When learning to ride a bike, a child falls down a number of times before achieving the first success. As falling down usually has only mild consequences, it can be seen as a tolerable failure in exchange for a faster learning process, as it provides rich information about an undesired behavior. In the context of Bayesian optimization under unknown constraints (BOC), typical strategies for safe learning explore conservatively and avoid failures by all means. On the other side of the spectrum, non conservative BOC algorithms that allow failing may fail an unbounded number of times before reaching the optimum. In this work, we propose a novel decision maker grounded in control theory that controls the amount of risk we allow in the search as a function of a given budget of failures. Empirical validation shows that our algorithm uses the failures budget more efficiently in a variety of optimization experiments, and generally achieves lower regret, than state-of-the-art methods. In addition, we propose an original algorithm for unconstrained Bayesian optimization inspired by the notion of excursion sets in stochastic processes, upon which the failures-aware algorithm is built.
Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes
School of Electrical Engineering and Computer Science, The University of Queensland, Australia（昆士兰大学电子工程与计算机科学学院）；College of Information and Communications Technology, Central Queensland University, Australia（中央昆士兰大学信息与通信技术学院）
UCB Exploration for Fixed-Budget Bayesian Best Arm Identification
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（复旦大学脑科学智能技术研究院）；School of Mathematics and Statistics, Wuhan University（武汉大学数学与统计学院）
Asymptotically Optimal Best Arm Identification with Fixed-Budget under Differential Privacy
Institute of Data Science（数据科学研究所）；National University of Singapore（新加坡国立大学）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Mathematics（数学系）
ReliableNet: A Chance-Constrained Approach to Trustworthy Classification in Deep Learning
School of Agriculture and Science, University of KwaZulu-Natal（夸祖鲁-纳塔尔大学农业与科学学院）；AIMS Research and Innovation Centre, African Institute for Mathematical Sciences（非洲数学科学研究所AIMS研究与创新中心）；African Institute for Mathematical Sciences（非洲数学科学研究所）
