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Showing 1 to 4 of 4 for “"Learning Augmented Algorithms"”.
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Learning-Augmented Algorithms
Traditional worst case analysis of algorithms does not fully capture real world behavior in many instances. Inspired by the great success of machine learning algorithms for various practical tasks, there has been recent interest in moving beyond pessimistic analysis of algorithms through the use of …
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Learning to Update: Using Reinforcement Learning to Discover Policies for List Update
The use of machine learning models in algorithms design is a rapidly growing f ield, often termed learning-augmented algorithms. A notable advancement in this field is the use of reinforcement learning for algorithm discovery. Developing algorithms in this manner offers certain advantages, novelty …
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Optimism and Robustness: Learning From Structured and Semi-Random Inputs
Traditionally, algorithms have been studied under two regimes: worst-case analysis, which makes no assumptions about the input, and average-case analysis, which assumes that inputs are drawn from a certain distribution. However, real-world inputs rarely conform to either of these extremes. A new …
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Warm Start Algorithms for Bipartite Matching and Optimal Transport
… the worst-case time complexities of standard algorithms are often prohibitively high. To mitigate this, prior information or warm starts are commonly employed to accelerate computation. In this thesis, we propose two novel warm-start algorithms for solving the minimum-cost bipartite matching …