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Learning Theory

Learning theory

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Showing 701–725 of 852 papers

TitleStatusHype
Sample Complexity of Automated Mechanism Design—0
Sample Complexity of Robust Learning against Evasion Attacks—0
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation—0
Scalable Large-Margin Structured Learning: Theory and Algorithms—0
Scaling Law for Stochastic Gradient Descent in Quadratically Parameterized Linear Regression—0
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation—0
Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena—0
Semi-Gradient SARSA Routing with Theoretical Guarantee on Traffic Stability and Weight Convergence—0
Building Efficient CNNs Using Depthwise Convolutional Eigen-Filters (DeCEF)—0
Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods—0
Shapley Value Based Multi-Agent Reinforcement Learning: Theory, Method and Its Application to Energy Network—0
ShardNet: One Filter Set to Rule Them All—0
Sharper bounds for uniformly stable algorithms—0
Sharp Generalization of Transductive Learning: A Transductive Local Rademacher Complexity Approach—0
Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss—0
Siamese Networks: The Tale of Two Manifolds—0
Simple to Complex Cross-modal Learning to Rank—0
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning—0
Singular leaning coefficients and efficiency in learning theory—0
Smoothed Online Learning is as Easy as Statistical Learning—0
On the Sample Complexity of Imitation Learning for Smoothed Model Predictive Control—0
CMAR-Net: Accurate Cross-Modal 3D SAR Reconstruction of Vehicle Targets with Sparse-Aspect Multi-Baseline Data—0
Robust Model Selection and Nearly-Proper Learning for GMMs—0
Sparsity, variance and curvature in multi-armed bandits—0
Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error—0
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