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Found 14 papers, 0 papers with code
Robust Semi-supervised Learning via $f$-Divergence and $α$-Rényi Divergence
This paper investigates a range of empirical risk functions and regularization methods suitable for self-training methods in semi-supervised learning.
Generalization Error of Graph Neural Networks in the Mean-field Regime
This work provides a theoretical framework for assessing the generalization error of graph classification tasks via graph neural networks in the over-parameterized regime, where the number of parameters surpasses the quantity of data points.
Mean-field Analysis of Generalization Errors
We propose a novel framework for exploring weak and $L_2$ generalization errors of algorithms through the lens of differential calculus on the space of probability measures.
On the Generalization Error of Meta Learning for the Gibbs Algorithm
We analyze the generalization ability of joint-training meta learning algorithms via the Gibbs algorithm.
How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?
Our findings offer new insights that the generalization performance of SSL with pseudo-labeling is affected not only by the information between the output hypothesis and input training data but also by the information {\em shared} between the {\em labeled} and {\em pseudo-labeled} data samples.
Learning Algorithm Generalization Error Bounds via Auxiliary Distributions
Additionally, we demonstrate how our auxiliary distribution method can be used to derive the upper bounds on excess risk of some learning algorithms in the supervised learning context {\blue and the generalization error under the distribution mismatch scenario in supervised learning algorithms, where the distribution mismatch is modeled as $\alpha$-Jensen-Shannon or $\alpha$-R\'enyi divergence between the distribution of test and training data samples distributions.}
Semi-supervised Batch Learning From Logged Data
We propose learning methods for problems where feedback is missing for some samples, so there are samples with feedback and samples missing-feedback in the logged data.
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift
A common assumption in semi-supervised learning is that the labeled, unlabeled, and test data are drawn from the same distribution.
Tighter Expected Generalization Error Bounds via Convexity of Information Measures
Due to the convexity of the information measures, the proposed bounds in terms of Wasserstein distance and total variation distance are shown to be tighter than their counterparts based on individual samples in the literature.
An Exact Characterization of the Generalization Error for the Gibbs Algorithm
Various approaches have been developed to upper bound the generalization error of a supervised learning algorithm.
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm
We provide an information-theoretic analysis of the generalization ability of Gibbs-based transfer learning algorithms by focusing on two popular transfer learning approaches, $\alpha$-weighted-ERM and two-stage-ERM.
Characterizing the Generalization Error of Gibbs Algorithm with Symmetrized KL information
As a result, they may fail to characterize the exact generalization ability of a learning algorithm.
Information-Theoretic Bounds on the Moments of the Generalization Error of Learning Algorithms
Generalization error bounds are critical to understanding the performance of machine learning models.
Jensen-Shannon Information Based Characterization of the Generalization Error of Learning Algorithms
Generalization error bounds are critical to understanding the performance of machine learning models.
