533 citations · 571 across the 9 of their papers we have counts for
9 papers
GPTZero: Robust Detection of LLM-Generated Texts
George Alexandru Adam, Alexander Cui, Edwin Thomas +7
While historical considerations surrounding text authenticity revolved primarily around plagiarism, the advent of large language models (LLMs) has introduced a new challenge: disti…
Maintaining Stability and Plasticity for Predictive Churn Reduction
George Adam, Benjamin Haibe-Kains, Anna Goldenberg
Deployed machine learning models should be updated to take advantage of a larger sample size to improve performance, as more data is gathered over time. Unfortunately, even when mo…
Extracting Expert's Goals by What-if Interpretable Modeling
Chun-Hao Chang, George Alexandru Adam, Rich Caruana +1
Although reinforcement learning (RL) has tremendous success in many fields, applying RL to real-world settings such as healthcare is challenging when the reward is hard to specify…
Towards Robust Classification Model by Counterfactual and Invariant Data Generation
Chun-Hao Chang, George Alexandru Adam, Anna Goldenberg
Despite the success of machine learning applications in science, industry, and society in general, many approaches are known to be non-robust, often relying on spurious correlation…
Evaluating Ensemble Robustness Against Adversarial Attacks
George Adam, Romain Speciel
Adversarial examples, which are slightly perturbed inputs generated with the aim of fooling a neural network, are known to transfer between models; adversaries which are effective…
The importance of transparency and reproducibility in artificial intelligence research
Benjamin Haibe-Kains, George Alexandru Adam, Ahmed Hosny +17
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines…
Reducing Adversarial Example Transferability Using Gradient Regularization
George Adam, Petr Smirnov, Benjamin Haibe-Kains +1
Deep learning algorithms have increasingly been shown to lack robustness to simple adversarial examples (AdvX). An equally troubling observation is that these adversarial examples…
Understanding Neural Architecture Search Techniques
George Adam, Jonathan Lorraine
Automatic methods for generating state-of-the-art neural network architectures without human experts have generated significant attention recently. This is because of the potential…
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples
George A. Adam, Petr Smirnov, David Duvenaud +2
Many deep learning algorithms can be easily fooled with simple adversarial examples. To address the limitations of existing defenses, we devised a probabilistic framework that can…