7 citations · 27 across the 14 of their papers we have counts for
7 papers · 1 filter
COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Data
Hossein Aboutalebi, Maya Pavlova, Mohammad Javad Shafiee +3
Since the World Health Organization declared COVID-19 a pandemic in 2020, the global community has faced ongoing challenges in controlling and mitigating the transmission of the SA…
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning
Maziar Gomrokchi, Susan Amin, Hossein Aboutalebi +2
While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial attack strategies in literature tailored for…
Residual Error: a New Performance Measure for Adversarial Robustness
Hossein Aboutalebi, Mohammad Javad Shafiee, Michelle Karg +2
Despite the significant advances in deep learning over the past decade, a major challenge that limits the wide-spread adoption of deep learning has been their fragility to adversar…
Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards
Susan Amin, Maziar Gomrokchi, Hossein Aboutalebi +2
A major challenge in reinforcement learning is the design of exploration strategies, especially for environments with sparse reward structures and continuous state and action space…
Self-Gradient Networks
Hossein Aboutalebi, Mohammad Javad Shafiee Alexander Wong
The incredible effectiveness of adversarial attacks on fooling deep neural networks poses a tremendous hurdle in the widespread adoption of deep learning in safety and security-cri…
Vulnerability Under Adversarial Machine Learning: Bias or Variance?
Hossein Aboutalebi, Mohammad Javad Shafiee, Michelle Karg +2
Prior studies have unveiled the vulnerability of the deep neural networks in the context of adversarial machine learning, leading to great recent attention into this area. One inte…
Learning Modular Safe Policies in the Bandit Setting with Application to Adaptive Clinical Trials
Hossein Aboutalebi, Doina Precup, Tibor Schuster
The stochastic multi-armed bandit problem is a well-known model for studying the exploration-exploitation trade-off. It has significant possible applications in adaptive clinical t…