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Found 14 papers, 4 papers with code
Minimax risk classifiers with 0-1 loss
Supervised classification techniques use training samples to learn a classification rule with small expected 0-1 loss (error probability).
PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes
We give a novel, unified derivation of conditional PAC-Bayesian and mutual information (MI) generalization bounds.
Generic E-Variables for Exact Sequential k-Sample Tests that allow for Optional Stopping
We develop E-variables for testing whether two or more data streams come from the same source or not, and more generally, whether the difference between the sources is larger than some minimal effect size.
Robust subgroup discovery
This novel model class allows us to formalise the problem of optimal robust subgroup discovery using the Minimum Description Length (MDL) principle, where we resort to optimal Normalised Maximum Likelihood and Bayesian encodings for nominal and numeric targets, respectively.
Discovering outstanding subgroup lists for numeric targets using MDL
We propose a dispersion-aware problem formulation for subgroup set discovery that is based on the minimum description length (MDL) principle and subgroup lists.
Safe-Bayesian Generalized Linear Regression
We study generalized Bayesian inference under misspecification, i. e. when the model is 'wrong but useful'.
Minimum Description Length Revisited
This is an up-to-date introduction to and overview of the Minimum Description Length (MDL) Principle, a theory of inductive inference that can be applied to general problems in statistics, machine learning and pattern recognition.
Safe Testing
We develop the theory of hypothesis testing based on the e-value, a notion of evidence that, unlike the p-value, allows for effortlessly combining results from several studies in the common scenario where the decision to perform a new study may depend on previous outcomes.
Optional Stopping with Bayes Factors: a categorization and extension of folklore results, with an application to invariant situations
It is often claimed that Bayesian methods, in particular Bayes factor methods for hypothesis testing, can deal with optional stopping.
Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning
We consider online learning algorithms that guarantee worst-case regret rates in adversarial environments (so they can be deployed safely and will perform robustly), yet adapt optimally to favorable stochastic environments (so they will perform well in a variety of settings of practical importance).
Safe Probability
We formalize the idea of probability distributions that lead to reliable predictions about some, but not all aspects of a domain.
Learning the Learning Rate for Prediction with Expert Advice
Most standard algorithms for prediction with expert advice depend on a parameter called the learning rate.
Mixability in Statistical Learning
We show that, in the special case of log-loss, stochastic mixability reduces to a well-known (but usually unnamed) martingale condition, which is used in existing convergence theorems for minimum description length and Bayesian inference.
Adaptive Hedge
In most previous analyses the learning rate was carefully tuned to obtain optimal worst-case performance, leading to suboptimal performance on easy instances, for example when there exists an action that is significantly better than all others.
