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Found 9 papers, 1 papers with code
Generalization and Informativeness of Conformal Prediction
A popular technique to achieve this goal is conformal prediction (CP), which transforms an arbitrary base predictor into a set predictor with coverage guarantees.
Comparing Comparators in Generalization Bounds
We derive generic information-theoretic and PAC-Bayesian generalization bounds involving an arbitrary convex comparator function, which measures the discrepancy between the training and population loss.
Generalization Bounds: Perspectives from Information Theory and PAC-Bayes
Over the past decades, the PAC-Bayesian approach has been established as a flexible framework to address the generalization capabilities of machine learning algorithms, and design new ones.
A New Family of Generalization Bounds Using Samplewise Evaluated CMI
Furthermore, using the evaluated CMI, we derive a samplewise, average version of Seeger's PAC-Bayesian bound, where the convex function is the binary KL divergence.
Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness
Recent work has established that the conditional mutual information (CMI) framework of Steinke and Zakynthinou (2020) is expressive enough to capture generalization guarantees in terms of algorithmic stability, VC dimension, and related complexity measures for conventional learning (Harutyunyan et al., 2021, Haghifam et al., 2021).
Fast-Rate Loss Bounds via Conditional Information Measures with Applications to Neural Networks
If the conditional information density is bounded uniformly in the size $n$ of the training set, our bounds decay as $1/n$.
Nonvacuous Loss Bounds with Fast Rates for Neural Networks via Conditional Information Measures
If the conditional information density is bounded uniformly in the size $n$ of the training set, our bounds decay as $1/n$, which is referred to as a fast rate.
Generalization Bounds via Information Density and Conditional Information Density
We present a general approach, based on exponential inequalities, to derive bounds on the generalization error of randomized learning algorithms.
Generalization Error Bounds via $m$th Central Moments of the Information Density
Our approach can be used to obtain bounds on the average generalization error as well as bounds on its tail probabilities, both for the case in which a new hypothesis is randomly generated every time the algorithm is used - as often assumed in the probably approximately correct (PAC)-Bayesian literature - and in the single-draw case, where the hypothesis is extracted only once.
