Bringing Managers into Theories of Multimarket Competition: CEOs and the Determinants of Market Entry. - John Stephan; Johann Peter Murmann; Warren Boeker; Jerry Goodstein (Organ. Sci., 2003)

HideMyApp: Hiding the Presence of Sensitive Apps on Android. - Anh Pham; Italo Dacosta; Eleonora Losiouk; John Stephan; Kévin Huguenin; Jean-Pierre Hubaux (USENIX Security Symposium, 2019)

Differential Privacy and Byzantine Resilience in SGD: Do They Add Up? - Rachid Guerraoui; Nirupam Gupta; Rafaël Pinot; Sébastien Rouault; John Stephan (PODC, 2021)

Combining Differential Privacy and Byzantine Resilience in Distributed SGD. - Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; Sébastien Rouault; John Stephan (CoRR, 2021)

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums. - Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; John Stephan (ICML, 2022)

Making Byzantine Decentralized Learning Efficient. - Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Lê Nguyên Hoang; Rafael Pinot; John Stephan (CoRR, 2022)

On the Impossible Safety of Large AI Models. - El-Mahdi El-Mhamdi; Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Lê-Nguyên Hoang; Rafael Pinot; John Stephan (CoRR, 2022)

Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity. - Youssef Allouah; Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; John Stephan (AISTATS, 2023)

On the Privacy-Robustness-Utility Trilemma in Distributed Learning. - Youssef Allouah; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; John Stephan (ICML, 2023)

Robust Collaborative Learning with Linear Gradient Overhead. - Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Lê-Nguyên Hoang; Rafael Pinot; John Stephan (ICML, 2023)

Distributed Learning with Curious and Adversarial Machines. - Youssef Allouah; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; John Stephan (CoRR, 2023)

Practical Homomorphic Aggregation for Byzantine ML. - Antoine Choffrut; Rachid Guerraoui; Rafael Pinot; Renaud Sirdey; John Stephan; Martin Zuber (CoRR, 2023)

Can Machines Learn Robustly, Privately, and Efficiently? - Youssef Allouah; Rachid Guerraoui; John Stephan (CoRR, 2023)

Towards Practical Homomorphic Aggregation in Byzantine-Resilient Distributed Learning. - Antoine Choffrut; Rachid Guerraoui; Rafael Pinot; Renaud Sirdey; John Stephan; Martin Zuber (Middleware, 2024)

Boosting Robustness by Clipping Gradients in Distributed Learning. - Youssef Allouah; Rachid Guerraoui; Nirupam Gupta; Ahmed Jellouli; Geovani Rizk; John Stephan (CoRR, 2024)

Overcoming the Challenges of Batch Normalization in Federated Learning. - Rachid Guerraoui; Rafael Pinot; Geovani Rizk; John Stephan; François Taïani (CoRR, 2024)

Adaptive Gradient Clipping for Robust Federated Learning. - Youssef Allouah; Rachid Guerraoui; Nirupam Gupta; Ahmed Jellouli; Geovani Rizk; John Stephan (ICLR, 2025)

CrowdProve: Community Proving for ZK Rollups. - John Stephan; Matej Pavlovic; Antonio Locascio; Benjamin Livshits (CoRR, 2025)

Towards Trustworthy Federated Learning with Untrusted Participants. - Youssef Allouah; Rachid Guerraoui; John Stephan (CoRR, 2025)

ByzFL: Research Framework for Robust Federated Learning. - Marc González 001; Rachid Guerraoui; Rafael Pinot; Geovani Rizk; John Stephan; François Taïani (CoRR, 2025)