stat.ML · 2026-09-11 · No. 112
Machine Learning, 2026-09-11.
8 new papers in stat.ML. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
8 entries-
01
Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact
Masahiro Kato, Daiki Honma, Taka Kato
stat.ML · cs.AI · cs.LG · econ.EM · stat.ME
Generative artificial intelligence changes how firms reach customers, but standard marketing data do not record how often users see and notice a firm's name in generated answers. We develop Generative Marketing Mix Modeling (GMMM) to estimate the causal effects of Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM). For GEO, GMMM combines repeated generated answers with question counts, shares of use across generative...
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02
Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting
Bowen Zhang, Hsiu-Wen Cheng, Hongyu Yang, Evie L. Shen, Joleen Vansomphone, Yuna Li, Kerry Zhou, Zitian Qu, Suning...
stat.ML · cs.LG
Continuous glucose monitoring (CGM) provides high-frequency measurements of glucose dynamics and enables short-term glucose forecasting for diabetes management. Although time-series foundation models have shown strong general forecasting ability, their effectiveness for CGM prediction and the added value of multimodal dietary context remain unclear. We conduct a comprehensive empirical study using eight public CGM datasets spanning Type 1...
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03
Near-Optimal Reinforcement Learning with Multi-Step Transition Lookahead
Corentin Pla, Hugo Richard, Marc Abeille, Vianney Perchet
stat.ML · cs.LG
We study reinforcement learning (RL) with transition look-ahead, where the agent may observe which states would be visited upon playing any sequence of $\ell$ actions before deciding its course of action. Although look-ahead can substantially improve achievable performance, it is known that optimal planning with multi-step transition look-ahead is NP-hard, but this hardness was established using discount factors arbitrarily close to one. It...
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04
Generalization Analysis of Distributed Kernel-based Robust Gradient Descent Algorithms
Jun-Yi Meng, Zheng-Chu Guo, Yuan Mao
stat.ML · cs.LG · math.OA · math.PR
In this paper, we investigate the generalization performance of distributed gradient descent algorithms in a reproducing kernel Hilbert space under a robust loss function $l_σ$. By exploiting the spectral characterization of gradient descent together with the intrinsic properties of robust loss functions, we establish optimal learning rates for the distributed kernel-based robust gradient descent (DKRGD) algorithm with an appropriately chosen...
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05
Identifiability of Nonnegative Tensor Decompositions via Positive Scattering
Haoming Wang, Ming Yuan
stat.ML · cs.LG · math.CO · math.ST
Identifiability of tensor decompositions is often established through linear-algebraic conditions on the factor families. For nonnegative decompositions, however, positivity provides additional information that is not captured by dimension and independence alone: nonnegative terms cannot cancel, and their supports constrain competing decompositions. We introduce a positive scattering term that quantifies this additional source of...
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06
A distribution-free certification framework for trustworthy crash-severity prediction
Amir Rafe, Subasish Das
stat.ML · cs.LG
Crash-severity models inform screening, dispatch and site prioritization, yet are deployed without a finite-sample statement of what one prediction means. Off-the-shelf guarantees fail here, because the features that make crash severity distinctive defeat them: the KABCO outcome is ordinal, the recorded label is a field assessment agreeing with medical severity about half the time, erring in a structured way, and deployment crosses...
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07
Risk-Averse Decision Making with Multi-Level Reliability Guarantees
Amirmohammad Farzaneh, Osvaldo Simeone
stat.ML · cs.IT · cs.LG
Many applications in engineering, including wireless broadcasting, require designs that provide performance certificates at different target outage levels. This paper studies the problem of maximizing the weighted average of such certificates in the presence of uncertainty about the true system state. The problem is shown to be equivalent to an optimization over nested prediction sets, connecting to the literature on conformal prediction and...
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08
A Hilbert-Valued Functional Decomposition Framework for Explaining Time-Dependent Outputs
Sophie Hanna Langbein, Niklas Koenen, Marvin N. Wright, Julia Herbinger
stat.ML · cs.LG
Feature-based explanations quantify features' influence on model predictions, but are primarily designed for scalar outputs. In many applications, however, outputs are functional or multivariate, such as time-dependent trajectories in demand forecasting. Consequently, existing approaches typically explain each output location independently, ignoring dependencies across the output components. We address this limitation by developing a unified...
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