Learning, Algorithm Design and Beyond Worst-Case Analysis

在计算复杂性理论中，算法的性能传统上由其最坏情况下的资源使用来定义，因为输入的大小趋于无穷大。在实践中，这种渐进的最坏情况的措施可能是不合适的几个原因：最坏的情况下的性能可能只发生在人为的情况下，从来没有发生在实践中;在实践中发生的实例可能有特殊的结构特征;算法的平均情况下的性能可能比它的最坏情况下好得多;或启发式算法可能会观察到经验表现良好。研讨会将重点讨论这些非最坏情况的现象。它将探索输入分布的概率模型，结构化的输入类，如种植模型，以及绩效的经验测量。讲习班的目标是提供最适合于实际评价算法的工具，加强数学、计算机科学、统计学和业务研究界之间的交流。研讨会将向所有潜在参与者开放，研讨会的结果（包括发言的录像）将分发给公众，供其发表意见和参与。组织者将鼓励学生参加研讨会，并将积极招募来自不同背景的科学家，为广泛的应用做出贡献。

In computational complexity theory, the performance of an algorithm is traditionally defined by its worst-case resource usage as the size of the input tends to infinity. In practice, this asymptotic worst-case measure may be inappropriate for several reasons: the worst-case performance may occur only for contrived instances that never occur in practice; the instances occurring in practice may have special structural features; the average-case performance of the algorithm may be much better than its worst case; or heuristic algorithms may empirically be observed to perform well. The workshop will focus on these non-worst-case phenomena. It will explore probabilistic models of the input distribution, structured classes of inputs such as planted models, and empirical measurement of performance. The goal will be to provide tools best suited for the practical evaluation of algorithms.The workshop will enhance communication among the mathematics, computer science, statistics and operations research communities. It will be open to all potential participants, and the workshop findings (including video recordings of presentations) will be distributed to the public for comment and engagement. The organizers will encourage students to attend the workshop, and will actively recruit scientists from a diversity of backgrounds to contribute to a wide range of applications.