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699 items on 07 Oct 2026
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How Wrong Is Your Marketing Mix Model (MMM)?

A well-built MMM can still be wrong, because the spend it learns from never held the information it needs. This article tests whether changing when you spend, not how much, can fix that. The post How Wrong Is Your…

Source: Towards Data Science Ryan OSullivan
AI Research AI Python

The Consistency Quadrant: A Visual Guide to LLM Reliability

Mapping structural variance against execution outputs to estimate coding agent reliability without ground truth. The post The Consistency Quadrant: A Visual Guide to LLM Reliability appeared first on Towards Data…

Source: Towards Data Science Robert Martin-Short

Mistral’s New AI Model Puts a Price on Sovereignty

Mistral Large 4 is competitive with some of the world’s strongest AI models, but its bigger bet is giving enterprises more control over how AI runs. Whether that independence proves practical depends on something…

Source: The Neuron AI Eric Gerard Ruiz, CPA
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Bayesian Optimization on Function Spaces via Sparse RKHS Manifolds

arXiv:2610.07417v1 Announce Type: cross Abstract: Bayesian Optimization (BO) has become an established methodology for minimizing black-box functions of a vector input. Often, however, this parameter vector arises from…

Source: arXiv cs.LG Davide Sartor, Meghan E. Huber, Donghyun Kim, Nathan Wycoff
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Direct Regret Optimization in Bayesian Optimization

arXiv:2507.06529v2 Announce Type: replace Abstract: Bayesian optimization (BO) is a powerful paradigm for optimizing expensive black-box functions. Traditional BO methods typically rely on separate hand-crafted…

Source: arXiv cs.LG Fengxue Zhang, Yuxin Chen
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Large Language Models for Cryptocurrency Transaction Analysis: A Bitcoin Case Study

arXiv:2501.18158v4 Announce Type: replace-cross Abstract: Cryptocurrencies are widely used, yet current methods for analyzing transactions often rely on opaque, black-box models. While these models may achieve high…

Source: arXiv cs.LG Yuchen Lei, Yuexin Xiang, Rafael Dowsley, Tsz Hon Yuen, Andreas Deppeler, Jiangshan Yu, Qin Wang, Kim-Kwang Raymond Choo
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Reward as Observation: Learning Reward-Based Policies for Rapid Adaptation

arXiv:2610.00729v2 Announce Type: replace Abstract: This paper explores a reward-based policy to achieve zero-shot transfer between source and target environments with completely different observation spaces. While…

Source: arXiv cs.LG Morgan Byrd, Jacob Blevins, Maks Sorokin, Robert Wright, Sehoon Ha
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SquidAgent: Parallelize Wisely, Coordinate Efficiently

arXiv:2610.08647v1 Announce Type: cross Abstract: LLM-based agents solve complex multi-step tasks, but sequential execution incurs substantial latency. In principle, parallelizing work across multiple agents should…

Source: arXiv cs.LG Yexiong Lin, Shanshan Ye, Yu Yao, Zhen Fang, Bo Han, Tongliang Liu
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DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment

arXiv:2510.24574v3 Announce Type: replace Abstract: Training time-series forecasting models requires aligning the conditional distribution of model forecasts with that of the label sequence. The standard direct forecast…

Source: arXiv cs.LG Hao Wang, Licheng Pan, Yuan Lu, Zhixuan Chu, Xiaoxi Li, Shuting He, Zhichao Chen, Haoxuan Li, Qingsong Wen, Zhouchen Lin
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Prediction-powered inference for time series across space

arXiv:2610.08715v1 Announce Type: cross Abstract: The following motif is common in spatiotemporal settings: we have a sequence of covariate and label pairs observed for a relatively short, recent time period. We have…

Source: arXiv cs.LG Shahzar Rizvi, David Burt, Vishwak Srinivasan, Renato Berlinghieri, Stefano Del Col, Tamara Broderick
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FFR: Forward-Forward Learning for Regression

arXiv:2606.03927v2 Announce Type: replace Abstract: The Forward-Forward (FF) algorithm offers a computationally efficient and biologically plausible alternative to backpropagation (BP) by training neural networks…

Source: arXiv cs.LG Xinyang Liu, Xuanyu Liang, Shiqi Ding, Boyang Li, Zhiqiang Que, Jiayang Li, Guosheng Hu

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