arXiv Machine Learning
Jun 10
Efficiently Learning Drifting Halfspaces with Massart Noise
arXiv:2606. 11149v1 Announce Type: new Abstract: We study the problem of learning a drifting concept in the presence of Massart noise.
Boosting with List-Decodable Codes
arXiv:2607. 05791v1 Announce Type: cross Abstract: Boosting is a fundamental technique for generically improving the accuracy of learning algorithms (Schapire 1989).
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arXiv Machine Learning
5d ago
Defensive Boosting for Online Probabilistic Forecasting
arXiv:2608. 13554v1 Announce Type: new Abstract: We study online probabilistic forecasting of binary outcomes chosen by an adaptive adversary.
arXiv Machine Learning
Jun 25
Space-Efficient Language Generation in the Limit
arXiv:2606. 25777v1 Announce Type: cross Abstract: We initiate a resource-aware theory of \textit{language generation in the limit} under the minimal constraint of space efficiency.
Hugging Face Trending Papers
Jul 13
Requential Coding: Pushing the Limits of Model Compression with Self-Generated Training Data
Compression is fundamental to intelligence. A model that can represent its training data as a short code has discovered regularities that enable generalization.
arXiv Machine Learning
Jul 14
Requential Coding: Pushing the Limits of Model Compression with Self-Generated Training Data
arXiv:2607. 11883v1 Announce Type: new Abstract: Compression is fundamental to intelligence.
arXiv Machine Learning
6d ago
Linear-Core Surrogates: Smooth Loss Functions with Linear Rates for Classification and Structured Prediction
arXiv:2604. 27742v2 Announce Type: replace Abstract: A fundamental dichotomy in the theory of classification sets smoothness against statistical efficiency: smooth surrogate losses such as the logistic loss enable fast $O(1/T)$ optimization but yield slow square-root $H$-consistency bounds, while piecewise-linear losses like the Hinge loss achieve optimal linear $H$-consistency rates but are non-differentiable.
arXiv Machine Learning
Jun 16
Tight Bounds for Logistic Regression with Large Stepsize Gradient Descent in Low Dimension
arXiv:2602. 12471v2 Announce Type: replace Abstract: We consider the optimization problem of minimizing the logistic loss with gradient descent to train a linear model for binary classification with separable data.
arXiv AI
Jun 26
Beyond the Hard Budget: Sparsity Regularizers for More Interpretable Top-k Sparse Autoencoders
arXiv:2606. 27321v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) have become a leading tool for interpreting the representations of vision foundation models, decomposing their polysemantic activations into a larger set of sparse, more monosemantic features.
arXiv Machine Learning
Aug 11
Optimal Learning Under Tsybakov Noise
arXiv:2608. 08416v1 Announce Type: new Abstract: Probably Approximately Correct (PAC) learning [Val84] is a fundamental learning model that has been extensively investigated.
arXiv AI
Aug 12
On Solomonoff Induction in Large Language Models and the Limits of Self-Improving: The Singularity Is Not Near Without Symbolic Model Synthesis
arXiv:2601. 05280v3 Announce Type: replace-cross Abstract: On the one hand, the question of whether large language models (LLMs) are Solomonoff induction estimators has become an explicit question at the intersection of Algorithmic Information Theory (AIT) and Machine Learning (ML) of great interest.
arXiv Machine Learning
Jul 23
On the Computational Complexity of Structural Generalization
arXiv:2607. 19573v1 Announce Type: cross Abstract: Structural generalization has been measured repeatedly by several benchmarks, yet it has never been formally defined.
arXiv Machine Learning
Aug 4
Dense Language Generation Made Simple: Deterministic, Randomized, and Multi-Order Algorithms
arXiv:2608. 01320v1 Announce Type: cross Abstract: Language generation in the limit is a theoretical framework for studying how a generator can learn to produce new valid strings from a stream of positive examples.