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Found 6 papers, 0 papers with code
Majority-of-Three: The Simplest Optimal Learner?
Furthermore, we prove a near-optimal high-probability bound on this algorithm's error.
Optimal PAC Bounds Without Uniform Convergence
In this paper, we address this issue by providing optimal high probability risk bounds through a framework that surpasses the limitations of uniform convergence arguments.
The One-Inclusion Graph Algorithm is not Always Optimal
In one of the first COLT open problems, Warmuth conjectured that this prediction strategy always implies an optimal high probability bound on the risk, and hence is also an optimal PAC algorithm.
Privately Learning Mixtures of Axis-Aligned Gaussians
We show that if $\mathcal{F}$ is privately list-decodable, then we can privately learn mixtures of distributions in $\mathcal{F}$.
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians
These are the first finite sample upper bounds for general Gaussians which do not impose restrictions on the parameters of the distribution.
On the Sample Complexity of Learning Sum-Product Networks
We show that the sample complexity of learning tree structured SPNs with the usual type of leaves (i. e., Gaussian or discrete) grows at most linearly (up to logarithmic factors) with the number of parameters of the SPN.
