21 citations
5 papers · 1 filter
Individually Fair Gradient Boosting
Alexander Vargo, Fan Zhang, Mikhail Yurochkin +1
We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in appli…
Generating Adversarial Computer Programs using Optimized Obfuscations
Shashank Srikant, Sijia Liu, Tamara Mitrovska +4
Machine learning (ML) models that learn and predict properties of computer programs are increasingly being adopted and deployed. These models have demonstrated success in applicati…
Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples
Washington Garcia, Pin-Yu Chen, Somesh Jha +2
Designing deep networks robust to adversarial examples remains an open problem. Likewise, recent zeroth order hard-label attacks on image classification models have shown comparabl…
Adam: A Stochastic Method with Adaptive Variance Reduction
Mingrui Liu, Wei Zhang, Francesco Orabona +1
Adam is a widely used stochastic optimization method for deep learning applications. While practitioners prefer Adam because it requires less parameter tuning, its use is problemat…
TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series
Yang Jiao, Kai Yang, Shaoyu Dou +3
Multivariate time series (MTS) data are becoming increasingly ubiquitous in diverse domains, e.g., IoT systems, health informatics, and 5G networks. To obtain an effective represen…