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02/27/2023
Differentially Private Diffusion Models Generate Useful Synthetic Images
The ability to generate privacy-preserving synthetic versions of sensiti...
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02/20/2023
Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts
Adversarial training is widely used to make classifiers robust to a spec...
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01/30/2023
Benchmarking Robustness to Adversarial Image Obfuscations
Automated content filtering and moderation is an important tool that all...
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10/22/2022
Hindering Adversarial Attacks with Implicit Neural Representations
We introduce the Lossy Implicit Network Activation Coding (LINAC) defenc...
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10/10/2022
Revisiting adapters with adversarial training
While adversarial training is generally used as a defense mechanism, rec...
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08/18/2022
Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning
Automatically discovering failures in vision models under real-world set...
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07/01/2022
Robustness of Epinets against Distributional Shifts
Recent work introduced the epinet as a new approach to uncertainty model...
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02/28/2022
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses
Adaptive defenses that use test-time optimization promise to improve rob...
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02/08/2022
Competition-Level Code Generation with AlphaCode
Programming is a powerful and ubiquitous problem-solving tool. Developin...
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11/09/2021
Data Augmentation Can Improve Robustness
Adversarial training suffers from robust overfitting, a phenomenon where...
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10/21/2021
A Fine-Grained Analysis on Distribution Shift
Robustness to distribution shifts is critical for deploying machine lear...
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10/18/2021
Improving Robustness using Generated Data
Recent work argues that robust training requires substantially larger da...
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10/04/2021
An Empirical Investigation of Learning from Biased Toxicity Labels
Collecting annotations from human raters often results in a trade-off be...
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07/12/2021
A Closer Look at the Adversarial Robustness of Information Bottleneck Models
We study the adversarial robustness of information bottleneck models for...
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04/02/2021
Defending Against Image Corruptions Through Adversarial Augmentations
Modern neural networks excel at image classification, yet they remain vu...
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03/02/2021
Fixing Data Augmentation to Improve Adversarial Robustness
Adversarial training suffers from robust overfitting, a phenomenon where...
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02/18/2021
Verifying Probabilistic Specifications with Functional Lagrangians
We propose a general framework for verifying input-output specifications...
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12/07/2020
Autoencoding Variational Autoencoder
Does a Variational AutoEncoder (VAE) consistently encode typical samples...
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10/07/2020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples
Adversarial training and its variants have become de facto standards for...
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03/24/2020
An empirical investigation of the challenges of real-world reinforcement learning
Reinforcement learning (RL) has proven its worth in a series of artifici...
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12/06/2019
Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations
Recent research has made the surprising finding that state-of-the-art de...
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12/04/2019
Towards Robust Image Classification Using Sequential Attention Models
In this paper we propose to augment a modern neural-network architecture...
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10/21/2019
An Alternative Surrogate Loss for PGD-based Adversarial Testing
Adversarial testing methods based on Projected Gradient Descent (PGD) ar...
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09/03/2019
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation
Neural networks are part of many contemporary NLP systems, yet their emp...
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07/04/2019
Adversarial Robustness through Local Linearization
Adversarial training is an effective methodology for training deep neura...
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02/25/2019
Verification of Non-Linear Specifications for Neural Networks
Prior work on neural network verification has focused on specifications ...
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10/30/2018
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models
Recent works have shown that it is possible to train models that are ver...
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07/24/2018
Learning from Delayed Outcomes with Intermediate Observations
Optimizing for long term value is desirable in many practical applicatio...
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05/25/2018
Training verified learners with learned verifiers
This paper proposes a new algorithmic framework,predictor-verifier train...
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03/17/2018
A Dual Approach to Scalable Verification of Deep Networks
This paper addresses the problem of formally verifying desirable propert...
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03/05/2018
