Robert Stanforth | DeepAI

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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...

0 Leonard Berrada, et al. ∙

∙ 05/23/2023

Expressive Losses for Verified Robustness via Convex Combinations

In order to train networks for verified adversarial robustness, previous...

0 Alessandro De Palma, et al. ∙

∙ 02/27/2023

Differentially Private Diffusion Models Generate Useful Synthetic Images

The ability to generate privacy-preserving synthetic versions of sensiti...

3 Sahra Ghalebikesabi, et al. ∙

∙ 06/29/2022

IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound

Recent works have tried to increase the verifiability of adversarially t...

0 Alessandro De Palma, et al. ∙

∙ 02/18/2021

Verifying Probabilistic Specifications with Functional Lagrangians

We propose a general framework for verifying input-output specifications...

0 Leonard Berrada, et al. ∙

∙ 07/10/2020

Contrastive Training for Improved Out-of-Distribution Detection

Reliable detection of out-of-distribution (OOD) inputs is increasingly u...

3 Jim Winkens, et al. ∙

∙ 11/08/2019

Reducing Sentiment Bias in Language Models via Counterfactual Evaluation

Recent improvements in large-scale language models have driven progress ...

0 Po-Sen Huang, et al. ∙

∙ 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...

1 Po-Sen Huang, et al. ∙

∙ 07/04/2019

Adversarial Robustness through Local Linearization

Adversarial training is an effective methodology for training deep neura...

3 Chongli Qin, et al. ∙

∙ 05/31/2019

Are Labels Required for Improving Adversarial Robustness?

Recent work has uncovered the interesting (and somewhat surprising) find...

0 Jonathan Uesato, et al. ∙

∙ 02/25/2019

Verification of Non-Linear Specifications for Neural Networks

Prior work on neural network verification has focused on specifications ...

14 Chongli Qin, et al. ∙

∙ 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...

0 Edward Grefenstette, et al. ∙

∙ 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...

0 Sven Gowal, et al. ∙

∙ 05/25/2018

Training verified learners with learned verifiers

This paper proposes a new algorithmic framework,predictor-verifier train...

0 Krishnamurthy Dvijotham, et al. ∙

∙ 03/17/2018

A Dual Approach to Scalable Verification of Deep Networks

This paper addresses the problem of formally verifying desirable propert...

0 Krishnamurthy Dvijotham, et al. ∙

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