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pro vyhledávání: '"Li, Kwing Hei"'
Autor:
Haselwarter, Philipp G., Li, Kwing Hei, Aguirre, Alejandro, Gregersen, Simon Oddershede, Tassarotti, Joseph, Birkedal, Lars
Publikováno v:
Proc. ACM Program. Lang. 9, POPL, Article 41 (January 2025)
Properties such as provable security and correctness for randomized programs are naturally expressed relationally as approximate equivalences. As a result, a number of relational program logics have been developed to reason about such approximate equ
Externí odkaz:
http://arxiv.org/abs/2407.14107
Autor:
Haselwarter, Philipp G., Li, Kwing Hei, de Medeiros, Markus, Gregersen, Simon Oddershede, Aguirre, Alejandro, Tassarotti, Joseph, Birkedal, Lars
Publikováno v:
Proc. ACM Program. Lang. 8, OOPSLA2, Article 313 (October 2024), 30 pages
We present Tachis, a higher-order separation logic to reason about the expected cost of probabilistic programs. Inspired by the uses of time credits for reasoning about the running time of deterministic programs, we introduce a novel notion of probab
Externí odkaz:
http://arxiv.org/abs/2405.20083
Autor:
Aguirre, Alejandro, Haselwarter, Philipp G., de Medeiros, Markus, Li, Kwing Hei, Gregersen, Simon Oddershede, Tassarotti, Joseph, Birkedal, Lars
Probabilistic programs often trade accuracy for efficiency, and thus may, with a small probability, return an incorrect result. It is important to obtain precise bounds for the probability of these errors, but existing verification approaches have li
Externí odkaz:
http://arxiv.org/abs/2404.14223
Autor:
Li, Kwing Hei
This report presents a formalization of May's theorem in the proof assistant Coq. It describes how the theorem statement is first translated into Coq definitions, and how it is subsequently proved. Various aspects of the proof and related work are di
Externí odkaz:
http://arxiv.org/abs/2210.05342
Federated Learning (FL) allows parties to learn a shared prediction model by delegating the training computation to clients and aggregating all the separately trained models on the server. To prevent private information being inferred from local mode
Externí odkaz:
http://arxiv.org/abs/2205.06117
Autor:
Beutel, Daniel J., Topal, Taner, Mathur, Akhil, Qiu, Xinchi, Fernandez-Marques, Javier, Gao, Yan, Sani, Lorenzo, Li, Kwing Hei, Parcollet, Titouan, de Gusmão, Pedro Porto Buarque, Lane, Nicholas D.
Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to
Externí odkaz:
http://arxiv.org/abs/2007.14390