10561 papers
This paper introduces Reward-Conditioned Reinforcement Learning (RCRL), a framework that trains a single agent to optimize a family of reward specifications from a shared off-policy dataset, enabling robust and efficient adaptation to changing task preferences without sacrificing the simplicity of single-task training.
This paper introduces a novel clustering algorithm for unit hypersphere data based on the -dimensional generalized Kuramoto model, demonstrating its effectiveness and superior or comparable accuracy against traditional methods through experiments on both synthetic and real-world datasets.
This paper introduces Aura, a universal framework that enhances aviation time series forecasting by explicitly encoding and integrating three distinct types of multi-dimensional exogenous factors through a tailored tripartite mechanism, achieving state-of-the-art performance on large-scale industrial datasets.
This paper proposes a novel Axiomatic On-Manifold Shapley framework that utilizes optimal generative flows and Wasserstein-2 geodesics to eliminate off-manifold artifacts, ensuring geometric efficiency, reparameterization invariance, and superior semantic alignment in model attribution.
This paper introduces ARC-TGI, an open-source framework featuring human-validated Python generators that produce diverse, rule-consistent ARC-AGI tasks with natural language reasoning chains and code, addressing dataset overfitting by ensuring training examples collectively reveal underlying rules for scalable benchmarking.
This paper introduces BLINK, a trajectory-based recurrent state-space model that learns latent interaction dynamics from time-lapse microscopy to accurately predict and forecast NK cell cytotoxic outcomes while providing an interpretable representation of cellular behavioral modes.
This paper proposes a two-stage reward curriculum that decouples task-specific objectives from behavioral terms to improve exploration and training stability in robotic reinforcement learning, demonstrating superior performance and robustness across multiple environments compared to direct full-reward training.
This paper proposes FedBCGD and its accelerated variant FedBCGD+, novel federated learning algorithms that split model parameters into blocks to enable selective client uploads, thereby significantly reducing communication overhead and achieving faster convergence for large-scale deep models compared to existing methods.
The paper proposes SRasP, a novel method for Cross-Domain Few-Shot Learning that mitigates domain shift and improves generalization by using global semantic guidance to reorient and aggregate style gradients, thereby stabilizing training and encouraging convergence to flatter, more transferable solutions.
This paper demonstrates that a particle-guided diffusion model trained on advection-reaction-diffusion solutions can effectively generate physically consistent concentration fields and accurately predict outlet concentrations for gas-phase chemical reactions, even under unseen parameter conditions.
This paper establishes an exact correspondence between the computational power of recurrent graph neural networks and recurrent arithmetic circuits over real numbers by demonstrating their mutual ability to simulate each other's computations.
This paper theoretically and empirically demonstrates that analogical reasoning in transformers emerges from a unified mechanism where entities with similar properties are encoded into aligned representations, a capability that depends critically on specific training curricula and the explicit inclusion of identity bridges in the data.
This paper introduces fedCI and fedCI-IOD, a novel framework for privacy-preserving federated causal discovery that enables conditional independence testing and latent confounding analysis across heterogeneous, distributed datasets with non-identical variables and mixed data types.
This study demonstrates that simple lung cropping preprocessing effectively mitigates racial shortcut learning in chest X-ray diagnosis models by suppressing spurious racial cues while preserving diagnostic accuracy, thereby avoiding the typical fairness-accuracy trade-off.
This paper proposes Alt-FL, a novel federated learning framework that employs a round-based interleaving strategy of Differential Privacy, Homomorphic Encryption, and synthetic data to effectively balance privacy protection, learning quality, and system efficiency under varying resource constraints.
This paper introduces GeoDVF, a geometry-adaptive deep variational framework that jointly optimizes neural network-parameterized order parameters and trainable domain sizes to eliminate artificial stress and robustly discover stable and metastable ordered phases in the Landau-Brazovskii model from random initializations.
This paper proposes a trainable bitwise soft quantization layer that compresses neural network input features using sigmoid-approximated step functions, achieving significant data transmission reductions (5x to 16x) with minimal accuracy loss for efficient IoT applications.
This paper proposes a mechanism design framework for AI regulation that forces providers to bet on their model's compliance, proving that such mechanisms can achieve perfect market outcomes if and only if the set of non-compliant distributions forms a credal set, thereby bridging mechanism design and imprecise probability to create enforceable regulations.
This paper introduces the Sequential Thresholding of Coefficient of Variation (STCV), a novel sparse regression algorithm that replaces magnitude-based thresholding with a dimensionless statistical metric to ensure robust and accurate identification of governing equations in non-linear dynamics, even when data is normalized and noisy.
This paper introduces Stable-LoRA, a weight-shrinkage optimization strategy that resolves the feature learning instability caused by non-zero initialization in Low-Rank Adaptation (LoRA) while preserving its benefits and achieving superior performance across diverse tasks without additional memory costs.