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08/21/2023
Unlocking Accuracy and Fairness in Differentially Private Image Classification
Privacy-preserving machine learning aims to train models on private data...
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05/23/2023
Expressive Losses for Verified Robustness via Convex Combinations
In order to train networks for verified adversarial robustness, previous...
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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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06/29/2022
IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound
Recent works have tried to increase the verifiability of adversarially t...
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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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07/10/2020
Contrastive Training for Improved Out-of-Distribution Detection
Reliable detection of out-of-distribution (OOD) inputs is increasingly u...
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11/08/2019
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation
Recent improvements in large-scale language models have driven progress ...
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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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05/31/2019
Are Labels Required for Improving Adversarial Robustness?
Recent work has uncovered the interesting (and somewhat surprising) find...
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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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11/22/2018
Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles
While deep learning has led to remarkable results on a number of challen...
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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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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