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Learning Theory

Learning theory

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Showing 551–600 of 852 papers

TitleStatusHype
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate—0
Geometry Perspective Of Estimating Learning Capability Of Neural Networks—0
A Learning Theoretic Perspective on Local Explainability—0
Toward Better Generalization Bounds with Locally Elastic Stability—0
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability—0
Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional DataCode0
Deep Learning is Singular, and That's GoodCode0
Regularised Least-Squares Regression with Infinite-Dimensional Output Space—0
Failures of model-dependent generalization bounds for least-norm interpolation—0
Depth-Width Trade-offs for Neural Networks via Topological Entropy—0
Learning Theory for Inferring Interaction Kernels in Second-Order Interacting Agent Systems—0
A Note on High-Probability versus In-Expectation Guarantees of Generalization Bounds in Machine Learning—0
Improving Few-Shot Learning through Multi-task Representation Learning TheoryCode0
Benign overfitting in ridge regression—0
A Framework of Learning Through Empirical Gain Maximization—0
Do Deeper Convolutional Networks Perform Better?—0
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms—0
Generalized Leverage Score Sampling for Neural Networks—0
A Principle of Least Action for the Training of Neural NetworksCode0
Too Much Information Kills Information: A Clustering Perspective—0
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic ModelsCode0
Gradient-based Competitive Learning: Theory—0
Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity—0
Optimal Approximations Made Easy—0
How Powerful are Shallow Neural Networks with Bandlimited Random Weights?—0
Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network Based Vector-to-Vector Regression—0
Verification of ML Systems via Reparameterization—0
Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets—0
A Revision of Neural Tangent Kernel-based Approaches for Neural Networks—0
Associative Memory in Iterated Overparameterized Sigmoid Autoencoders—0
Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications—0
Ensuring Learning Guarantees on Concept Drift Detection with Statistical Learning Theory—0
Good Classifiers are Abundant in the Interpolating Regime—0
An Optimal Elimination Algorithm for Learning a Best Arm—0
Logic of Machine Learning—0
Estimates on Learning Rates for Multi-Penalty Distribution Regression—0
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation—0
Why Mixup Improves the Model Performance—0
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers—0
On the Maximum Mutual Information Capacity of Neural Architectures—0
Binary Classification with Classical Instances and Quantum Labels—0
Foreseeing the Benefits of Incidental SupervisionCode0
Uncertain multi-agent MILPs: A data-driven decentralized solution with probabilistic feasibility guarantees—0
Unique properties of adversarially trained linear classifiers on Gaussian data—0
Hierarchical robust aggregation of sales forecasts at aggregated levels in e-commerce, based on exponential smoothing and Holt's linear trend method—0
On Learnability under General Stochastic Processes—0
Rethink the Connections among Generalization, Memorization and the Spectral Bias of DNNsCode0
Learning Theory for Estimation of Animal Motion Submanifolds—0
Distributed Kernel Ridge Regression with Communications—0
Design-unbiased statistical learning in survey sampling—0
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