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Learning Theory

Learning theory

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Showing 251–300 of 852 papers

TitleStatusHype
Estimates on Learning Rates for Multi-Penalty Distribution Regression—0
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting—0
Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness—0
Evidence on the Regularisation Properties of Maximum-Entropy Reinforcement Learning—0
Anomaly detecting and ranking of the cloud computing platform by multi-view learning—0
A General Characterization of the Statistical Query Complexity—0
Generalization Error Bounds for Noisy, Iterative Algorithms—0
Binary Iterative Hard Thresholding Converges with Optimal Number of Measurements for 1-Bit Compressed Sensing—0
Binary Excess Risk for Smooth Convex Surrogates—0
An Introduction to Kernel and Operator Learning Methods for Homogenization by Self-consistent Clustering Analysis—0
Failures of model-dependent generalization bounds for least-norm interpolation—0
FADO: A Deterministic Detection/Learning Algorithm—0
Binary Classification with Classical Instances and Quantum Labels—0
Exponential separations between classical and quantum learners—0
Exponential inequalities for nonstationary Markov Chains—0
An introduction to computational complexity and statistical learning theory applied to nuclear models—0
Fairness, Accuracy, and Unreliable Data—0
Fast Approximation of Frequent k-mers and Applications to Metagenomics—0
Fast classification rates without standard margin assumptions—0
Fast Collaborative Filtering from Implicit Feedback with Provable Guarantees—0
Fast Hyperparameter Tuning using Bayesian Optimization with Directional Derivatives—0
Statistical Limits of Supervised Quantum Learning—0
Fast rates in statistical and online learning—0
f-Domain-Adversarial Learning: Theory and Algorithms for Unsupervised Domain Adaptation with Neural Networks—0
A Framework of Learning Through Empirical Gain Maximization—0
Graph-based Discriminators: Sample Complexity and Expressiveness—0
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach—0
Finding the True Frequent Itemsets—0
Fine-Grained Distribution-Dependent Learning Curves—0
Finite-Sample Analysis of Policy Evaluation for Robust Average Reward Reinforcement Learning—0
Which distribution were you sampled from? Towards a more tangible conception of data—0
Bridging the Gap Between Approximation and Learning via Optimal Approximation by ReLU MLPs of Maximal Regularity—0
Formal models of memory based on temporally-varying representations—0
Foundations and Frontiers of Graph Learning Theory—0
Exponential Hardness of Reinforcement Learning with Linear Function Approximation—0
Free resolutions of function classes via order complexes—0
From average case complexity to improper learning complexity—0
Function Norms and Regularization in Deep Networks—0
Artificial Intelligence and Spontaneous Collusion—0
GBHT: Gradient Boosting Histogram Transform for Density Estimation—0
Probability Distribution Learning and Its Application in Deep Learning—0
Generalisation in fully-connected neural networks for time series forecasting—0
Generalising realisability in statistical learning theory under epistemic uncertainty—0
Generalization and Expressivity for Deep Nets—0
Generalization Bounds for Gradient Methods via Discrete and Continuous Prior—0
Generalization bounds for learning under graph-dependence: A survey—0
Generalization Bounds in Hybrid Quantum-Classical Machine Learning Models—0
Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints—0
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR—0
Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles—0
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