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Found 11 papers, 4 papers with code
Loss Balancing for Fair Supervised Learning
Imposing EL on the learning process leads to a non-convex optimization problem even if the loss function is convex, and the existing fair learning algorithms cannot properly be adopted to find the fair predictor under the EL constraint.
Revisiting DeepFool: generalization and improvement
Our proposed attacks are also suitable for evaluating the robustness of large models and can be used to perform adversarial training (AT) to achieve state-of-the-art robustness to minimal l2 adversarial perturbations.
Safe AI for health and beyond -- Monitoring to transform a health service
Machine learning techniques are effective for building predictive models because they identify patterns in large datasets.
Symbolic Metamodels for Interpreting Black-boxes Using Primitive Functions
This composition can be represented in the form of a tree.
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift
A common assumption in semi-supervised learning is that the labeled, unlabeled, and test data are drawn from the same distribution.
Fair Sequential Selection Using Supervised Learning Models
This observation implies that the fairness notions used in classification problems are not suitable for a selection problem where the applicants compete for a limited number of positions.
Non-convex Optimization for Learning a Fair Predictor under Equalized Loss Fairness Constraint
In general, finding a fair predictor leads to a constrained optimization problem, and depending on the fairness notion, it may be non-convex.
Interpreting Black-boxes Using Primitive Parameterized Functions
One approach for interpreting black-box machine learning models is to find a global approximation of the model using simple interpretable functions, which is called a metamodel (a model of the model).
Conservative Policy Construction Using Variational Autoencoders for Logged Data with Missing Values
Secondly, such datasets are usually imperfect, additionally cursed with missing values in the attributes of features.
Improving Fairness and Privacy in Selection Problems
In this work, we study the possibility of using a differentially private exponential mechanism as a post-processing step to improve both fairness and privacy of supervised learning models.
Multilayer Codes for Synchronization from Deletions
Consider two remote nodes, each having a binary sequence.
Information Theory Information Theory
