arXiv:2510.00815v1 Announce Type: cross Abstract: Classifier-free guidance (CFG) is a widely used technique for improving the perceptual quality of samples from conditional diffusion models. It operates by linearly combining conditional and unconditional score estimates using a guidance weight $omega$. While…
arXiv:2506.00286v2 Announce Type: replace-cross Abstract: In this paper, we analyze the sample complexities of learning the optimal state-action value function $Q^*$ and an optimal policy $pi^*$ in a finite discounted Markov decision process (MDP) where the agent has recursive entropic…
arXiv:2510.00643v1 Announce Type: cross Abstract: Recent optimizers like Muon, Scion, and Gluon have pushed the frontier of large-scale deep learning by exploiting layer-wise linear minimization oracles (LMOs) over non-Euclidean norm balls, capturing neural network structure in ways traditional algorithms cannot.…
arXiv:2505.14999v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) struggle with reliable mathematical reasoning, and current verification methods are often computationally expensive. This paper introduces the Energy Outcome Reward Model (EORM), a highly efficient, lightweight post-hoc verifier designed to address…
arXiv:2510.00557v1 Announce Type: cross Abstract: In many machine learning problems, understanding variable importance is a central concern. Two common approaches are Permute-and-Predict (PaP), which randomly permutes a feature in a validation set, and Leave-One-Covariate-Out (LOCO), which retrains models after permuting…
arXiv:2510.00479v1 Announce Type: cross Abstract: We numerically investigate the joint observability of flow states and unknown particle properties from Lagrangian particle tracking (LPT) data. LPT offers time-resolved, volumetric measurements of particle trajectories, but experimental tracks are spatially sparse, potentially noisy,…
arXiv:2410.04996v4 Announce Type: replace-cross Abstract: Data integration methods aim to extract low-dimensional embeddings from high-dimensional outcomes to remove unwanted variations, such as batch effects and unmeasured covariates, across heterogeneous datasets. However, multiple hypothesis testing after integration can be biased due…
arXiv:2510.00460v1 Announce Type: cross Abstract: Anomaly detection in spatiotemporal data is a challenging problem encountered in a variety of applications, including video surveillance, medical imaging data, and urban traffic monitoring. Existing anomaly detection methods focus mainly on point anomalies and…
arXiv:2307.11249v2 Announce Type: replace-cross Abstract: This article studies the Fisher-Rao gradient, also referred to as the natural gradient, of the evidence lower bound (ELBO) which plays a central role in generative machine learning. It reveals that the gap between the…
arXiv:2510.00417v1 Announce Type: cross Abstract: An algorithm is proposed, analyzed, and tested for solving continuous nonlinear-equality-constrained optimization problems where the constraints are defined by an expectation or an average over a large (finite) number of terms. The main idea of…
