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A Distributionally Robust Approach to Shannon Limits ...
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由 V Malik 著作2024 — This paper shows that if the nominal distribution is Gaussian, then so is the worst-case source / noise distribution.
A Distributionally Robust Approach to Shannon Limits ...
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由 V Malik 著作2024 — We assume that the distribution of the source or of the additive channel noise is unknown and lies within a Wasserstein-2 ambiguity set of a given radius ...
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A Distributionally Robust Approach to Shannon Limits ...
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由 V Malik 著作2024 — Abstract—We consider the rate-distortion function for lossy source compression, as well as the channel capacity for error.
A Distributionally Robust Approach to Shannon Limits using ...
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2024年7月7日 — Wasserstein balls, which contain all probability measures within a pre-specified Wasserstein distance to a reference measure, have recently ...
Taylan Kargin - Google 学术搜索
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A Distributionally Robust Approach to Shannon Limits using the Wasserstein Distance. V Malik, T Kargin, V Kostina, B Hassibi. arXiv preprint arXiv:2405.06528 ...
IEEE ISIT 2024 || Athens, Greece || 7-12 July 2024
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2: A Distributionally Robust Approach to Shannon Limits using the Wasserstein Distance. Vikrant Malik, Taylan Kargin, Victoria Kostina, Babak Hassibi ...
A Distributionally Robust Approach to Regret Optimal ...
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This approach seeks to find control policies that perform well across all possible distributions within the uncertainty set, thereby providing robustness ...
IEEE ISIT 2024 || Athens, Greece || 7-12 July 2024
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2024年7月6日 — 2: A Distributionally Robust Approach to Shannon Limits using the Wasserstein Distance. Vikrant Malik, Taylan Kargin, Victoria Kostina, Babak ...
Theory and Applications in Machine Learning | Request PDF
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Wasserstein distributionally robust optimization seeks data-driven decisions that perform well under the most adverse distribution within a certain Wasserstein ...
A data-driven distributionally robust newsvendor model ...
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A Distributionally Robust Approach to Shannon Limits using the Wasserstein Distance ... Distributionally Robust Stochastic Optimization with Wasserstein Distance.