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Trust is crucial for ensuring the safety, security, and widespread adoption of automated vehicles (AVs), and if trust is lacking, drivers and the general public may hesitate to embrace this technology. This research seeks to investigate contextualized trust profiles in order to create personalized experiences for drivers in AVs with varying levels of reliability. A driving simulator experiment involving 70 participants revealed three distinct contextualized trust profiles (i.e., confident copilots, myopic pragmatists, and reluctant automators) identified through K-means clustering, and analyzed in relation to drivers' dynamic trust, dispositional trust, initial learned trust, personality traits, and emotions. The experiment encompassed eight scenarios where participants were requested to take over control from the AV in three conditions: a control condition, a false alarm condition, and a miss condition. To validate the models, a multinomial logistic regression model was constructed using the shapley additive explanations explainer to determine the most influential features in predicting contextualized trust profiles, achieving an F1-score of 0.90 and an accuracy of 0.89. In addition, an examination of how individual factors impact contextualized trust profiles provided valuable insights into trust dynamics from a user-centric perspective. The outcomes of this research hold significant implications for the development of personalized in-vehicle trust monitoring and calibration systems to modulate drivers' trust levels, thereby enhancing safety and user experience in automated driving. 

Current PEFT methods for LLMs can achieve either high quality, efficient training, or scalable serving, but not all three simultaneously. To address this limitation, we investigate sparse fine-tuning and observe a remarkable improvement in generalization ability. Utilizing this key insight, we propose a family of \underline{S}tructured \underline{S}parse \underline{F}ine-\underline{T}uning (\textbf{\model}) methods for LLMs, which \textit{concurrently achieve state-of-the-art fine-tuning performance, training efficiency, and inference scalability}. \model \mbox{accomplishes this by ``selecting sparsely and computing densely". It selects a few} heads and channels in the MHA and FFN modules for each Transformer block, respectively. Next, it co-permutes weight matrices on both sides of the coupled structures in LLMs to connect the selected components in each layer into a dense submatrix. Finally, \model performs in-place gradient updates on all submatrices. Through theoretical analysis and empirical results, our method prevents overfitting and forgetting, delivers SOTA performance on both commonsense and arithmetic reasoning with 4.6$$\%$$ and 1.3$$\%$$ average improvements compared to LoRA, and surpasses full FT by 11.5$$\%$$ when generalizing to various domains after instruction tuning. Using our partial backpropagation algorithm, \model saves training memory up to 3$$\times$$ and improves latency by 1.5-2.7$$\times$$ compared to full FT, while delivering an average 10\% improvement over LoRA on both metrics. We further demonstrate that the weight updates in \model can be decoupled into adapters, enabling effective fusion, fast switch, and efficient parallelism for serving multiple fine-tuned models. 

Introduces EM‑EDM, an AL framework using expectation‑maximization to model heterogeneous student pedagogical strategies across large continuous state spaces. EM‑EDM outperforms four AL baselines and two DRL policies on two pedagogical action prediction tasks. 

A key challenge in e-learning environments like Intelligent Tutoring Systems (ITSs) is to induce effective pedagogical policies efficiently. While Deep Reinforcement Learning (DRL) often suffers from \textbf{\emph{sample inefficiency}} and \textbf{\emph{reward function}} design difficulty, Apprenticeship Learning (AL) algorithms can overcome them. However, most AL algorithms can not handle heterogeneity as they assume all demonstrations are generated with a homogeneous policy driven by a single reward function. Still, some AL algorithms which consider heterogeneity, often can not generalize to large continuous state space and only work with discrete states. In this paper, we propose an expectation-maximization(EM)-EDM, a general AL framework to induce effective pedagogical policies from given optimal or near-optimal demonstrations, which are assumed to be driven by heterogeneous reward functions. We compare the effectiveness of the policies induced by our proposed EM-EDM against four AL-based baselines and two policies induced by DRL on two different but related tasks that involve pedagogical action prediction. Our overall results showed that, for both tasks, EM-EDM outperforms the four AL baselines across all performance metrics and the two DRL baselines. This suggests that EM-EDM can effectively model complex student pedagogical decision-making processes through the ability to manage a large, continuous state space and adapt to handle diverse and heterogeneous reward functions with very few given demonstrations. 

Introduces OAT (Offline with Augmented Trajectories), a generative sub-trajectory augmentation method designed to enhance off-policy evaluation accuracy. Experiments across robotics, healthcare, and e-learning show substantial performance gains over baselines. 

In the realm of reinforcement learning (RL), off-policy evaluation (OPE) holds a pivotal position, especially in high-stake human-centric scenarios such as e-learning and healthcare. Applying OPE to these domains is often challenging with scarce and underrepresentative offline training trajectories. Data augmentation has been a successful technique to enrich training data. However, directly employing existing data augmentation methods to OPE may not be feasible, due to the Markovian nature within the offline trajectories and the desire for generalizability across diverse target policies. In this work, we propose an offline trajectory augmentation approach, named \textbf{OAT}, to specifically facilitate OPE in human-involved scenarios. We propose sub-trajectory mining to extract potentially valuable sub-trajectories from offline data, and diversify the behaviors within those sub-trajectories by varying coverage of the state-action space. Our work was empirically evaluated in a wide array of environments, encompassing both simulated scenarios and real-world domains like robotic control, healthcare, and e-learning, where the training trajectories include varying levels of coverage of the state-action space. By enhancing the performance of a variety of OPE methods, our work offers a promising path forward for tackling OPE challenges in situations where human-centric data may be limited or underrepresentative.