Tag: empirical
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Universal priors: solving empirical Bayes via Bayesian inference and pretraining
Universal priors: solving empirical Bayes via Bayesian inference and pretraining arXiv:2602.15136v1 Announce Type: new Abstract: We theoretically justify the recent empirical finding of [Teh et al., 2025] that a transformer pretrained on synthetically generated data achieves strong performance on empirical Bayes (EB) problems. We take an indirect approach to this question: rather than analyzing the…
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Empirical Mode Decomposition: The Most Intuitive Way to Decompose Complex Signals and Time Series
Empirical Mode Decomposition: The Most Intuitive Way to Decompose Complex Signals and Time Series A step-by-step breakdown of empirical mode decomposition to help you extract patterns from time series The post Empirical Mode Decomposition: The Most Intuitive Way to Decompose Complex Signals and Time Series appeared first on Towards Data Science. Sabrine Bendimerad Go to…
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Benefits of Online Tilted Empirical Risk Minimization: A Case Study of Outlier Detection and Robust Regression
Benefits of Online Tilted Empirical Risk Minimization: A Case Study of Outlier Detection and Robust Regression arXiv:2509.15141v1 Announce Type: new Abstract: Empirical Risk Minimization (ERM) is a foundational framework for supervised learning but primarily optimizes average-case performance, often neglecting fairness and robustness considerations. Tilted Empirical Risk Minimization (TERM) extends ERM by introducing an exponential tilt…
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Estimation of the Learning Coefficient Using Empirical Loss
Estimation of the Learning Coefficient Using Empirical Loss arXiv:2502.09998v1 Announce Type: new Abstract: The learning coefficient plays a crucial role in analyzing the performance of information criteria, such as the Widely Applicable Information Criterion (WAIC) and the Widely Applicable Bayesian Information Criterion (WBIC), which Sumio Watanabe developed to assess model generalization ability. In regular statistical…
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Local minima of the empirical risk in high dimension: General theorems and convex examples
Local minima of the empirical risk in high dimension: General theorems and convex examples arXiv:2502.01953v1 Announce Type: new Abstract: We consider a general model for high-dimensional empirical risk minimization whereby the data $mathbf{x}_i$ are $d$-dimensional isotropic Gaussian vectors, the model is parametrized by $mathbf{Theta}inmathbb{R}^{dtimes k}$, and the loss depends on the data via the projection…