250 citations · 532 across the 72 of their papers we have counts for
5 papers · 2 filters
Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense
Bao Gia Doan, Ehsan Abbasnejad, Javen Qinfeng Shi +1
We present a new algorithm to learn a deep neural network model robust against adversarial attacks. Previous algorithms demonstrate an adversarially trained Bayesian Neural Network…
ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets
Damien Teney, Yong Lin, Seong Joon Oh +1
Several studies have compared the in-distribution (ID) and out-of-distribution (OOD) performance of models in computer vision and NLP. They report a frequent positive correlation a…
Truncated Matrix Power Iteration for Differentiable DAG Learning
Zhen Zhang, Ignavier Ng, Dong Gong +5
Recovering underlying Directed Acyclic Graph (DAG) structures from observational data is highly challenging due to the combinatorial nature of the DAG-constrained optimization prob…
Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning
Damien Teney, Maxime Peyrard, Ehsan Abbasnejad
Machine learning (ML) models are typically optimized for their accuracy on a given dataset. However, this predictive criterion rarely captures all desirable properties of a model,…
Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Models
Viet Quoc Vo, Ehsan Abbasnejad, Damith C. Ranasinghe
Despite our best efforts, deep learning models remain highly vulnerable to even tiny adversarial perturbations applied to the inputs. The ability to extract information from solely…