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Learning Theory

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Showing 651–700 of 852 papers

TitleStatusHype
Topological Generalization Bounds for Discrete-Time Stochastic Optimization Algorithms—0
Toward Better Generalization Bounds with Locally Elastic Stability—0
Towards a Formal Creativity Theory: Preliminary results in Novelty and Transformativeness—0
Towards a Learning Theory of Representation Alignment—0
Towards an Understanding of Benign Overfitting in Neural Networks—0
Towards a theory of out-of-distribution learning—0
Towards Explaining Adversarial Examples Phenomenon in Artificial Neural Networks—0
Towards Low-Barrier Cybersecurity Research and Education for Industrial Control Systems—0
Towards Optimal Problem Dependent Generalization Error Bounds in Statistical Learning Theory—0
Towards Robust and Generalizable Gerchberg Saxton based Physics Inspired Neural Networks for Computer Generated Holography: A Sensitivity Analysis Framework—0
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning—0
Towards Unsupervised Domain Adaptation via Domain-Transformer—0
Logic of Machine Learning—0
Transformers are Minimax Optimal Nonparametric In-Context Learners—0
Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness—0
Triadic Temporal Exponential Random Graph Models (TTERGM)—0
Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient—0
LSTSVR-PI: Least square twin support vector regression with privileged information—0
Two-stage Sampled Learning Theory on Distributions—0
Uncertain multi-agent MILPs: A data-driven decentralized solution with probabilistic feasibility guarantees—0
Uncertainty Estimation in Machine Learning—0
Under-bagging Nearest Neighbors for Imbalanced Classification—0
Understanding Benign Overfitting in Gradient-Based Meta Learning—0
Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition—0
Understanding Few-Shot Multi-Task Representation Learning Theory—0
Understanding Machine Learning Paradigms through the Lens of Statistical Thermodynamics: A tutorial—0
Understanding the Behaviour of the Empirical Cross-Entropy Beyond the Training Distribution—0
Understanding the role of importance weighting for deep learning—0
Understanding What Affects the Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence—0
Unified Inductive Logic: From Formal Learning to Statistical Inference to Supervised Learning—0
Unified Regularity Measures for Sample-wise Learning and Generalization—0
Uniform Brackets, Containers, and Combinatorial Macbeath Regions—0
Uniform Generalization, Concentration, and Adaptive Learning—0
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent—0
Uniform Risk Bounds for Learning with Dependent Data Sequences—0
Unique properties of adversarially trained linear classifiers on Gaussian data—0
KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-Zero Training Loss—0
Universality of Deep Convolutional Neural Networks—0
Stretched and measured neural predictions of complex network dynamics—0
Universal Online Learning: an Optimistically Universal Learning Rule—0
Universal Online Learning with Unbounded Losses: Memory Is All You Need—0
Unlabelled Sample Compression Schemes for Intersection-Closed Classes and Extremal Classes—0
Upper Bounds for Learning in Reproducing Kernel Hilbert Spaces for Non IID Samples—0
User-friendly introduction to PAC-Bayes bounds—0
Using Degeneracy in the Loss Landscape for Mechanistic Interpretability—0
Using Empirical Covariance Matrix in Enhancing Prediction Accuracy of Linear Models with Missing Information—0
Variational Adaptive Noise and Dropout towards Stable Recurrent Neural Networks—0
Variational Inference via Resolution of Singularities—0
V-Coder: Adaptive AutoEncoder for Semantic Disclosure in Knowledge Graphs—0
Verification of ML Systems via Reparameterization—0
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