Differential privacy (DP) is a widely used approach for mitigating privacy risks when training machine learning models on sensitive data. DP mechanisms add noise during training to limit the risk of information leakage. The scale of the added noise is critical, as it determines the trade-off between privacy and utility. The standard practice is to select the noise scale to satisfy a given privacy budget ε. This privacy budget is in turn interpreted in terms of operational attack risks, such as accuracy, sensitivity, and specificity of inference attacks aimed to recover information about the training data records.

We consider the robust routing problem with uncertain travel times under the min-max regret criterion, which represents an extended and robust version of the classic traveling salesman problem (TSP) and vehicle routing problem (VRP). The general budget uncertainty set is employed to capture the uncertainty, which provides the capability to control the conservatism of obtained solutions and covers the commonly used interval uncertainty set as a special case. The goal is to obtain a robust solution that minimizes the maximum deviation from the optimal routing time in the worst-case scenario. Given the significant advancements and broad applications of neural combinatorial optimization methods in recent years, we present our initial attempt to combine neural approaches for solving this problem. We propose a dual multi-head cross attention mechanism to extract problem features represented by the inputted uncertainty sets. To tackle the built-in maximization problem, we derive the regret value by invoking a pre-trained model, subsequently utilizing it as the reward during the model training. Our experimental results on the robust TSP and VRP demonstrate the efficacy of our neural combinatorial optimization method, showcasing its ability to efficiently handle the robust routing problem of various sizes within a shorter time compared with alternative heuristic approaches.

We have included in the supplementary material the parts that we could not mention in the main paper. Section A covers the implementation details, Section B presents additional experiments, and Section C describes the detailed annotation process. Lastly, we have included a description of the license and ethical considerations in the Section D.

Multi-person pose estimation and tracking have been actively researched by the computer vision community due to their practical applicability. However, existing human pose estimation and tracking datasets have only been successful in typical scenarios, such as those without motion blur or with well-lit conditions. These RGB-based datasets are limited to learning under extreme motion blur situations or poor lighting conditions, making them inherently vulnerable to such scenarios.

Eq. (8) and generate samples library S

We consider the penalized distributionally robust optimization (DRO) problem with a closed, convex uncertainty set, a setting that encompasses learning using f-DRO and spectral/L-risk minimization.

We implement pipeline between data downloading and data ingestion to accelerate the training. After completing the computation of gradients, the worker would directly send the gradient with the token back to the PS in a non-blocking way. In this way, the fast workers would ingest much more data than the straggling workers. When a worker recovered from a failure, it would drop the previous state (e.g., data in the batch buffer and token) and proceed to deal with the new batch. The disappearance of a specific token would not change the correctness and efficiency of GBA.