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Showing 1 to 6 of 6 for “"Meta Optimization"”.
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Automating the construction of a complier heuristics using machine learning
… allocation. To make matters worse, separate optimization phases have strong interactions and competing resource constraints. Compiler writers deal with system complexity by dividing the problem into multiple phases and devising approximate heuristics for each phase. However, to achieve …
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Aligning Machine Learning and Robust Decision-Making
… complexity of existing methods. We present a meta-optimization machine learning framework to learn fast approximations to general convex problems. We further apply this within an end-to-end learning framework which trains ML models with an optimization-based loss function to minimize the …
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A Computational Framework for Assessing and Optimizing the Performance of Observational Networks in 4D-Var Data Assimilation
… network configuration problem is formulated as a meta-optimization problem. Best values for parameters such as sensor location are obtained by optimizing a performance criterion, subject to the constraint posed by the 4D-Var optimization. Tractable computational solutions to this …
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On Bilevel Optimization without Full Unrolls: Methods and Applications
Bilevel optimization (BLO) problems are nested optimization problems where an outer objective must be minimized subject to the optimality of an inner objective. This nested structure poses several challenges, including the cost of running full unrolls of the inner problem for each outer parameter …
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Derivative-Free Meta-Blackbox Optimization on Manifold
… nonconvex, but potentially similar optimization problems poses a significant computational challenge in various engineering applications. This thesis presents the first meta-learning framework that leverages the shared structure among sequential tasks to improve the computational …
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Multiagent planning and learning using random decompositions and adaptive representations
… for heterogeneous teams by using embedded optimization processes to automate the search for decouplings among agents, thus decreasing the dependency on the domain knowledge. Motivated by the low computational complexity and theoretical guarantees of the Bayesian Optimization Algorithm (BOA) …