Poster "statistical learning theory" Papers

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Bridging the Gap Between f-divergences and Bayes Hilbert Spaces

Linus Lach, Alexander Fottner, Yarema Okhrin

Data-Driven Performance Guarantees for Classical and Learned Optimizers

Rajiv Sambharya, Bartolomeo Stellato

NEURIPS 2025arXiv:2404.13831

Learning with Statistical Equality Constraints

Aneesh Barthakur, Luiz Chamon

NEURIPS 2025arXiv:2511.14320

PAC-Bayes Bounds for Multivariate Linear Regression and Linear Autoencoders

Ruixin Guo, Ruoming Jin, Xinyu Li et al.

NEURIPS 2025arXiv:2512.12905

Algorithmic Stability Unleashed: Generalization Bounds with Unbounded Losses

Shaojie Li, Bowei Zhu, Yong Liu

A Statistical Theory of Regularization-Based Continual Learning

Xuyang Zhao, Huiyuan Wang, Weiran Huang et al.

ICML 2024arXiv:2406.06213

On Statistical Learning Theory for Distributional Inputs

Christian Fiedler, Pierre-François Massiani, Friedrich Solowjow et al.

On the Consistency of Kernel Methods with Dependent Observations

Pierre-François Massiani, Sebastian Trimpe, Friedrich Solowjow

ICML 2024arXiv:2406.06101

Smoothness Adaptive Hypothesis Transfer Learning

Haotian Lin, Matthew Reimherr

ICML 2024arXiv:2402.14966

Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms

Ming Yang, Xiyuan Wei, Tianbao Yang et al.

ICML 2024arXiv:2307.03357