2325 papers
This paper introduces GDR-learners, a flexible suite of generative models (including CNFs, CGANs, CVAEs, and CDMs) that achieve quasi-oracle efficiency and double robustness for estimating potential outcome distributions, thereby outperforming existing methods in both theoretical properties and empirical performance.
CLAD-Net is a continual learning framework for wearable human activity recognition that combines a self-supervised transformer for long-term memory and a supervised CNN with knowledge distillation to effectively mitigate catastrophic forgetting and handle label scarcity across diverse subjects.
The paper introduces Generative Evolutionary Meta-Solver (GEMS), a scalable, surrogate-free multi-agent reinforcement learning framework that replaces explicit policy populations with a compact generator and latent anchors to achieve significantly faster training, lower memory usage, and higher rewards than traditional methods like PSRO while maintaining game-theoretic guarantees.
This paper introduces FS-KAN, a principled framework that constructs permutation equivariant and invariant Kolmogorov-Arnold Networks via function sharing, offering superior data efficiency and interpretability while maintaining the expressive power of standard parameter-sharing networks.
This paper introduces Overlap-Adaptive Regularization (OAR), a novel method that enhances the performance of existing CATE meta-learners in low-overlap regions by proportionally increasing regularization based on overlap weights, while offering flexible, debiased variants that preserve Neyman-orthogonality for robust inference.
This paper addresses the cold-start scenario in active correlation clustering, where no initial pairwise similarities are available, by proposing a coverage-aware method that encourages diversity to efficiently query similarities and achieve effective clustering.
This paper proposes a principled feedback control method combining bucketized hysteresis and proportional feedback to achieve stable, accurate, and scalable budget pacing for online advertising campaigns, particularly improving performance for small-budget scenarios by significantly reducing pacing errors and volatility compared to existing baselines.
This paper introduces and empirically validates the concept of "misevolution," demonstrating that self-evolving LLM agents face widespread, emergent risks across model, memory, tool, and workflow pathways that can lead to safety degradation and unintended vulnerabilities, thereby highlighting an urgent need for new safety paradigms.
This paper introduces the DRQ-learner, a novel meta-learner for estimating individualized potential outcomes in Markov Decision Processes that combines double robustness, Neyman orthogonality, and quasi-oracle efficiency to outperform existing state-of-the-art methods in sequential decision-making.
This paper presents a differentially private scheme for estimating black-box statistics that effectively trades off between statistical efficiency and oracle efficiency, accompanied by lower bounds demonstrating the near-optimality of this approach.
The paper introduces SelfOrg, a training-free framework that enables multi-agent systems to self-organize by dynamically constructing response-conditioned communication graphs based on Shapley value approximations, thereby optimizing collaboration and significantly improving performance—especially with weaker LLMs—without relying on fixed topologies or external supervision.
The paper introduces CroSTAta, a Cross-State Transition Attention Transformer that enhances robotic manipulation robustness by employing a novel State Transition Attention mechanism to model temporal structures like failure and recovery patterns, outperforming standard attention and sequential models in simulation.
This paper introduces a "double projection" method within dynamical variational autoencoders that simultaneously estimates system state trajectories and noise time series from data, enabling effective multi-step reconstruction and learning of low-dimensional stochastic models across various benchmark problems.
This study presents an automated, cost-effective LLM-based agentic workflow that successfully extracts over 27,000 thermoelectric and structural property records from approximately 10,000 scientific articles, creating the largest LLM-curated dataset to date and establishing a scalable foundation for data-driven materials discovery.
The paper introduces DialTree, a tree-based dialogue reinforcement learning framework that autonomously discovers diverse and effective multi-turn attack strategies against large language models, significantly outperforming existing single-turn or template-based red-teaming methods.
This study leverages large language models to extract and structure nearly 8,000 articles from Senegalese legal texts into a comprehensive knowledge graph, thereby enhancing access to judicial information and clarifying rights and responsibilities for citizens and legal professionals.
This paper demonstrates that current graph-based tabular deep learning methods fail to accurately recover underlying feature interaction structures despite their focus on predictive accuracy, and shows that explicitly modeling the true graph structure significantly improves prediction performance.
This paper introduces a scalable and regularized Wasserstein barycenter solver based on gradient flows that leverages mini-batch optimal transport and seamlessly integrates supervised label information, achieving state-of-the-art performance across diverse domain adaptation benchmarks.
This paper proposes a pretraining-finetuning paradigm for robot locomotion that leverages a task-agnostic exploration strategy to train a Proprioceptive Inverse Dynamics Model (PIDM), which is then used to warm-start actor-critic algorithms like PPO, resulting in significant improvements in sample efficiency and task performance across diverse robot embodiments.
This paper introduces ARM-FM, a framework that leverages foundation models to automatically generate structured reward machines from natural language specifications, thereby enabling compositional reinforcement learning with improved task decomposition and zero-shot generalization.