Sven Gowal

According to our database1, Sven Gowal authored at least 55 papers between 2009 and 2024.

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Bibliography

2024
Evaluating Model Bias Requires Characterizing its Mistakes.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Generative models improve fairness of medical classifiers under distribution shifts.
CoRR, 2023

Differentially Private Diffusion Models Generate Useful Synthetic Images.
CoRR, 2023

Benchmarking Robustness to Adversarial Image Obfuscations.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Revisiting adapters with adversarial training.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning.
CoRR, 2022

Robustness of Epinets against Distributional Shifts.
CoRR, 2022

Competition-Level Code Generation with AlphaCode.
CoRR, 2022

Hindering Adversarial Attacks with Implicit Neural Representations.
Proceedings of the International Conference on Machine Learning, 2022

Evaluating the Adversarial Robustness of Adaptive Test-time Defenses.
Proceedings of the International Conference on Machine Learning, 2022

A Fine-Grained Analysis on Distribution Shift.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Defending Against Image Corruptions Through Adversarial Augmentations.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis.
Mach. Learn., 2021

An Empirical Investigation of Learning from Biased Toxicity Labels.
CoRR, 2021

A Closer Look at the Adversarial Robustness of Information Bottleneck Models.
CoRR, 2021

Fixing Data Augmentation to Improve Adversarial Robustness.
CoRR, 2021

Verifying Probabilistic Specifications with Functional Lagrangians.
CoRR, 2021

Data Augmentation Can Improve Robustness.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Improving Robustness using Generated Data.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Self-supervised Adversarial Robustness for the Low-label, High-data Regime.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples.
CoRR, 2020

An empirical investigation of the challenges of real-world reinforcement learning.
CoRR, 2020

The Autoencoding Variational Autoencoder.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Towards Stable and Efficient Training of Verifiably Robust Neural Networks.
Proceedings of the 8th International Conference on Learning Representations, 2020

Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control.
Proceedings of the 8th International Conference on Learning Representations, 2020

Towards Verified Robustness under Text Deletion Interventions.
Proceedings of the 8th International Conference on Learning Representations, 2020

A Framework for robustness Certification of Smoothed Classifiers using F-Divergences.
Proceedings of the 8th International Conference on Learning Representations, 2020

Towards Robust Image Classification Using Sequential Attention Models.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
An Alternative Surrogate Loss for PGD-based Adversarial Testing.
CoRR, 2019

Efficient Neural Network Verification with Exactness Characterization.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Adversarial Robustness through Local Linearization.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Dual Approach to Verify and Train Deep Networks.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems.
Proceedings of the 36th International Conference on Machine Learning, 2019

Verification of Non-Linear Specifications for Neural Networks.
Proceedings of the 7th International Conference on Learning Representations, 2019

Beyond Greedy Ranking: Slate Optimization via List-CVAE.
Proceedings of the 7th International Conference on Learning Representations, 2019

Scalable Verified Training for Provably Robust Image Classification.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation.
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

2018
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models.
CoRR, 2018

Learning from Delayed Outcomes with Intermediate Observations.
CoRR, 2018

Training verified learners with learned verifiers.
CoRR, 2018

Optimizing Slate Recommendations via Slate-CVAE.
CoRR, 2018

A Dual Approach to Scalable Verification of Deep Networks.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

2013
A Framework for Graph-Based Distributed Rendezvous of Nonholonomic Multi-Robot Systems.
PhD thesis, 2013

2012
A new collision warning system for lead vehicles in rear-end collisions.
Proceedings of the 2012 IEEE Intelligent Vehicles Symposium, 2012

Real-Time Optimized Rendezvous on Nonholonomic Resource-Constrained Robots.
Proceedings of the Experimental Robotics, 2012

Real-time optimization of trajectories that guarantee the rendezvous of mobile robots.
Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012

2011
Two-phase online calibration for infrared-based inter-robot positioning modules.
Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011

Bayesian rendezvous for distributed robotic systems.
Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011

2010
A realistic simulator for the design and evaluation of intelligent vehicles.
Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, 2010

Local graph-based distributed control for safe highway platooning.
Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010

Graph based distributed control of non-holonomic vehicles endowed with local positioning information engaged in escorting missions.
Proceedings of the IEEE International Conference on Robotics and Automation, 2010

2009
Graph-based distributed control for non-holonomic vehicles engaged in a reconfiguration task using local positioning information.
Proceedings of the 2nd International ICST Conference on Robot Communication and Coordination, 2009


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