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11/03/2020
Loss Bounds for Approximate Influence-Based Abstraction
Sequential decision making techniques hold great promise to improve the ...
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10/06/2020
A Note on High-Probability versus In-Expectation Guarantees of Generalization Bounds in Machine Learning
Statistical machine learning theory often tries to give generalization g...
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04/07/2020
A Brief Prehistory of Double Descent
In their thought-provoking paper [1], Belkin et al. illustrate and discu...
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11/25/2019
Making Learners (More) Monotone
Learning performance can show non-monotonic behavior. That is, more data...
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08/30/2019
Consistency and Finite Sample Behavior of Binary Class Probability Estimation
In this work we investigate to which extent one can recover class probab...
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08/26/2019
Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results
Semi-supervised learning is a setting in which one has labeled and unlab...
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07/11/2019
Minimizers of the Empirical Risk and Risk Monotonicity
Plotting a learner's average performance against the number of training ...
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06/14/2019
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization
Manifold regularization is a commonly used technique in semi-supervised ...
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05/28/2019
Semi-Supervised Learning, Causality and the Conditional Cluster Assumption
While the success of semi-supervised learning (SSL) is still not fully u...
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07/20/2018