The reviewers evaluated the paper and there was broad agreement that this is quality work. However, there was still some concerns about readability so we would urge you to revise the submission for the camera-ready.

The paper addresses the issue of exploiting correlation structures in Markov Decision Processes with discrete state spaces. The authors identify a gap that currently makes working with discrete state spaces problematic - that there is no principled method for modelling the state correlations that is flexible enough to accommodate all the ways in which these correlations could be exploited. The paper presents a hierarchical Bayesian model and proposes a variational inference method to find solutions. The model and procedure presented in the paper are an original application of variational inference, and represent a more general method for dealing with correlation structures than anything I have encountered before. The authors have done a great job of demonstrating this by employing three vastly different problem domains. It is unusual to see Imitation Learning, System Identification and Reinforcement Learning all being tested under a new model in one paper.

This paper has a lot of content: Interesting cognitive science question of modelling human decision-making, data fusion of texts and eye movements, modelled with a new dynamic Bayesian nonparametric model, and introduces a new sampler for the model. This paper received a special amount of attention, 5 reviews which were needed because the paper makes several different kinds of contributions. Hence it is not a stereotypical good conference paper having one neat idea and presenting convincing theoretical or empirical support for it. Reviewers discussed the paper intensively, concluding that the paper is likely to be interesting at NeurIPS, and since there is not easy fix to make it more suitable to the format such as dividing it into two papers, it is good enough to be accepted though not among the best papers. Clarity can easily be improved by the authors, and additional details added in both the paper and the supplement.

This paper presents a method for structural learning of a BN given observational data. The work is mainly theoretical, and for the proposal some assumptions are taken. A great effort is also given in presenting and develop theoretically the complexity of the algorithm. One of the key points in the proposed algorithm is the use of Fourier basis vectors (coefficients) and how they are applied in the compressed sensing step. I haven't checked thoroughly all the mathematical part, which is the core of the paper.

In many real-world scenarios, such as gas leak detection or environmental pollutant tracking, solving the Inverse Source Localization and Characterization problem involves navigating complex, dynamic fields with sparse and noisy observations. Traditional methods face significant challenges, including partial observability, temporal and spatial dynamics, out-of-distribution generalization, and reward sparsity. To address these issues, we propose a hierarchical framework that integrates Bayesian inference and reinforcement learning. The framework leverages an attention-enhanced particle filtering mechanism for efficient and accurate belief updates, and incorporates two complementary execution strategies: Attention Particle Filtering Planning and Attention Particle Filtering Reinforcement Learning. These approaches optimize exploration and adaptation under uncertainty. Theoretical analysis proves the convergence of the attention-enhanced particle filter, while extensive experiments across diverse scenarios validate the framework's superior accuracy, adaptability, and computational efficiency. Our results highlight the framework's potential for broad applications in dynamic field estimation tasks.

Farmers rely on in-field observations to make well-informed crop management decisions to maximize profit and minimize adverse environmental impact. However, obtaining real-world crop state measurements is labor-intensive, time-consuming and expensive. In most cases, it is not feasible to gather crop state measurements before every decision moment. Moreover, in previous research pertaining to farm management optimization, these observations are often assumed to be readily available without any cost, which is unrealistic. Hence, enabling optimization without the need to have temporally complete crop state observations is important. An approach to that problem is to include measuring as part of decision making. As a solution, we apply reinforcement learning (RL) to recommend opportune moments to simultaneously measure crop features and apply nitrogen fertilizer. With realistic considerations, we design an RL environment with explicit crop feature measuring costs. While balancing costs, we find that an RL agent, trained with recurrent PPO, discovers adaptive measuring policies that follow critical crop development stages, with results aligned by what domain experts would consider a sensible approach. Our results highlight the importance of measuring when crop feature measurements are not readily available.

Large language models (LLMs) have emerged as powerful tools in natural language processing (NLP), showing a promising future of artificial generated intelligence (AGI). Despite their notable performance in the general domain, LLMs have remained suboptimal in the field of education, owing to the unique challenges presented by this domain, such as the need for more specialized knowledge, the requirement for personalized learning experiences, and the necessity for concise explanations of complex concepts. To address these issues, this paper presents a novel LLM for education named WisdomBot, which combines the power of LLMs with educational theories, enabling their seamless integration into educational contexts. To be specific, we harness self-instructed knowledge concepts and instructions under the guidance of Bloom's Taxonomy as training data. To further enhance the accuracy and professionalism of model's response on factual questions, we introduce two key enhancements during inference, i.e., local knowledge base retrieval augmentation and search engine retrieval augmentation during inference. We substantiate the effectiveness of our approach by applying it to several Chinese LLMs, thereby showcasing that the fine-tuned models can generate more reliable and professional responses.

This paper proposes a robot action planning scheme that provides an efficient and probabilistically safe plan for a robot interacting with an unconcerned human -- someone who is either unaware of the robot's presence or unwilling to engage in ensuring safety. The proposed scheme is predictive, meaning that the robot is required to predict human actions over a finite future horizon; such predictions are often inaccurate in real-world scenarios. One possible approach to reduce the uncertainties is to provide the robot with the capability of reasoning about the human's awareness of potential dangers. This paper discusses that by using a binary variable, so-called danger awareness coefficient, it is possible to differentiate between concerned and unconcerned humans, and provides a learning algorithm to determine this coefficient by observing human actions. Moreover, this paper argues how humans rely on predictions of other agents' future actions (including those of robots in human-robot interaction) in their decision-making. It also shows that ignoring this aspect in predicting human's future actions can significantly degrade the efficiency of the interaction, causing agents to deviate from their optimal paths. The proposed robot action planning scheme is verified and validated via extensive simulation and experimental studies on a LoCoBot WidowX-250.

Causal discovery --the process of identifying cause-and-effect relationships from observational data-- is a pivotal challenge in artificial intelligence (AI) and machine learning. Unveiling causal structures enables robust predictions, facilitates counterfactual reasoning, and enhances decision-making processes in complex systems [1]. Traditional methods for causal discovery often rely on statistical tests for independence and structural equation modeling, which may not scale efficiently with high-dimensional data or effectively capture intricate non-linear relationships [2, 3]. In recent years, machine learning models, particularly deep learning architectures, have achieved remarkable success in predictive tasks. However, these models are typically considered "black boxes" due to their lack of interpretability. This opacity has led to a growing interest in explainable AI (XAI) techniques, with Shapley values emerging as a prominent method for interpreting model predictions [4]. Shapley values, grounded in cooperative game theory, provide a principled approach to attributing the contribution of each feature to the output of a model by quantifying the average marginal contribution of a feature across all possible subsets of features [5]. While Shapley values offer valuable insights into feature importance within a model's predictive framework, the link between feature importance and causal influence is non-trivial.

Offline multi-agent reinforcement learning (MARL) addresses key limitations of online MARL, such as safety concerns, expensive data collection, extended training intervals, and high signaling overhead caused by online interactions with the environment. In this work, we propose an offline MARL algorithm for radio resource management (RRM), focusing on optimizing scheduling policies for multiple access points (APs) to jointly maximize the sum and tail rates of user equipment (UEs). We evaluate three training paradigms: centralized, independent, and centralized training with decentralized execution (CTDE). Our simulation results demonstrate that the proposed offline MARL framework outperforms conventional baseline approaches, achieving over a 15\% improvement in a weighted combination of sum and tail rates. Additionally, the CTDE framework strikes an effective balance, reducing the computational complexity of centralized methods while addressing the inefficiencies of independent training. These results underscore the potential of offline MARL to deliver scalable, robust, and efficient solutions for resource management in dynamic wireless networks.