arXiv:2604.02889v1 Announce Type: cross
Abstract: Data assimilation is the process of estimating the time-evolving state of a dynamical system by integrating model predictions and noisy observations. It is commonly formulated as Bayesian filtering, bu…
arXiv:2512.03537v3 Announce Type: replace
Abstract: Continual learning requires models to learn continuously while preserving prior knowledge under evolving data streams. Distillation-based methods are appealing for retaining past knowledge in a share…
arXiv:2503.02178v2 Announce Type: replace-cross
Abstract: This paper develops asymptotic theory for quantile estimation via stochastic gradient descent (SGD) with a constant learning rate. The quantile loss function is neither smooth nor strongly conv…
arXiv:2503.10773v2 Announce Type: replace-cross
Abstract: As data marketplaces become increasingly central to the digital economy, it is crucial to design efficient pricing mechanisms that optimize revenue while ensuring fair and adaptive pricing. We …
arXiv:2505.21723v3 Announce Type: replace-cross
Abstract: In the era of AI, neural networks have become increasingly popular for modeling, inference, and prediction, largely due to their potential for universal approximation. With the proliferation of…
arXiv:2604.02507v1 Announce Type: cross
Abstract: Reinforcement learning from human feedback (RLHF) has emerged as a central framework for aligning large language models (LLMs) with human preferences. Despite its practical success, RLHF raises fundame…
arXiv:2603.02417v2 Announce Type: replace-cross
Abstract: Classical stochastic-approximation analyses treat the covariance of stochastic gradients as an exogenous modeling input. We show that under exchangeable mini-batch sampling this covariance is i…
arXiv:2510.02513v2 Announce Type: replace-cross
Abstract: Adaptive randomized pivoting (ARP) is a recently proposed and highly effective algorithm for column subset selection. This paper reinterprets the ARP algorithm by drawing connections to the vol…
arXiv:2604.02656v1 Announce Type: cross
Abstract: Randomized controlled trials often do not represent the populations where decisions are made, and covariate shift across studies can invalidate standard IPD meta-analysis and transport estimators. We p…
arXiv:2510.15483v2 Announce Type: replace-cross
Abstract: We study the problem of stochastic contextual bandits in the agnostic setting, where the goal is to compete with the best policy in a given class without assuming realizability or imposing mode…