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Found 7 papers, 0 papers with code
A Trichotomy for Transductive Online Learning
We present new upper and lower bounds on the number of learner mistakes in the `transductive' online learning setting of Ben-David, Kushilevitz and Mansour (1997).
The Bayesian Stability Zoo
We show that many definitions of stability found in the learning theory literature are equivalent to one another.
Fantastic Generalization Measures are Nowhere to be Found
We study the notion of a generalization bound being uniformly tight, meaning that the difference between the bound and the population loss is small for all learning algorithms and all population distributions.
PAC Verification of Statistical Algorithms
Showcasing our proposed definition, our final result is a protocol for the verification of statistical query algorithms that satisfy a combinatorial constraint on their queries.
Fine-Grained Distribution-Dependent Learning Curves
We solve this problem in a principled manner, by introducing a combinatorial dimension called VCL that characterizes the best $d'$ for which $d'/n$ is a strong minimax lower bound.
A Direct Sum Result for the Information Complexity of Learning
We introduce a class of functions of VC dimension $d$ over the domain $\mathcal{X}$ with information complexity at least $\Omega\left(d\log \log \frac{|\mathcal{X}|}{d}\right)$ bits for any consistent and proper algorithm (deterministic or random).
Learners that Use Little Information
We discuss an approach that allows us to prove upper bounds on the amount of information that algorithms reveal about their inputs, and also provide a lower bound by showing a simple concept class for which every (possibly randomized) empirical risk minimizer must reveal a lot of information.
