10 accepted papers
Transaction cost optimization (TCO) of online portfolio selection is crucial in computing science, due to the significant impact of transaction costs in practical short-term trading. Moreover, sparsity of portfolio vector is often desired to enhance stability and decrease risk. However, there is a l…
Multimodal fusion has an important research value in environmental perception for autonomous driving. Among them, BEVFusion has become one of the mainstream framework for LiDAR camera fusion by unifying multimodal features in the bird's-eye view (BEV) space. However, its performance is limited by in
Multimodal fusion has an important research value in environmental perception for autonomous driving. Among them, BEVFusion has become one of the mainstream framework for LiDAR camera fusion by unifying multimodal features in the bird’s-eye view (BEV) space. However, its performance is limited by in…
To address the challenges of insufficient wind resistance stability and low detection accuracy for strand breakage defects during overhead power line inspection, this study proposes a dual-mode inspection robot solution integrating a Dynamic Conforming Pressing (DCP) mechanism and an infrared vision
This paper presents an uncertainty-aware exploration framework for cooperative underwater operations using multiple unmanned underwater vehicles (UUVs). The proposed framework leverages prior environmental information to iteratively integrate task planning, task assignment, and prior belief updating
Accurate relative state observation of Unmanned Underwater Vehicles (UUVs) for tracking uncooperative targets remains a significant challenge due to the absence of GPS, complex underwater dynamics, and sensor limitations. Existing localization approaches rely on either global positioning infrastruct
Data augmentation has been proven effective for training high-accuracy convolutional neural network classifiers by preventing overfitting. However, building deep neural networks in real-world scenarios requires not only high accuracy on clean data but also robustness when data distributions shift. W…
Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies. Therefore, OPE is a key step in applying reinforcement learning to real-world domains such as medical treatment, where interactive data col…
Learning monotonic models with respect to a subset of the inputs is a desirable feature to effectively address the fairness, interpretability, and generalization issues in practice. Existing methods for learning monotonic neural networks either require specifically designed model structures to ensur…
Off-policy evaluation provides an essential tool for evaluating the effects of different policies or treatments using only observed data. When applied to high-stakes scenarios such as medical diagnosis or financial decision-making, it is essential to provide provably correct upper and lower bounds o…