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Statistical Learning

4,117 papers

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Papers

Least Squares Regression Can Exhibit Under-Parameterized Double Descent NIPS 2024
Capturing the denoising effect of PCA via compression ratio NIPS 2024
Non-parametric classification via expand-and-sparsify representation NIPS 2024
Combining Statistical Depth and Fermat Distance for Uncertainty Quantification NIPS 2024
Implicit Regularization Paths of Weighted Neural Representations NIPS 2024
Estimating the Hallucination Rate of Generative AI NIPS 2024
High-dimensional (Group) Adversarial Training in Linear Regression NIPS 2024
Predicting the Performance of Foundation Models via Agreement-on-the-Line NIPS 2024
Replicable Uniformity Testing NIPS 2024
Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence NIPS 2024
Distributional regression: CRPS-error bounds for model fitting, model selection and convex aggregation NIPS 2024
Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems NIPS 2024
Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms NIPS 2024
Mission Impossible: A Statistical Perspective on Jailbreaking LLMs NIPS 2024
A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models NIPS 2024
Bias Amplification in Language Model Evolution: An Iterated Learning Perspective NIPS 2024
Credal Learning Theory NIPS 2024
Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models NIPS 2024
On Differentially Private Subspace Estimation in a Distribution-Free Setting NIPS 2024
Revealing Distribution Discrepancy by Sampling Transfer in Unlabeled Data NIPS 2024
Quasi-Bayes meets Vines NIPS 2024
On the Target-kernel Alignment: a Unified Analysis with Kernel Complexity NIPS 2024
Debiasing Synthetic Data Generated by Deep Generative Models NIPS 2024
A Universal Growth Rate for Learning with Smooth Surrogate Losses NIPS 2024
Improved Sample Complexity for Multiclass PAC Learning NIPS 2024