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Unlike standard methods that fail when reward models are poorly specified, ESR provably yields policies that asymptotically achieve optimal performance while remaining compatible with gradient-based optimization. The value of this approach is demonstrated through applications in health datasets, news recommendation, and computational materials science. The second part addresses large-scale logistics involving simultaneous routing and scheduling of commodity deliveries across intermodal networks. In collaboration with the United States Marine Corps and Navy, I develop a mixed-integer programming formulation for expeditionary warfare logistics that captures the various physical constraints placed on the network. To address computational limitations, I propose an efficient solution method based on dual decomposition that leverages Lagrangian duality to split the problem into smaller, computationally tractable subproblems. 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The value of this approach is demonstrated through applications in health datasets, news recommendation, and computational materials science. The second part addresses large-scale logistics involving simultaneous routing and scheduling of commodity deliveries across intermodal networks. In collaboration with the United States Marine Corps and Navy, I develop a mixed-integer programming formulation for expeditionary warfare logistics that captures the various physical constraints placed on the network. To address computational limitations, I propose an efficient solution method based on dual decomposition that leverages Lagrangian duality to split the problem into smaller, computationally tractable subproblems. 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The first part focuses on offline contextual bandits, drawing from work at Uber on driver incentive programs. I introduce Empirical Soft Regret (ESR), a novel loss function for value-based learning that addresses limitations of accuracy-based approaches in misspecified settings. Unlike standard methods that fail when reward models are poorly specified, ESR provably yields policies that asymptotically achieve optimal performance while remaining compatible with gradient-based optimization. The value of this approach is demonstrated through applications in health datasets, news recommendation, and computational materials science. The second part addresses large-scale logistics involving simultaneous routing and scheduling of commodity deliveries across intermodal networks. In collaboration with the United States Marine Corps and Navy, I develop a mixed-integer programming formulation for expeditionary warfare logistics that captures the various physical constraints placed on the network. To address computational limitations, I propose an efficient solution method based on dual decomposition that leverages Lagrangian duality to split the problem into smaller, computationally tractable subproblems. 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To address computational limitations, I propose an efficient solution method based on dual decomposition that leverages Lagrangian duality to split the problem into smaller, computationally tractable subproblems. 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