The paper argues that linear models can still perform well in complex, changing environments because the world’s complexity often exceeds what can be reliably learned from finite, partially observed data. It explains that flexible, nonlinear models may capture idiosyncratic patterns specific to a particular setting—such as a hospital or policy period—leading to poor generalization when conditions shift. The authors formalize this intuition with a squared‑error proposition and discuss how factors like bias‑variance trade‑offs, regularization, measurement error, and distribution shift influence the choice between linear and nonlinear approaches.
arXiv:2606. 00563v1 Announce Type: cross Abstract: Selection bias is a common and often unavoidable aspect of real-world data that challenges the generalizability of machine learning models.
arXiv:2607. 23454v1 Announce Type: new Abstract: Data-driven remaining useful life (RUL) prediction requires complete degradation trajectories for training, yet such run-to-failure data are scarce and expensive.
arXiv:2609.23937v1 Announce Type: cross Abstract: Robust linear fits can resist response contamination yet remain too dense or unstable for useful global explanations. We propose penalized distillati...
arXiv:2605. 24212v2 Announce Type: replace-cross Abstract: Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target domain.
arXiv:2607. 21773v1 Announce Type: new Abstract: In this paper, we propose and study a robust variant of the smart predict-then-optimize approach that accounts for prediction shifts due to disturbance in the covariate feature space.
arXiv:2605. 26704v2 Announce Type: replace-cross Abstract: Epidemic forecasting faces a fundamental challenge: human behavior dynamically responds to disease spread, creating feedback loops that induce distribution shifts at policy intervention points.
arXiv:2606. 22775v2 Announce Type: replace-cross Abstract: Distribution shift between training and deployment is a pervasive challenge for modern AI systems.
arXiv:2309. 15769v3 Announce Type: replace-cross Abstract: Recent advances in deep learning have highlighted the phenomenon of benign overfitting in overparameterized statistical models, sparking significant interest in understanding its foundations.
arXiv:2608. 02412v1 Announce Type: new Abstract: Large language models (LLMs) have become the default tool for a remarkable range of tasks, yet they have had conspicuously little success at one of the most common machine learning workloads: predictive analytics over tabular data.
Direct Inference on Admissible Sets (Diona) is a regression method that estimates a state and its derivatives at a query point directly from samples, providing admissible bounds without extensive global training. It formulates the estimation as a constrained optimisation problem over latent variables, enabling on‑demand local solves. In benchmarks, Diona outperformed Gaussian processes on noiseless data, offered uncertainty bounds covering the true value in most cases, and achieved first predictions up to four orders of magnitude faster than Gaussian processes and two to three orders faster than neural networks.
arXiv:2411.02771v3 Announce Type: replace-cross Abstract: Doubly robust estimators are widely used for estimating average treatment effects and other linear summaries of regression functions. While c...
arXiv:2609.38540v1 Announce Type: new Abstract: A valid curvature upper bound need not justify either a robustness certificate or an intervention on an intrinsic predictor property. We demonstrate th...