arXiv Machine Learning
Jun 24
Data Augmentation: A Fourier Analysis Perspective
arXiv:2606. 24418v1 Announce Type: new Abstract: Data augmentation is a simple and model-agnostic approach for exploiting known invariances in learning problems.
Symmetries in PAC-Bayesian Learning
arXiv:2510. 17303v2 Announce Type: replace Abstract: Symmetries are known to improve the empirical performance of machine learning models, yet theoretical guarantees explaining these gains remain limited.
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arXiv AI
Jun 16
Discovering Symmetry Groups with Flow Matching
arXiv:2512. 20043v3 Announce Type: replace Abstract: Symmetry is fundamental to understanding physical systems and can improve performance and sample efficiency in machine learning.
arXiv Machine Learning
Sep 10
Efficient Learning and Symmetry Discovery under Exact Invariances
arXiv:2609.07031v1 Announce Type: new Abstract: Learning with group invariances is central to many scientific and geometric learning problems, yet its computational foundations remain poorly understo...
arXiv AI
Jun 30
Representation Learning for Equivariant Inference with Guarantees
arXiv:2505. 19809v3 Announce Type: replace-cross Abstract: In many real-world applications of regression, conditional probability estimation, and uncertainty quantification, exploiting symmetries rooted in physics or geometry can dramatically improve generalization and sample efficiency.
arXiv Machine Learning
Aug 4
Information-Theoretic Foundations for Machine Learning
arXiv:2407. 12288v5 Announce Type: replace-cross Abstract: The progress of machine learning over the past decade is undeniable.
arXiv Machine Learning
Aug 10
Equivariant Sparse Autoencoders: Mechanistic Interpretability of Neural Networks on Symmetric Data
arXiv:2511. 09432v2 Announce Type: replace Abstract: Machine learning (ML) models achieve remarkable performance but remain hard to interpret due to their scale and complexity.
arXiv Machine Learning
Jun 26
Equivariance and Augmentation for Bayesian Neural Networks
arXiv:2606. 26273v1 Announce Type: new Abstract: Symmetries are important for many deep learning tasks, ranging from applications in the sciences to medical imaging.
arXiv Machine Learning
Sep 21
Sparse Priors for Efficient Distribution Learning
arXiv:2609. 20883v1 Announce Type: new Abstract: Despite the widespread use and success of generative AI techniques today, theoretical guarantees on learning a distribution supported in $d$ dimensions from $n$ samples degrade as $O(n^{-1/\Theta(d)})$, though shown to be minimax optimal.
arXiv Machine Learning
2d ago
Can Domain Generalization be Guaranteed in Small-Sample Learning?
arXiv:2609.39512v1 Announce Type: new Abstract: The small-sample learning problem remains a fundamental challenge in machine learning because limited training data lead to unstable model estimation a...
Hugging Face Trending Papers
Jun 8
Data augmented bootstrap: Unifying confidence interval construction by approximate invariance
We propose the data augmented bootstrap (DAB), a framework for constructing confidence intervals from approximately invariant transformations of the data. As special cases, DAB recovers popular methods that rely on exact group symmetries, such as conformal prediction, wild bootstrap for Maximum Mean Discrepancy U-statistics and the recently proposed SymmPI.
Hugging Face Trending Papers
Sep 21
Adversarially Robust PAC Learning with Optimal VC Rates
We study the problem of \emph{adversarially robust} PAC learning. In this framework, the learner observes independent samples from an unknown distribution over $\mathcal{X} \times \{0,1\}$, as in clas...
arXiv Machine Learning
Sep 14
Quantifying the Value of Privileged Information Using a PAC-Bayesian Approach
The paper introduces a PAC‑Bayesian, algorithm‑agnostic framework to quantify the value of privileged information (PI) in Learning Using Privileged Information (LUPI). By comparing the tightest achievable risk bounds with and without PI, the authors derive a training‑time metric that estimates the maximum potential gain from PI without requiring test data. Experiments in supervised and unsupervised settings show a strong correspondence between this metric and actual test‑time performance improvements.