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Learning Theory

Learning theory

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Showing 551–575 of 852 papers

TitleStatusHype
Risk bounds for reservoir computing—0
Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm—0
Risk Guarantees for End-to-End Prediction and Optimization Processes—0
Robot Learning Theory of Mind through Self-Observation: Exploiting the Intentions-Beliefs Synergy—0
Robust Generalization of Quadratic Neural Networks via Function Identification—0
Robust Kernel-based Distribution Regression—0
Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics—0
Robust NAS under adversarial training: benchmark, theory, and beyond—0
Robust Semi-supervised Learning by Wisely Leveraging Open-set Data—0
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
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