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Showing 1 to 5 of 5 for “"Distributional robustness"”.

  1. Model-Based and Data-Driven Covariance Control: Theory and Applications

    … methods often struggle with ensuring safety and robustness in stochastic environments. This dissertation advances the theory of covariance steering (CS), which shifts the focus from controlling specific system states to steering entire state distributions under constraints. Historically, CS has …

    gatech Repository record for Model-Based and Data-Driven Covariance Control: Theory and Applications (opens in a new tab)

  2. A Systems Approach to the Modeling and Control of Molecular, Microparticle, and Biological Distributions

    … optimal control formulation, and worst-case and distributional robustness analysis. Free radical bulk polymerization is the model system for molecular distributions. In situ ATR-FTIR spectroscopy was used to determine monomer concentration; off-line gel permeation chromatography was used to …

    uiuc Repository record for A Systems Approach to the Modeling and Control of Molecular, Microparticle, and Biological Distributions (opens in a new tab)

  3. Robust Inference via Optimal Transport Ambiguity Sets

    … two widely used statistical algorithms with distributional robustness. The Kalman filter enables accurate, real-time tracking of latent states by assimilating noisy, indirect measurements over time. Its performance relies on precise state-space models for both the evolution dynamics and the …

    mit Repository record for Robust Inference via Optimal Transport Ambiguity Sets (opens in a new tab)

  4. Distributionally robust binary classifier under Wasserstein distance

    The robustification of statistical models has been a popular topic for decades. Statistical robustification and robust optimization are the two main approaches in the literature, where the former stabilizes the model output by removing the outlier points while the latter concerns more the outlier …

    calgary Repository record for Distributionally robust binary classifier under Wasserstein distance (opens in a new tab)

  5. Certifying robustness in inference and learning problems

    … answered. In this dissertation, a few topics in robustness of inference and learning methods are studied. One of the key issues with data-driven methods in practice is unexpected changes that can occur at inference time potentially affecting the performance of these methods, for instance, …

    uiuc Repository record for Certifying robustness in inference and learning problems (opens in a new tab)