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Found 8 papers, 2 papers with code
Automated Testing of AI Models
The last decade has seen tremendous progress in AI technology and applications.
A Framework for Learning Ante-hoc Explainable Models via Concepts
To the best of our knowledge, we are the first ante-hoc explanation generation method to show results with a large-scale dataset such as ImageNet.
Verifying Individual Fairness in Machine Learning Models
Our objective is to construct verifiers for proving individual fairness of a given model, and we do so by considering appropriate relaxations of the problem.
Exploring the Hyperparameter Landscape of Adversarial Robustness
We present a sensitivity analysis that illustrates that the effectiveness of adversarial training hinges on the settings of a few salient hyperparameters.
An ADMM Based Framework for AutoML Pipeline Configuration
We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines.
Explaining Deep Learning Models using Causal Inference
Although deep learning models have been successfully applied to a variety of tasks, due to the millions of parameters, they are becoming increasingly opaque and complex.
Hardening Deep Neural Networks via Adversarial Model Cascades
Deep neural networks (DNNs) are vulnerable to malicious inputs crafted by an adversary to produce erroneous outputs.
Debugging Machine Learning Tasks
Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data.
