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07/27/2023
Universal and Transferable Adversarial Attacks on Aligned Language Models
Because "out-of-the-box" large language models are capable of generating...
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01/29/2023
Scaling in Depth: Unlocking Robustness Certification on ImageNet
Notwithstanding the promise of Lipschitz-based approaches to determinist...
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01/26/2023
Learning Modulo Theories
Recent techniques that integrate solver layers into Deep Neural Networks...
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09/08/2022
Black-Box Audits for Group Distribution Shifts
When a model informs decisions about people, distribution shifts can cre...
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06/01/2022
On the Perils of Cascading Robust Classifiers
Ensembling certifiably robust neural networks has been shown to be a pro...
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05/24/2022
Faithful Explanations for Deep Graph Models
This paper studies faithful explanations for Graph Neural Networks (GNNs...
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11/18/2021
Enhancing the Insertion of NOP Instructions to Obfuscate Malware via Deep Reinforcement Learning
Current state-of-the-art research for tackling the problem of malware de...
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11/16/2021
Selective Ensembles for Consistent Predictions
Recent work has shown that models trained to the same objective, and whi...
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10/06/2021
Consistent Counterfactuals for Deep Models
Counterfactual examples are one of the most commonly-cited methods for e...
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07/23/2021
Self-Repairing Neural Networks: Provable Safety for Deep Networks via Dynamic Repair
Neural networks are increasingly being deployed in contexts where safety...
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07/21/2021
Leave-one-out Unfairness
We introduce leave-one-out unfairness, which characterizes how likely a ...
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06/11/2021
Relaxing Local Robustness
Certifiable local robustness, which rigorously precludes small-norm adve...
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04/24/2021
The Design of the User Interfaces for Privacy Enhancements for Android
We present the design and design rationale for the user interfaces for P...
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03/20/2021
Boundary Attributions Provide Normal (Vector) Explanations
Recent work on explaining Deep Neural Networks (DNNs) focuses on attribu...
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02/16/2021
Globally-Robust Neural Networks
The threat of adversarial examples has motivated work on training certif...
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06/11/2020
Smoothed Geometry for Robust Attribution
Feature attributions are a popular tool for explaining the behavior of D...
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02/19/2020
Interpreting Interpretations: Organizing Attribution Methods by Criteria
Attribution methods that explains the behaviour of machine learning mode...
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02/18/2020
Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness
We turn the definition of individual fairness on its head—rather than as...
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02/12/2020
Fast Geometric Projections for Local Robustness Certification
Local robustness ensures that a model classifies all inputs within an ϵ-...
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06/27/2019
Learning Fair Representations for Kernel Models
Fair representations are a powerful tool for establishing criteria like ...
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06/27/2019
Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference
Membership inference (MI) attacks exploit a learned model's lack of gene...
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06/21/2019
FlipTest: Fairness Auditing via Optimal Transport
We present FlipTest, a black-box auditing technique for uncovering subgr...
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12/21/2018
Feature-Wise Bias Amplification
We study the phenomenon of bias amplification in classifiers, wherein a ...
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11/20/2018
ESTRELA: Automated Policy Enforcement Across Remote APIs
Web applications routinely access sensitive and confidential data of use...
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10/16/2018
Hunting for Discriminatory Proxies in Linear Regression Models
A machine learning model may exhibit discrimination when used to make de...
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02/11/2018
Influence-Directed Explanations for Deep Convolutional Networks
We study the problem of explaining a rich class of behavioral properties...
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09/27/2017
Case Study: Explaining Diabetic Retinopathy Detection Deep CNNs via Integrated Gradients
In this report, we applied integrated gradients to explaining a neural n...
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09/05/2017
The Unintended Consequences of Overfitting: Training Data Inference Attacks
Machine learning algorithms that are applied to sensitive data pose a di...
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11/24/2015