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06/14/2023
Selective Concept Models: Permitting Stakeholder Customisation at Test-Time
Concept-based models perform prediction using a set of concepts that are...
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05/28/2023
Training Private Models That Know What They Don't Know
Training reliable deep learning models which avoid making overconfident ...
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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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03/22/2023
Human Uncertainty in Concept-Based AI Systems
Placing a human in the loop may abate the risks of deploying AI systems ...
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02/02/2023
Provably Bounding Neural Network Preimages
Most work on the formal verification of neural networks has focused on b...
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12/14/2022
Interactive Concept Bottleneck Models
Concept bottleneck models (CBMs) (Koh et al. 2020) are interpretable neu...
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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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12/13/2021
Role of Human-AI Interaction in Selective Prediction
Recent work has shown the potential benefit of selective prediction syst...
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04/14/2021
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition
We improve the scalability of Branch and Bound (BaB) algorithms for form...
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12/07/2020
Autoencoding Variational Autoencoder
Does a Variational AutoEncoder (VAE) consistently encode typical samples...
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10/22/2020
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
Convex relaxations have emerged as a promising approach for verifying de...
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02/24/2020
Lagrangian Decomposition for Neural Network Verification
A fundamental component of neural network verification is the computatio...
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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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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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04/26/2019
Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications
Models such as Sequence-to-Sequence and Image-to-Sequence are widely use...
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12/06/2018
Verification of deep probabilistic models
Probabilistic models are a critical part of the modern deep learning too...
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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
A Dual Approach to Scalable Verification of Deep Networks
This paper addresses the problem of formally verifying desirable propert...
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01/26/2018
Safe Exploration in Continuous Action Spaces
We address the problem of deploying a reinforcement learning (RL) agent ...
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06/21/2016
