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Learning Theory

Learning theory

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Showing 601–650 of 852 papers

TitleStatusHype
On the Generalization Gap in Reparameterizable Reinforcement Learning—0
On the generalization of learning algorithms that do not converge—0
On the Hardness of Bandit Learning—0
On the Hardness of PAC-learning Stabilizer States with Noise—0
On the Hardness of Robust Classification—0
On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systems—0
On the Learnability of Out-of-distribution Detection—0
On the Maximum Mutual Information Capacity of Neural Architectures—0
On the non-efficient PAC learnability of conjunctive queries—0
On the Pseudo-Dimension of Nearly Optimal Auctions—0
On the robustness of randomized classifiers to adversarial examples—0
On the Robustness of Regularized Pairwise Learning Methods Based on Kernels—0
On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing—0
On the Sampling Strategy for Evaluation of Spectral-spatial Methods in Hyperspectral Image Classification—0
On the Complexity of Labeled Datasets—0
Optimality Implies Kernel Sum Classifiers are Statistically Efficient—0
On the use of Statistical Learning Theory for model selection in Structural Health Monitoring—0
On the Variance of the Fisher Information for Deep Learning—0
Optimal choice: new machine learning problem and its solution—0
Õptimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization—0
Optimal lower bounds for Quantum Learning via Information Theory—0
Optimal PAC Bounds Without Uniform Convergence—0
Optimal Quantum Sample Complexity of Learning Algorithms—0
Optimal Rejection Function Meets Character Recognition Tasks—0
Optimal Sample Complexity of Contrastive Learning—0
Optimization for deep learning: theory and algorithms—0
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers—0
Outlier-Robust Linear System Identification Under Heavy-tailed Noise—0
PAC Learning is just Bipartite Matching (Sort of)—0
PAC Synthesis of Machine Learning Programs—0
Parameter Symmetry Potentially Unifies Deep Learning Theory—0
Parametric Prediction from Parametric Agents—0
Partial differential equation regularization for supervised machine learning—0
Passive Learning with Target Risk—0
Path classification by stochastic linear recurrent neural networks—0
Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning—0
Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation—0
Piecewise Strong Convexity of Neural Networks—0
Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metrics—0
Potential-Based Agnostic Boosting—0
Practical machine learning is learning on small samples—0
Preparing for Black Swans: The Antifragility Imperative for Machine Learning—0
Present and Future of AI in Renewable Energy Domain : A Comprehensive Survey—0
Data-driven Mixed Integer Optimization through Probabilistic Multi-variable Branching—0
Primal and Dual Combinatorial Dimensions—0
Private learning implies quantum stability—0
Programs as Singularities—0
Properly Learning Decision Trees with Queries Is NP-Hard—0
Provable Hierarchy-Based Meta-Reinforcement Learning—0
Provably efficient, succinct, and precise explanations—0
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