, A Karbasi
Advances in Neural Information Processing Systems 35, 2022
强制性开放获取政策： US National Science Foundation, US Department of Defense
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods
C Caramanis, D Fotakis, A Kalavasis, V Kontonis, C Tzamos
Advances in Neural Information Processing Systems 36, 2023
强制性开放获取政策： US National Science Foundation
Learning and covering sums of independent random variables with unbounded support
A Kalavasis, K Stavropoulos, E Zampetakis
Advances in Neural Information Processing Systems 35, 25185-25197, 2022
强制性开放获取政策： US National Science Foundation, US Department of Defense, UK Engineering and …
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