← Authors

Sven Gowal

28 papers · 2019–2025 · 10 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (10) πŸƒ Academic Marathon (6) πŸ—ΊοΈ Taxonomy Completionist (36) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (12) 🀝 Dynamic Duo (13) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (10) πŸ† Keyword Champion (3) πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (60) ⚑ Prolific Year (6) πŸ”₯ Unstoppable (7) πŸ’Ž Century Club (28)

Conferences

ICLR (9) NIPS (6) ICML (4) CVPR (3) AISTATS (1) EMNLP (1) ICCV (1) IJCAI (1) IJCNLP (1) UAI (1)

Top co-authors

Pushmeet Kohli (13) Robert Stanforth (10) Krishnamurthy Dvijotham (7) Sylvestre-Alvise Rebuffi (6) Po-Sen Huang (6) Chongli Qin (6) Krishnamurthy (Dj) Dvijotham (5) Olivia Wiles (5) Timothy Mann (5) Florian Stimberg (5)

Keywords

adversarial robustness (14) adversarial training (5) formal verification (4) adversarial attack (4) neural network verification (4) interval bound propagation (3) data augmentation (3) image classification (3) model robustness (2) lagrangian relaxation (2) neural network (2) text classification (2) generative model (2) verified accuracy (2) symbol substitution (2) content moderation (1) domain generalization (1) attention mechanism (1) online learning (1) self-supervised learning (1)

Papers

On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark AISTATS 2025
Evaluating Model Bias Requires Characterizing its Mistakes ICML 2024
Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts CVPR 2023
Revisiting adapters with adversarial training ICLR 2023
Benchmarking Robustness to Adversarial Image Obfuscations NIPS 2023
Defending Against Image Corruptions Through Adversarial Augmentations ICLR 2022
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses ICML 2022
Hindering Adversarial Attacks with Implicit Neural Representations ICML 2022
A Fine-Grained Analysis on Distribution Shift ICLR 2022
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications NIPS 2021
Data Augmentation Can Improve Robustness NIPS 2021
Self-supervised Adversarial Robustness for the Low-label, High-data Regime ICLR 2021
Improving Robustness using Generated Data NIPS 2021
Towards Stable and Efficient Training of Verifiably Robust Neural Networks ICLR 2020
Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations CVPR 2020
Towards Robust Image Classification Using Sequential Attention Models CVPR 2020
The Autoencoding Variational Autoencoder NIPS 2020
A FRAMEWORK FOR ROBUSTNESS CERTIFICATION OF SMOOTHED CLASSIFIERS USING F-DIVERGENCES ICLR 2020
Towards Verified Robustness under Text Deletion Interventions ICLR 2020
Efficient Neural Network Verification with Exactness Characterization UAI 2019
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation EMNLP 2019
Scalable Verified Training for Provably Robust Image Classification ICCV 2019
Beyond Greedy Ranking: Slate Optimization via List-CVAE ICLR 2019
Verification of Non-Linear Specifications for Neural Networks ICLR 2019
Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems ICML 2019
A Dual Approach to Verify and Train Deep Networks IJCAI 2019
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation IJCNLP 2019
Adversarial Robustness through Local Linearization NIPS 2019