Generalization Bounds
131 papers with code • 0 benchmarks • 0 datasets
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Class-wise Generalization Error: an Information-Theoretic Analysis
Existing generalization theories of supervised learning typically take a holistic approach and provide bounds for the expected generalization over the whole data distribution, which implicitly assumes that the model generalizes similarly for all the classes.
PAC-Bayesian Domain Adaptation Bounds for Multi-view learning
This paper presents a series of new results for domain adaptation in the multi-view learning setting.
Non-Vacuous Generalization Bounds for Large Language Models
Modern language models can contain billions of parameters, raising the question of whether they can generalize beyond the training data or simply regurgitate their training corpora.
Statistical Spatially Inhomogeneous Diffusion Inference
Inferring a diffusion equation from discretely-observed measurements is a statistical challenge of significant importance in a variety of fields, from single-molecule tracking in biophysical systems to modeling financial instruments.
PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction
To remedy this, recent work has proposed learning model and score function parameters using data to directly optimize the efficiency of the ICP prediction sets.
From Mutual Information to Expected Dynamics: New Generalization Bounds for Heavy-Tailed SGD
This has been successfully applied to generalization theory by exploiting the fractal properties of those dynamics.
A unified framework for learning with nonlinear model classes from arbitrary linear samples
In summary, our work not only introduces a unified way to study learning unknown objects from general types of data, but also establishes a series of general theoretical guarantees which consolidate and improve various known results.
Aggregation Weighting of Federated Learning via Generalization Bound Estimation
In this paper, we replace the aforementioned weighting method with a new strategy that considers the generalization bounds of each local model.
Information-theoretic generalization bounds for learning from quantum data
Learning tasks play an increasingly prominent role in quantum information and computation.
Information-Theoretic Generalization Bounds for Transductive Learning and its Applications
In this paper, we develop data-dependent and algorithm-dependent generalization bounds for transductive learning algorithms in the context of information theory for the first time.
