Ridge Regression
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To Grok Grokking: Provable Grokking in Ridge Regression
arXiv:2601.19791v3 Announce Type: replace Abstract: We study grokking, the onset of generalization long after overfitting, in a classical ridge regression setting. We prove end-to-end grokking results for learning over-parameterized linear regression models using gradient descent with weight decay. Specifically, we prove that the following stages occur: (i) the model overfits the training data early during training; (ii) poor generalization persists long after overfitting has manifested; and...
Improved Scaling Laws via Weak-to-Strong Generalization in Random Feature Ridge Regression
Announce Type: replace Abstract: It is increasingly common in machine learning to use learned models to label data and then employ such data to train more capable models. The phenomenon of weak-to-strong generalization exemplifies the advantage of this two-stage procedure: a strong student is trained on imperfect labels obtained from a weak teacher, and yet the strong student outperforms the weak teacher. In this paper, we show that the potential improvement is substantial, in the sense that...
Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models
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Efficient Benchmarking Is Just Feature Selection and Multiple Regression
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Digital Quantum Reservoir Computing for ATM Time Series Prediction
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Loop Current Extension as an Effective Delayed Dynamical System
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FlagGAM: Rule-Based Generalized Additive Modeling for Explainable Tabular Prediction
arXiv:2605.31189v1 Announce Type: new Abstract: Tabular prediction in high-stakes domains requires models that are accurate, transparent, and robust to imperfect inputs. We propose FlagGAM, a rule-defined basis framework that separates feature-level rule construction from prediction. A Flag Core Module converts numerical and categorical variables into sparse, human-readable univariate bases, including threshold flags, category-level flags, tail-deviation bases, and categorical step...
Hybrid Dynamics Modeling for a Flexible 2-DoF Robotic Arm
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GRKV: Global Regression for Training-Free KV Cache Compression in Long-Context LLMs
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