2321 papers
This paper introduces A3RL, a novel framework that integrates offline and online reinforcement learning through a confidence-aware active advantage-aligned sampling strategy to dynamically prioritize high-value data, thereby overcoming challenges like catastrophic forgetting and improving sample efficiency to outperform existing methods.
This paper introduces Texts as Time Series (TaTS), a novel framework that leverages the periodic alignment between paired texts and time series data to enhance multimodal forecasting and imputation performance in existing numerical-only models without requiring architectural changes.
The paper proposes LaVCa, a novel data-driven approach that leverages large language models to generate natural-language captions for images, thereby providing more accurate and detailed interpretations of human visual cortex voxel selectivity and revealing fine-grained functional differentiation within the visual cortex compared to existing deep neural network-based methods.
This paper proposes a Clustering-On-Difficulty (COD) framework that groups tasks by their difficulty scaling features to predict downstream LLM performance with high accuracy (1.55% error), effectively addressing challenges like emergent capabilities and inconsistent scaling patterns.
This study proposes a high-accuracy glioma subclass classification framework that fuses 2D and 3D UNET-segmented multimodal MRI images using weighted averaging and classifies them via a pre-trained ResNet50 model, achieving a 99.25% accuracy rate.
This paper proposes Leave-One-Out PPO (LOOP), a novel reinforcement learning method that combines variance reduction techniques from REINFORCE with the robustness of PPO to achieve a superior balance between sample efficiency and performance in fine-tuning text-to-image diffusion models.
This paper introduces OrthoGrad, a novel machine unlearning method that effectively removes the influence of specific data by projecting unlearn gradients onto the subspace orthogonal to retain gradients, thereby mitigating interference and outperforming existing approaches even when only a small portion of the training data is available.
This paper introduces GlucoLens, an explainable machine learning system that integrates wearable sensor data, diet, and activity logs to accurately predict postprandial hyperglycemia and recommend personalized behavioral interventions for managing blood glucose levels.
The paper proposes IMPACT, a novel motion planning framework that leverages Vision-Language Models to infer environment semantics and generate anisotropic cost maps, enabling a contact-aware A* planner to safely navigate cluttered environments by distinguishing between acceptable and dangerous object contacts.
This paper introduces Smooth Prototype Equivalences (SPE), a framework utilizing invertible neural networks to match sparse, noisy empirical observations to prototypical dynamical behaviors, enabling the equation-free identification of invariant structures and classification of dynamical regimes in complex biological and physical systems.
This paper introduces MUSS, a novel multilevel subset selection method that significantly improves both the scalability and performance of relevant and diverse selection tasks in applications like recommender systems and RAG, while providing a constant factor approximation guarantee and a tighter theoretical bound for existing distributed approaches.
The paper introduces EDU-PRM, an entropy-driven process reward model that automatically identifies reasoning step boundaries using predictive entropy to eliminate manual annotations, achieving state-of-the-art performance with only 1.5% of the training data while significantly improving accuracy and reducing token usage.
This paper proposes MetaBoost, a novel hybrid framework combining SMOTE, ADASYN, and CTGAN to optimize data balancing for enhanced Metabolic Syndrome prediction, while utilizing counterfactual analysis to identify blood glucose and triglycerides as the most critical modifiable risk factors.
This study demonstrates that while Large Language Models can directly estimate item difficulty for K-5 assessments, a hybrid approach combining LLM-extracted cognitive and linguistic features with tree-based machine learning algorithms yields significantly higher predictive accuracy, offering a scalable alternative to resource-intensive field testing.
This paper introduces a vision-based reinforcement learning agent that achieves champion-level performance in Gran Turismo 7 by utilizing an asymmetric actor-critic framework to rely solely on ego-centric camera views and onboard sensors, thereby eliminating the need for external global localization while outperforming the game's built-in drivers.
This paper introduces a linear response framework that models neural networks as Bayesian statistical mechanical systems to efficiently compute susceptibility-based attribution scores, revealing a low-rank structure that isolates functional modules like multigram and induction heads in small transformers.
This paper proposes HetGL2R, a heterogeneous graph learning framework that integrates origin-destination flows, routes, and network topology via a tripartite graph and attribute-guided nodes to effectively rank critical road segments by capturing long-range spatial dependencies and functional similarities.
This paper presents a comprehensive review that consolidates fragmented evaluation efforts into a unified taxonomy of approximately 60 benchmarks, surveys AI-agent frameworks and collaboration protocols, and explores real-world applications and future research directions for autonomous AI agents.
This paper introduces StablePCA, a distributionally robust framework for extracting shared low-dimensional representations from multi-source data by maximizing worst-case explained variance, and addresses its inherent nonconvexity through a convex relaxation solved by an efficient Mirror-Prox algorithm with global convergence guarantees and a data-dependent certificate for solution tightness.
This paper proposes a penalized pessimistic personalized policy learning (P4L) framework that leverages individual latent variables to derive optimal policies for heterogeneous populations from offline data, achieving fast regret rates under weak coverage assumptions and outperforming existing methods in both simulations and real-world applications.