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Found 11 papers, 4 papers with code
Invariant Causal Prediction with Locally Linear Models
We consider the task of identifying the causal parents of a target variable among a set of candidate variables from observational data.
Loss Bounds for Approximate Influence-Based Abstraction
Sequential decision making techniques hold great promise to improve the performance of many real-world systems, but computational complexity hampers their principled application.
A Note on High-Probability versus In-Expectation Guarantees of Generalization Bounds in Machine Learning
Following that, statements made about the performance of machine learning models have to take the sampling process into account.
A Brief Prehistory of Double Descent
In their thought-provoking paper [1], Belkin et al. illustrate and discuss the shape of risk curves in the context of modern high-complexity learners.
Consistency and Finite Sample Behavior of Binary Class Probability Estimation
Our main contribution is to present a way to derive finite sample L1-convergence rates of this estimator for different surrogate loss functions.
Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results
In this review we gather results about the possible gains one can achieve when using semi-supervised learning as well as results about the limits of such methods.
Minimizers of the Empirical Risk and Risk Monotonicity
Plotting a learner's average performance against the number of training samples results in a learning curve.
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization
Here, we derive sample complexity bounds based on pseudo-dimension for models that add a convex data dependent regularization term to a supervised learning process, as is in particular done in Manifold regularization.
Semi-Supervised Learning, Causality and the Conditional Cluster Assumption
While the success of semi-supervised learning (SSL) is still not fully understood, Sch\"olkopf et al. (2012) have established a link to the principle of independent causal mechanisms.
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features
Current methods for covariate-shift adaptation use unlabelled data to compute importance weights or domain-invariant features, while the final model is trained on labelled data only.
