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Found 8 papers, 2 papers with code
Fighting Selection Bias in Statistical Learning: Application to Visual Recognition from Biased Image Databases
In practice, and especially when training deep neural networks, visual recognition rules are often learned based on various sources of information.
A Multiclass Classification Approach to Label Ranking
However, in a wide variety of situations, the task targeted may be more ambitious, consisting in sorting all the possible label values $y$ that may be assigned to $X$ by decreasing order of the posterior probability $\eta_y(X)=\mathbb{P}\{Y=y \mid X \}$.
Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints
We establish generalization bounds for scoring functions learned under such constraints, design practical learning algorithms and show the relevance our approach with numerical experiments on real and synthetic data.
Weighted Empirical Risk Minimization: Sample Selection Bias Correction based on Importance Sampling
We consider statistical learning problems, when the distribution $P'$ of the training observations $Z'_1,\; \ldots,\; Z'_n$ differs from the distribution $P$ involved in the risk one seeks to minimize (referred to as the test distribution) but is still defined on the same measurable space as $P$ and dominates it.
Weighted Empirical Risk Minimization: Transfer Learning based on Importance Sampling
We consider statistical learning problems, when the distribution $P'$ of the training observations $Z'_1,\; \ldots,\; Z'_n$ differs from the distribution $P$ involved in the risk one seeks to minimize (referred to as the \textit{test distribution}) but is still defined on the same measurable space as $P$ and dominates it.
On Tree-based Methods for Similarity Learning
In many situations, the choice of an adequate similarity measure or metric on the feature space dramatically determines the performance of machine learning methods.
Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning
The development of cluster computing frameworks has allowed practitioners to scale out various statistical estimation and machine learning algorithms with minimal programming effort.
A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization
In this paper, similarity learning is investigated from the perspective of pairwise bipartite ranking, where the goal is to rank the elements of a database by decreasing order of the probability that they share the same label with some query data point, based on the similarity scores.
