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Deep neural networks (DNNs) have been widely deployed in real-world, mission-critical applications, necessitating effective approaches to protect deep learning models against malicious attacks. Motivated by the high stealthiness and potential harm of backdoor attacks, a series of backdoor defense methods for DNNs have been proposed. However, most existing approaches require access to clean training data, hindering their practical use. Additionally, state-of-the-art (SOTA) solutions cannot simultaneously enhance model robustness and compactness in a data-free manner, which is crucial in resource-constrained applications. To address these challenges, in this paper, we propose Clean & Compact (C&C), an efficient data-free backdoor defense mechanism that can bring both purification and compactness to the original infected DNNs. Built upon the intriguing rank-level sensitivity to trigger patterns, C&C co-explores and achieves high model cleanliness and efficiency without the need for training data, making this solution very attractive in many real-world, resource-limited scenarios. Extensive evaluations across different settings consistently demonstrate that our proposed approach outperforms SOTA backdoor defense methods. 

Assortment optimization finds many important applications in both brick-and-mortar and online retailing. Decision makers select a subset of products to offer to customers from a universe of substitutable products, based on the assumption that customers purchase according to a Markov chain choice model, which is a very general choice model encompassing many popular models. The existing literature predominantly assumes that the customer arrival process and the Markov chain choice model parameters are given as input to the stochastic optimization model. However, in practice, decision makers may not have this information and must learn them while maximizing the total expected revenue on the fly. In “Online Learning for Constrained Assortment Optimization under the Markov Chain Choice Model,” S. Li, Q. Luo, Z. Huang, and C. Shi developed a series of online learning algorithms for Markov chain choice-based assortment optimization problems with efficiency, as well as provable performance guarantees.