10563 papers
This paper leverages Chinese open-weight LLMs that censor politically sensitive topics as a natural testbed to evaluate honesty elicitation and lie detection techniques, finding that methods like few-shot prompting and self-classification effectively increase truthful responses and detect falsehoods, though no approach completely eliminates deception.
This paper proposes a novel three-stage framework that combines inexpensive imperfect labels, supervised pretraining, and self-supervised refinement to achieve effective amortized optimization with significantly reduced costs and improved performance across challenging domains.
The paper introduces POET-X, a memory-efficient and scalable variant of the POET framework that utilizes optimized orthogonal equivalence transformations to enable the stable pretraining of billion-parameter large language models on a single GPU, overcoming the high memory and computational costs of the original implementation.
RoboPocket is a smartphone-based system that enhances robot imitation learning by using AR visual foresight to guide targeted data collection and asynchronous online finetuning, thereby doubling data efficiency and enabling instant policy iteration without requiring physical robot execution.
The paper proposes the Recurrent Action Transformer with Memory (RATE), a novel architecture that integrates a recurrent memory mechanism into transformers to overcome context length limitations in partially observable environments, demonstrating superior performance in memory-intensive offline reinforcement learning tasks while remaining competitive on standard benchmarks.
This paper introduces Crystal-GFN, a multi-environment, continuous-discrete GFlowNet that sequentially samples crystal structural attributes to efficiently generate diverse, valid materials with specific desirable properties and hard constraints, thereby accelerating the discovery of novel solid-state materials.
GeoTop is a mathematically principled framework that unifies Topological Data Analysis and Lipschitz-Killing Curvatures to resolve the diagnostic ambiguity of topologically equivalent structures by integrating robust topological signatures with precise geometric features, thereby achieving superior accuracy and interpretability in image classification tasks such as skin lesion diagnosis.
This paper presents a sample-optimal, locally differentially private algorithm for hypothesis selection that achieves the information-theoretic lower bound of using only rounds of interaction, thereby demonstrating the provable power of interactivity to overcome the sample complexity barrier inherent in non-interactive approaches.
This survey provides a comprehensive review and unified framework for Graph Neural Networks in EEG-based emotion recognition, categorizing existing methods by graph construction stages to offer guidance on their unique physiological foundations while outlining open challenges and future directions.
This paper investigates the applicability of classical generalization principles to list PAC learning, demonstrating that while uniform convergence remains equivalent to learnability, the sample compression conjecture fails as there exist list-learnable classes that cannot be compressed, even with arbitrarily large output lists.
This paper introduces the concept of agnostic tomography and presents an efficient algorithm that learns a stabilizer product state approximating an arbitrary quantum state as well as the best possible match within that class, running in polynomial time for constant fidelity.
This paper reviews and categorizes existing reward functions for reinforcement learning in autonomous driving into safety, comfort, progress, and traffic rule compliance, while highlighting their current limitations in standardization and context-awareness to propose future research directions for more robust and conflict-resolving reward designs.
This paper extends tracking guarantees for time-varying variational inequalities to non-monotone functions and periodic problems without sublinear solution paths, while also demonstrating that the associated discrete dynamical systems can exhibit either convergence or provably chaotic behavior.
This paper introduces a black-box meta-learning approach that optimizes intrinsic rewards to enhance data efficiency and generalization in sparse-reward continuous control environments, demonstrating its effectiveness compared to extrinsic rewards and meta-learned advantage functions.
The paper introduces AuToMATo, a novel persistence-based clustering algorithm that combines ToMATo with bootstrapping to provide robust, out-of-the-box performance without parameter tuning, making it particularly effective for topological data analysis applications like Mapper.
This paper establishes that low-degree polynomial tests can distinguish between correlated sparse stochastic block models and independent Erdős-Rényi graphs if and only if the subsampling probability exceeds the minimum of Otter's constant and the Kesten-Stigum threshold, thereby identifying a sharp computational transition for detection and partial recovery.
This paper introduces the Iterative Proportional Markovian Fitting (IPMF) procedure, a unified framework that combines Iterative Markovian Fitting and Iterative Proportional Fitting to solve the Schrödinger Bridge problem with proven convergence and improved stability for applications like unpaired domain translation.
The paper proposes ANOMIX, a graph anomaly detection framework that enhances reasoning capabilities for identifying subtle boundary anomalies by synthesizing informative hard negatives through a mixup strategy that interpolates normal and abnormal subgraph representations.
This paper proposes Curriculum-enhanced Group Distributionally Robust Optimization (CeGDRO), a novel approach that strategically prioritizes hard bias-confirming and easy bias-conflicting samples to initialize model weights in an unbiased vantage point, thereby overcoming the limitations of traditional curriculum learning in subpopulation shift scenarios and achieving state-of-the-art performance across benchmark datasets.
The paper introduces FlowCLAS, a hybrid framework that enhances normalizing flows for anomaly segmentation by integrating a contrastive loss with outlier exposure to bridge the performance gap between generative and discriminative methods, achieving state-of-the-art results on multiple robotics benchmarks.