Through theoretical analysis in TD-MPC implementation leads to persistent value and experiments, we argue that this issue is deeply rooted overestimation. It is also empirically observed that the performance in the structural policy mismatch between the data generation of TD-MPC2 is far from satisfactory at some policy that is always bootstrapped by the planner and high-dimensional locomotion tasks [33]. This phenomenon the learned policy prior. To mitigate such a mismatch in is closely connected to, yet distinct from, the well-known a minimalist way, we propose a policy regularization term overestimation bias arising from function approximation reducing out-of-distribution (OOD) queries, thereby improving errors and error accumulation in temporal difference learning value learning. Our method involves minimum changes [39, 37, 7]. More precisely, we identify the underlying on top of existing frameworks and requires no additional issue as policy mismatch. The behavior policy generated by computation. Extensive experiments demonstrate that the the MPC planner governs data collection, creating a buffered proposed approach improves performance over baselines data distribution that does not directly align with the learned such as TD-MPC2 by large margins, particularly in 61-DoF value or policy prior.

Nonparametric regression is one of the most extensively studied problems in past decades due to its remarkable flexibility in modeling the relationship between an input X and output Y . While numerous algorithms have been developed, the strong guarantees of learnability and generalization rely on the fact that there are a sufficient number of training samples and that the future data possess the same distribution as the training. However, training sample scarcity in the target domain of interest and distribution shifts occur frequently in practical applications and deteriorate the effectiveness of most existing algorithms both empirically and theoretically. Transfer learning has emerged as an appealing and promising paradigm for addressing these challenges by leveraging samples or pre-trained models from similar, yet not identical, source domains. In this work, we study the problem of transfer learning in the presence of the concept shifts for nonparametric regression over some specific reproducing kernel Hilbert spaces (RKHS). Specifically, we posit there are limited labeled samples from the target domain but sufficient labeled samples from a similar source domain where the concept shifted, namely, the conditional distribution of Y |X changes across domains, which implies the underlying regression function shifts.

Evaluating autonomous driving systems in complex and diverse traffic scenarios through controllable simulation is essential to ensure their safety and reliability. However, existing traffic simulation methods face challenges in their controllability. To address this, this paper proposes a novel diffusion-based and LLM-enhanced traffic simulation framework. Our approach incorporates a unique chain-of-thought (CoT) mechanism, which systematically examines the hierarchical structure of traffic elements and guides LLMs to thoroughly analyze traffic scenario descriptions step by step, enhancing their understanding of complex situations. Furthermore, we propose a Frenet-frame-based cost function framework that provides LLMs with geometrically meaningful quantities, improving their grasp of spatial relationships in a scenario and enabling more accurate cost function generation. Experiments on the Waymo Open Motion Dataset (WOMD) demonstrate that our method handles more intricate descriptions, generates a broader range of scenarios in a controllable manner, and outperforms existing diffusion-based methods in terms of efficiency.

This integration signifies a We introduce a pioneering unified library that leverages significant advancement in the field, facilitating a deeper depth anything, segment anything models to augment neural understanding of images through language models and improving comprehension in language-vision model zero-shot understanding. the efficacy of multi-modal tasks. This library synergizes the capabilities of the In recent works on text-image multi-modal tasks [1, 6, Depth Anything Model (DAM), Segment Anything Model 7, 9], the primary focus has been on training specific models (SAM), and GPT-4V, enhancing multimodal tasks such as to enhance the similarity between text-image pairs and vision-question-answering (VQA) and composition reasoning.

Scene understanding, defined as learning, extraction, and representation of interactions among traffic elements, is one of the critical challenges toward high-level autonomous driving (AD). Current scene understanding methods mainly focus on one concrete single task, such as trajectory prediction and risk level evaluation. Although they perform well on specific metrics, the generalization ability is insufficient to adapt to the real traffic complexity and downstream demand diversity. In this study, we propose PreGSU, a generalized pre-trained scene understanding model based on graph attention network to learn the universal interaction and reasoning of traffic scenes to support various downstream tasks. After the feature engineering and sub-graph module, all elements are embedded as nodes to form a dynamic weighted graph. Then, four graph attention layers are applied to learn the relationships among agents and lanes. In the pre-train phase, the understanding model is trained on two self-supervised tasks: Virtual Interaction Force (VIF) modeling and Masked Road Modeling (MRM). Based on the artificial potential field theory, VIF modeling enables PreGSU to capture the agent-to-agent interactions while MRM extracts agent-to-road connections. In the fine-tuning process, the pre-trained parameters are loaded to derive detailed understanding outputs. We conduct validation experiments on two downstream tasks, i.e., trajectory prediction in urban scenario, and intention recognition in highway scenario, to verify the generalized ability and understanding ability. Results show that compared with the baselines, PreGSU achieves better accuracy on both tasks, indicating the potential to be generalized to various scenes and targets. Ablation study shows the effectiveness of pre-train task design.

