Tag
This paper analyzes distance-preserving embeddings in inhomogeneous random graphs, providing tighter distortion bounds than classical worst-case results and introducing a GNN-augmented variant that learns universal features from small graphs.
This paper presents a unified algorithmic framework for distributed online submodular maximization under partition matroid constraints, achieving sublinear (1-1/e)-regret guarantees for both full-information and bandit feedback. It also introduces a bounded stochastic pipage rounding scheme to ensure cumulative sampling violations remain sublinear.
This paper introduces the Markov decision contest, a new problem model for reinforcement learning with pairwise preferences. It proves optimality guarantees for stationary policies, exact solvability in P, and presents a learning-efficient approximate algorithm.
This paper presents the first systematic study of uncertainty quantification (UQ) for Large Language Diffusion Models (LLDMs), proposing lightweight zero-shot uncertainty signals derived from the iterative denoising process and showing that LLDMs can achieve both fast inference and reliable hallucination detection with up to 100x lower computational overhead compared to sampling-based baselines.
This paper establishes the first population risk bounds for Kolmogorov-Arnold Networks trained with mini-batch SGD and DP-SGD using correlated noise, advancing theoretical understanding of KANs in privacy-sensitive domains.