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Learning Theory

Learning theory

Papers

Showing 1–25 of 852 papers

TitleStatusHype
On the Hardness of Bandit Learning—0
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach—0
The Universality Lens: Why Even Highly Over-Parametrized Models Learn Well—0
Learning Theory of Decentralized Robust Kernel-Based Learning Algorithm—0
What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness—0
A Machine Learning Theory Perspective on Strategic Litigation—0
Variational Adaptive Noise and Dropout towards Stable Recurrent Neural Networks—0
Learning DNF through Generalized Fourier Representations—0
Distribution free M-estimation—0
Attribute-Efficient PAC Learning of Sparse Halfspaces with Constant Malicious Noise Rate—0
A Semantic Change Detection Network Based on Boundary Detection and Task Interaction for High-Resolution Remote Sensing ImagesCode1
Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical SystemsCode0
Data-Driven Breakthroughs and Future Directions in AI Infrastructure: A Comprehensive Review—0
An Asymptotic Equation Linking WAIC and WBIC in Singular Models—0
Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping—0
Learning Guarantee of Reward Modeling Using Deep Neural Networks—0
New Statistical and Computational Results for Learning Junta Distributions—0
Lower Bounds for Greedy Teaching Set Constructions—0
Towards Robust and Generalizable Gerchberg Saxton based Physics Inspired Neural Networks for Computer Generated Holography: A Sensitivity Analysis Framework—0
Data Selection for ERMs—0
Erzeugunsgrad, VC-Dimension and Neural Networks with rational activation function—0
auto-fpt: Automating Free Probability Theory Calculations for Machine Learning TheoryCode0
Generalization Bounds in Hybrid Quantum-Classical Machine Learning Models—0
Programs as Singularities—0
A Framework of decision-relevant observability: Reinforcement Learning converges under relative ignorability—0
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