Joint pedestrian trajectory prediction has long grappled with the inherent unpredictability of human behaviors. Recent investigations employing variants of conditional diffusion models in trajectory prediction have exhibited notable success. Nevertheless, the heavy dependence on accurate historical data results in their vulnerability to noise disturbances and data incompleteness. To improve the robustness and reliability, we introduce the Guided Full Trajectory Diffuser (GFTD), a novel diffusion model framework that captures the joint full (historical and future) trajectory distribution. By learning from the full trajectory, GFTD can recover the noisy and missing data, hence improving the robustness. In addition, GFTD can adapt to data imperfections without additional training requirements, leveraging posterior sampling for reliable prediction and controllable generation. Our approach not only simplifies the prediction process but also enhances generalizability in scenarios with noise and incomplete inputs. Through rigorous experimental evaluation, GFTD exhibits superior performance in both trajectory prediction and controllable generation.

Many existing two-phase kernel-based hypothesis transfer learning algorithms employ the same kernel regularization across phases and rely on the known smoothness of functions to obtain optimality. Therefore, they fail to adapt to the varying and unknown smoothness between the target/source and their offset in practice. In this paper, we address these problems by proposing Smoothness Adaptive Transfer Learning (SATL), a two-phase kernel ridge regression(KRR)-based algorithm. We first prove that employing the misspecified fixed bandwidth Gaussian kernel in target-only KRR learning can achieve minimax optimality and derive an adaptive procedure to the unknown Sobolev smoothness. Leveraging these results, SATL employs Gaussian kernels in both phases so that the estimators can adapt to the unknown smoothness of the target/source and their offset function. We derive the minimax lower bound of the learning problem in excess risk and show that SATL enjoys a matching upper bound up to a logarithmic factor. The minimax convergence rate sheds light on the factors influencing transfer dynamics and demonstrates the superiority of SATL compared to non-transfer learning settings. While our main objective is a theoretical analysis, we also conduct several experiments to confirm our results.

In this work, we propose a new mechanism for releasing differentially private functional summaries called the Independent Component Laplace Process, or ICLP, mechanism. By treating the functional summaries of interest as truly infinite-dimensional objects and perturbing them with the ICLP noise, this new mechanism relaxes assumptions on data trajectories and preserves higher utility compared to classical finite-dimensional subspace embedding approaches in the literature. We establish the feasibility of the proposed mechanism in multiple function spaces. Several statistical estimation problems are considered, and we demonstrate by slightly over-smoothing the summary, the privacy cost will not dominate the statistical error and is asymptotically negligible. Numerical experiments on synthetic and real datasets demonstrate the efficacy of the proposed mechanism.

This work studies the problem of transfer learning under the functional linear regression model framework, which aims to improve the estimation and prediction of the target model by leveraging the information from related source models. We measure the relatedness between target and source models using Reproducing Kernel Hilbert Spaces (RKHS) norm, allowing the type of information being transferred to be interpreted by the structural properties of the spaces. Two transfer learning algorithms are proposed: one transfers information from source tasks when we know which sources to use, while the other one aggregates multiple transfer learning results from the first algorithm to achieve robust transfer learning without prior information about the sources. Furthermore, we establish the optimal convergence rates for the prediction risk in the target model, making the statistical gain via transfer learning mathematically provable. The theoretical analysis of the prediction risk also provides insights regarding what factors are affecting the transfer learning effect, i.e. what makes source tasks useful to the target task. We demonstrate the effectiveness of the proposed transfer learning algorithms on extensive synthetic data as well as real financial data application.