stat.ML · 2026-07-03 · No. 42

Machine Learning, 2026-07-03.

6 new papers in stat.ML. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

6 entries
  1. 01

    The Dual Nature of LLM Persona: Aggregated Tendencies and Frame-Dependent Geometry

    Yuan Yuan

    stat.ML · cs.AI · cs.LG · math.DG

    Evaluations of LLM personas via psychometric questionnaires typically rely on aggregate scores, discarding within-instance correlation structure. We test whether this geometric structure is intrinsic or frame-dependent. Constructing within-instance correlation matrices from IPIP-50 responses, we analyze geometry on SPD manifolds under manipulated question orderings in GPT-4o simulating American and Chinese-American personas. We find that...

    arxiv.org/abs/2607.02368 · PDF

  2. 02

    An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility

    Sampreeti Bhattacharya, Arkaprava Roy

    stat.ML · cs.LG

    Aqueous solubility is a key property in early-stage drug discovery, but most predictive models merge physicochemical descriptors and molecular graph information into a single representation, obscuring whether a prediction is driven by global chemistry, molecular structure, or both. We present an additive deep-learning framework that keeps these two sources of information separate throughout training: physicochemical descriptors are encoded by...

    arxiv.org/abs/2607.02212 · PDF

  3. 03

    Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction

    Yurui Zheng, Ying Jin

    stat.ML · cs.LG · math.ST

    Predictions are increasingly used to guide high-stakes decisions, from treatment selection to policy making. To ensure reliability with imperfect predictions, uncertainty quantification methods such as conformal prediction build prediction sets with coverage guarantees. However, statistical validity alone does not immediately determine the decisions to take, nor the optimality thereof. This gap is especially delicate in counterfactual...

    arxiv.org/abs/2607.02206 · PDF

  4. 04

    Born Discrete, Made Smooth: Variational Formulation of Shallow Neural Networks

    Matej Benko, Pierre Bousquet, Iwona Chlebicka, Błażej Miasojedow

    stat.ML · cs.LG

    Although neural networks are remarkably effective, their underlying optimization principles remain theoretically elusive, often characterized by non-convex landscapes and stochastic heuristics. In this work, we propose a paradigm shift by replacing the discrete training problem of shallow neural networks with a well-posed continuum variational surrogate. We identify a family of $λ$-convex functionals over parameter densities in weighted...

    arxiv.org/abs/2607.02003 · PDF

  5. 05

    Autorelevance function and other feature relevance measures for univariate time series

    Julian Cardenas, Jamie Arjona, Pedro Delicado

    stat.ML · cs.LG · stat.ME

    We propose a model agnostic methodology to measure lag relevance in machine learning forecasting models applied to univariate time series. Particularly, we are working in the context of time series using the frameworks of Ghost variables and Shapley values, together with additive importance measures, to introduce the auto-relevance and partial auto-relevance functions as the lag importance values. Additionally, we propose a novel method to...

    arxiv.org/abs/2607.01959 · PDF

  6. 06

    Statistical Properties of $k$-means Clustering for Data Missing Completely at Random

    Xin Guan

    stat.ML · cs.LG

    The classical $k$-means clustering cannot be directly used to incomplete data, and existing $k$-means-based clustering for missing data primarily focus on improving the practical accuracy of clustering, whereas most of them lack theoretical guarantees in the asymptotic sense. In this paper, we investigate the statistical properties of $k$-means clustering in the presence of missing data. We first establish the $\sqrt{n}$-excess risk bound and...

    arxiv.org/abs/2607.01945 · PDF

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