The Kalman filter and Rauch-Tung-Striebel (RTS) smoother are optimal for state estimation in linear dynamic systems. With nonlinear systems, the challenge consists in how to propagate uncertainty through the state transitions and output function. For the case of a neural network model, we enable accurate uncertainty propagation using a recent state-of-the-art analytic formula for computing the mean and covariance of a deep neural network with Gaussian input. We argue that cross entropy is a more appropriate performance metric than RMSE for evaluating the accuracy of filters and smoothers. We demonstrate the superiority of our method for state estimation on a stochastic Lorenz system and a Wiener system, and find that our method enables more optimal linear quadratic regulation when the state estimate is used for feedback.

Traditional collaborative filtering (CF) methods, relying on user - item interaction matrices, effectively capture latent patterns but face challenges such as data sparsity, cold - start problems, and limited contextual integration . To address these issues, M atrix F actorization (MF) techniques such as Singular Value Decomposition (SVD) and Alternating Least Squares (ALS) [1, 2 ] have been employed, improving accuracy but still struggling with sparsity and contextual richness. This has spurred the integration of side information, such as item content, social networks, and knowledge graphs, to enhance CF performance [3, 4 ] . Graph - based CF methods have emerged as a promising alternative, leveraging graph structures to model user - item interactions more effectively. Early approaches, such as ItemRank [5] and BiRank [6], used label propagation but lacked optimization capabilities . More advanced techniques, like HOP - Rec [7], integrated random walks with BPR . However, these models remain highly sensitive to hyperparameter tuning and often fail to capture high - order collaborative signals effectively . Graph Neural Networks (GNNs) have revolutionized recommendation systems by capturing complex user - item interactions, particularly in sparse data scenarios . Models like GC - MC [8] and PinSage [9] enhance user - item and item - item relationships, while SpectralCF [10] leverages spectral convolutions but faces scalability challenges.

Neural Information Processing Systems http://nips.cc/

Abstract--Most educational recommender systems are tuned and judged on click-or rating-based relevance, leaving their true pedagogical impact unclear . We introduce OBER--an Outcome-Based Educational Recommender that embeds learning outcomes and assessment items directly into the data schema, so any algorithm can be evaluated on the mastery it fosters. OBER uses a minimalist entity-relation model, a log-driven mastery formula, and a plug-in architecture. Integrated into an e-learning system in non-formal domain, it was evaluated trough a two-week A/B/C test with over 5 700 learners across three methods: fixed expert trajectory, collaborative filtering (CF), and knowledge-based (KB) filtering. CF maximized retention, but the fixed path achieved the highest mastery. Because OBER derives business, relevance, and learning metrics from the same logs, it lets practitioners weigh relevance and engagement against outcome mastery with no extra testing overhead. The framework is method-agnostic and readily extensible to future adaptive or context-aware recommenders. Index T erms--recommendation systems, e-learning, evaluation, assessment, intended learning outcomes, constructive alingment, empirical software engineering.

Synthetic tabular data generation is increasingly essential in data management, supporting downstream applications when real-world and high-quality tabular data is insufficient. Existing tabular generation approaches, such as generative adversarial networks (GANs), diffusion models, and fine-tuned Large Language Models (LLMs), typically require sufficient reference data, limiting their effectiveness in domain-specific databases with scarce records. While prompt-based LLMs offer flexibility without parameter tuning, they often fail to capture dataset-specific feature-label dependencies and generate redundant data, leading to degradation in downstream task performance. To overcome these issues, we propose ReFine, a framework that (i) derives symbolic "if-then" rules from interpretable models and embeds them into prompts to explicitly guide generation toward domain-specific feature distribution, and (ii) applies a dual-granularity filtering strategy that suppresses over-sampling patterns and selectively refines rare but informative samples to reduce distributional imbalance. Extensive experiments on various regression and classification benchmarks demonstrate that ReFine consistently outperforms state-of-the-art methods, achieving up to 0.44 absolute improvement in R-squared for regression and 10.0 percent relative improvement in F1 score for classification tasks.

Retrieval-augmented generation (RAG) has become a fundamental paradigm for addressing the challenges faced by large language models in handling real-time information and domain-specific problems. Traditional RAG systems primarily rely on the in-context learning (ICL) capabilities of the large language model itself. Still, in-depth research on the specific capabilities needed by the RAG generation model is lacking, leading to challenges with inconsistent document quality and retrieval system imperfections. Even the limited studies that fine-tune RAG generative models often \textit{lack a granular focus on RAG task} or \textit{a deeper utilization of chain-of-thought processes}. To address this, we propose that RAG models should possess three progressively hierarchical abilities (1) Filtering: the ability to select relevant information; (2) Combination: the ability to combine semantic information across paragraphs; and (3) RAG-specific reasoning: the ability to further process external knowledge using internal knowledge. Thus, we introduce our new RAG instruction fine-tuning method, Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation (HIRAG) incorporates a "think before answering" strategy. This method enhances the model's open-book examination capability by utilizing multi-level progressive chain-of-thought. Experiments show that the HIRAG training strategy significantly improves the model's performance on datasets such as RGB, PopQA, MuSiQue, HotpotQA, and PubmedQA.

Emerging ethical approaches have attempted to filter pretraining material, but such approaches have been ad hoc and failed to take context into account. We offer an approach to filtering grounded in law, which has directly addressed the tradeoffs in filtering material.

Precision Livestock Farming (PLF) has emerged as a critical field for monitoring and improving animal health and behavior[1]. Accurate and continuous tracking of livestock behavior is essential for identifying early signs of health issues an d enabling timely intervention. Traditional methods for monitoring pig behavior, such as manual observation, are labor - intensive, limited in scalability, and prone to inaccuracies [2]. Recent advancements in PLF have introduced automated systems that lev erage biosensors to track behavior in real time. These sensors, often attached to animals, collect data that is both costeffective and reliable, making them indispensable for modern livestock management [3,4].

--The analysis of conversational dynamics has gained increasing importance with the rise of large language model-based systems, which interact with users across diverse contexts. In this work, we propose a novel computational framework for constructing conversational graphs that capture the flow and structure of loosely organized dialogues, referred to as quasi-patterned conversations. We introduce the Filter & Reconnect method, a novel graph simplification technique that minimizes noise while preserving semantic coherence and structural integrity of conversational graphs. Through comparative analysis, we demonstrate that the use of large language models combined with our graph simplification technique has resulted in semantic metric S increasing by a factor of 2.06 compared to previous approaches while simultaneously enforcing a tree-like structure with 0 δ -hyperbolicity, ensuring optimal clarity in conversation modeling. This work provides a computational method for analyzing large-scale dialogue datasets, with practical applications related to monitoring automated systems such as chatbots, dialogue management tools, and user behavior analytics.

A. SMPL: A Skinned Multi-Person Linear Model The SMPL model ( Skinned Multi-Person Linear model) is a widely adopted statistical representation of the human body that combines a low-dimensional parameter space with linear blend skinning (LBS) to generate realistic, fully differentiable 3-D meshes. It underpins many state-of-the-art pipelines for monocular pose estimation, motion capture, and animation because it offers three essential properties: a compact pose-shape space learned from thousands of laser scans, an articulated skeletal structure compatible with traditional skinning, and analytic gradients with respect to both pose and shape parameters [6].