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07/17/2023
A General Framework for Learning under Corruption: Label Noise, Attribute Noise, and Beyond
Corruption is frequently observed in collected data and has been extensi...
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06/26/2023
Insights From Insurance for Fair Machine Learning: Responsibility, Performativity and Aggregates
We argue that insurance can act as an analogon for the social situatedne...
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02/23/2023
The Geometry of Mixability
Mixable loss functions are of fundamental importance in the context of p...
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02/08/2023
On the Richness of Calibration
Probabilistic predictions can be evaluated through comparisons with obse...
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02/07/2023
The Set Structure of Precision: Coherent Probabilities on Pre-Dynkin-Systems
In literature on imprecise probability little attention is paid to the f...
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02/07/2023
Strictly Frequentist Imprecise Probability
Strict frequentism defines probability as the limiting relative frequenc...
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09/01/2022
The Geometry and Calculus of Losses
Statistical decision problems are the foundation of statistical machine ...
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08/05/2022
Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity
Expected risk minimization (ERM) is at the core of machine learning syst...
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07/27/2022
Fairness and Randomness in Machine Learning: Statistical Independence and Relativization
Fair Machine Learning endeavors to prevent unfairness arising in the con...
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07/25/2022
Information Processing Equalities and the Information-Risk Bridge
We introduce two new classes of measures of information for statistical ...
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06/07/2022
Risk Measures and Upper Probabilities: Coherence and Stratification
Machine learning typically presupposes classical probability theory whic...
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05/19/2022
What killed the Convex Booster ?
A landmark negative result of Long and Servedio established a worst-case...
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06/26/2020
PAC-Bayesian Bound for the Conditional Value at Risk
Conditional Value at Risk (CVaR) is a family of "coherent risk measures"...
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02/19/2019
Proper-Composite Loss Functions in Arbitrary Dimensions
The study of a machine learning problem is in many ways is difficult to ...
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02/03/2019
Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds
Since the introduction of Generative Adversarial Networks (GANs) and Var...
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01/24/2019
Fairness risk measures
Ensuring that classifiers are non-discriminatory or fair with respect to...
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05/20/2018
Exp-Concavity of Proper Composite Losses
The goal of online prediction with expert advice is to find a decision s...
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05/20/2018
Minimax Lower Bounds for Cost Sensitive Classification
The cost-sensitive classification problem plays a crucial role in missio...
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02/20/2018
Constant Regret, Generalized Mixability, and Mirror Descent
We consider the setting of prediction with expert advice; a learner make...
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02/20/2018
Generalized Mixability Constant Regret, Generalized Mixability, and Mirror Descent
We consider the setting of prediction with expert advice; a learner make...
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07/14/2017
f-GANs in an Information Geometric Nutshell
Nowozin et al showed last year how to extend the GAN principle to all f-...
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07/09/2015
Fast rates in statistical and online learning
The speed with which a learning algorithm converges as it is presented w...
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06/04/2015
An Average Classification Algorithm
Many classification algorithms produce a classifier that is a weighted a...
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04/01/2015
Learning in the Presence of Corruption
In supervised learning one wishes to identify a pattern present in a joi...
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04/01/2015
A Theory of Feature Learning
Feature Learning aims to extract relevant information contained in data ...
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03/10/2014
Generalised Mixability, Constant Regret, and Bayesian Updating
Mixability of a loss is known to characterise when constant regret bound...
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02/20/2014
Le Cam meets LeCun: Deficiency and Generic Feature Learning
"Deep Learning" methods attempt to learn generic features in an unsuperv...
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12/17/2009
