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Learning Theory

Learning theory

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Showing 676–700 of 852 papers

TitleStatusHype
Understanding Machine Learning Paradigms through the Lens of Statistical Thermodynamics: A tutorial—0
Understanding the Behaviour of the Empirical Cross-Entropy Beyond the Training Distribution—0
Understanding the role of importance weighting for deep learning—0
Understanding What Affects the Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence—0
Unified Inductive Logic: From Formal Learning to Statistical Inference to Supervised Learning—0
Unified Regularity Measures for Sample-wise Learning and Generalization—0
Uniform Brackets, Containers, and Combinatorial Macbeath Regions—0
Uniform Generalization, Concentration, and Adaptive Learning—0
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent—0
Uniform Risk Bounds for Learning with Dependent Data Sequences—0
Unique properties of adversarially trained linear classifiers on Gaussian data—0
KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-Zero Training Loss—0
Universality of Deep Convolutional Neural Networks—0
Stretched and measured neural predictions of complex network dynamics—0
Universal Online Learning: an Optimistically Universal Learning Rule—0
Universal Online Learning with Unbounded Losses: Memory Is All You Need—0
Unlabelled Sample Compression Schemes for Intersection-Closed Classes and Extremal Classes—0
Upper Bounds for Learning in Reproducing Kernel Hilbert Spaces for Non IID Samples—0
User-friendly introduction to PAC-Bayes bounds—0
Using Degeneracy in the Loss Landscape for Mechanistic Interpretability—0
Using Empirical Covariance Matrix in Enhancing Prediction Accuracy of Linear Models with Missing Information—0
Variational Adaptive Noise and Dropout towards Stable Recurrent Neural Networks—0
Variational Inference via Resolution of Singularities—0
V-Coder: Adaptive AutoEncoder for Semantic Disclosure in Knowledge Graphs—0
Verification of ML Systems via Reparameterization—0
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