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: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: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:2510.00643v1 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: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:2510.00815v1 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:2506.04118v2 Announce Type: replace-cross Abstract: We propose Guided Speculative Inference (GSI), a novel algorithm for efficient reward-guided decoding in large language models. GSI combines soft best-of-$n$ test-time scaling with a reward model $r(x,y)$ and speculative samples from a small auxiliary…
arXiv:2507.02442v2 Announce Type: replace-cross Abstract: Enhancing the intelligibility and interpretability of machine learning is a crucial task in responding to the demand for Explicability as an AI principle, and in promoting the better social implementation of AI. The aim of…
arXiv:2510.00875v1 Announce Type: cross Abstract: Simultaneously performing variable selection and inference in high-dimensional models is an open challenge in statistics and machine learning. The increasing availability of vast amounts of variables requires the adoption of specific statistical procedures to accurately…
arXiv:2508.15051v2 Announce Type: replace-cross Abstract: We study the problem of robust estimation under heterogeneous corruption rates, where each sample may be independently corrupted with a known but non-identical probability. This setting arises naturally in distributed and federated learning, crowdsourcing, and…
