arXiv Machine Learning By Joseph K. Chege, Arie Yeredor, Martin Haardt

Joint Bayesian Parameter and Model Order Estimation for Low-Rank Probability Mass Tensors

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arXiv:2410. 06329v4 Announce Type: replace-cross Abstract: Obtaining a reliable estimate of the joint probability mass function (PMF) of a set of random variables from observed data is a significant objective in statistical signal processing and machine learning.

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arXiv Machine Learning
Jun 4

Low-rank Distributional Matrix Completion

arXiv:2606. 04176v1 Announce Type: new Abstract: We study a distributional generalization of the matrix completion problem in which each entry of the target matrix is a probability distribution rather than a scalar.

By Jiayi Wang, Raymond K. W. Wong
arXiv AI
Sep 4

LLM Evaluation as Tensor Completion: Low Rank Structure and Semiparametric Efficiency

The paper treats large language model (LLM) evaluation as a tensor completion problem, modeling noisy, sparse, and non‑uniform pairwise human judgments through a low‑rank latent score tensor under Bradley‑Terry‑Luce‑type models. It derives the efficient influence function and semiparametric efficiency bound for smooth functionals of the true tensor, and proposes a one‑step debiased estimator with asymptotic normality. A key innovation is a score‑whitening technique that equalizes local Fisher information, overcoming anisotropy in the information operator and enabling stable inference at optimal sample‑complexity.

By Jiachun Li, David Simchi-Levi, Will Wei Sun
arXiv Machine Learning
Aug 6

E$^2$M: Double Bounded $\alpha$-Divergence Optimization for Tensor-based Discrete Density Estimation

arXiv:2405. 18220v4 Announce Type: replace-cross Abstract: Tensor-based discrete density estimation requires flexible modeling and proper divergence criteria to enable effective learning; however, traditional approaches using $\alpha$-divergence face analytical challenges due to the $\alpha$-power terms in the objective function, which hinder the derivation of closed-form update rules.

By Kazu Ghalamkari, Jesper L{\o}ve Hinrich, Morten M{\o}rup