learning theory

Bayes-optimal learning of an extensive-width neural network from quadratically many samples NeurIPS 2024 Can Transformers Learn n-gram Language Models? EMNLP 2024 Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework NeurIPS 2024 Credal Learning Theory NeurIPS 2024 Debiasing Evaluations That Are Biased by Evaluations JMLR 2024 Deep linear networks for regression are implicitly regularized towards flat minima NeurIPS 2024 Depth Separation in Norm-Bounded Infinite-Width Neural Networks COLT 2024 Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking ICLR 2024 Dimension-free deterministic equivalents and scaling laws for random feature regression NeurIPS 2024 Early Neuron Alignment in Two-layer ReLU Networks with Small Initialization ICLR 2024 Efficient Discrepancy Testing for Learning with Distribution Shift NeurIPS 2024 Exploring the Learning Capabilities of Language Models using LEVERWORLDS EMNLP 2024 Fat-Shattering Dimension of k-fold Aggregations JMLR 2024 Generalization Analysis of Machine Learning Algorithms via the Worst-Case Data-Generating Probability Measure AAAI 2024 Generalization Bounds via Conditional f-Information NeurIPS 2024 Generalization of Hamiltonian algorithms NeurIPS 2024 Hardness of Learning Neural Networks under the Manifold Hypothesis NeurIPS 2024 How many classifiers do we need? NeurIPS 2024 How Sparse Can We Prune A Deep Network: A Fundamental Limit Perspective NeurIPS 2024 Improved Hardness Results for Learning Intersections of Halfspaces COLT 2024 Improved Sample Complexity Bounds for Diffusion Model Training NeurIPS 2024 Improved Sample Complexity for Multiclass PAC Learning NeurIPS 2024 Improving Domain Generalization with Domain Relations ICLR 2024 Inherent limitations of dimensions for characterizing learnability of distribution classes COLT 2024 Input Margins Can Predict Generalization Too AAAI 2024