Learning, Algorithm Design and Beyond Worst-Case Analysis

在计算复杂性理论中，当输入的大小趋于无穷大时，算法的性能传统上由其最坏情况下的资源使用来定义。在实践中，这种渐近的最坏情况度量可能出于以下几个原因而不合适：最坏情况的性能可能仅发生在实际中从未发生的人为实例中；实际发生的实例可能具有特殊的结构特征；算法的平均情况性能可能比其最坏情况好得多；或者启发式算法可能被经验地观察到性能良好。研讨会将侧重于这些非最坏情况下的现象。它将探索投入分布的概率模型，投入的结构化类别，如种植模型，以及绩效的经验衡量。工作坊的目标是提供最适合实际评估算法的工具。工作坊将加强数学、计算机科学、统计学和运筹学研究界之间的交流。它将向所有潜在的参与者开放，研讨会的成果(包括演讲的录像)将分发给公众，以征求意见和参与。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.