Variational Causal Networks: Approximate Bayesian Inference over Causal Structures. - Yashas Annadani; Jonas Rothfuss; Alexandre Lacoste; Nino Scherrer; Anirudh Goyal; Yoshua Bengio; Stefan Bauer (CoRR, 2021)

Learning Neural Causal Models with Active Interventions. - Nino Scherrer; Olexa Bilaniuk; Yashas Annadani; Anirudh Goyal; Patrick Schwab; Bernhard Schölkopf; Michael C. Mozer; Yoshua Bengio; Stefan Bauer; Nan Rosemary Ke (CoRR, 2021)

On the Generalization and Adaption Performance of Causal Models. - Nino Scherrer; Anirudh Goyal; Stefan Bauer; Yoshua Bengio; Nan Rosemary Ke (CoRR, 2022)

FED-CD: Federated Causal Discovery from Interventional and Observational Data. - Amin Abyaneh; Nino Scherrer; Patrick Schwab; Stefan Bauer; Bernhard Schölkopf; Arash Mehrjou (CoRR, 2022)

Trust Your 𝛁: Gradient-based Intervention Targeting for Causal Discovery. - Mateusz Olko; Michal Zajac 005; Aleksandra Nowak 001; Nino Scherrer; Yashas Annadani; Stefan Bauer; Lukasz Kucinski; Piotr Milos (NeurIPS, 2023)

Evaluating the Moral Beliefs Encoded in LLMs. - Nino Scherrer; Claudia Shi; Amir Feder; David M. Blei (NeurIPS, 2023)

Uncovering mesa-optimization algorithms in Transformers. - Johannes von Oswald; Eyvind Niklasson; Maximilian Schlegel; Seijin Kobayashi; Nicolas Zucchet; Nino Scherrer; Nolan Miller; Mark Sandler 002; Blaise Agüera y Arcas; Max Vladymyrov; Razvan Pascanu; João Sacramento (CoRR, 2023)

SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models. - Bertie Vidgen; Hannah Rose Kirk; Rebecca Qian; Nino Scherrer; Anand Kannappan; Scott A. Hale; Paul Röttger (CoRR, 2023)

FinanceBench: A New Benchmark for Financial Question Answering. - Pranab Islam; Anand Kannappan; Douwe Kiela; Rebecca Qian; Nino Scherrer; Bertie Vidgen (CoRR, 2023)

Introducing v0.5 of the AI Safety Benchmark from MLCommons. - Bertie Vidgen; Adarsh Agrawal; Ahmed M. Ahmed 004; Victor Akinwande; Namir Al-Nuaimi; Najla Alfaraj; Elie Alhajjar; Lora Aroyo; Trupti Bavalatti; Borhane Blili-Hamelin; Kurt D. Bollacker; Rishi Bomassani; et al. (CoRR, 2024)

Multi-agent cooperation through learning-aware policy gradients. - Alexander Meulemans; Seijin Kobayashi; Johannes von Oswald; Nino Scherrer; Eric Elmoznino; Blake Aaron Richards; Guillaume Lajoie; Blaise Agüera y Arcas; João Sacramento (ICLR, 2025)

Not Every AI Problem is a Data Problem: We Should Be Intentional About Data Scaling. - Tanya Rodchenko; Natasha Noy; Nino Scherrer; Jennifer Prendki (CoRR, 2025)

Review, Refine, Repeat: Understanding Iterative Decoding of AI Agents with Dynamic Evaluation and Selection. - Souradip Chakraborty; Mohammadreza Pourreza; Ruoxi Sun 002; Yiwen Song; Nino Scherrer; Furong Huang; Amrit Singh Bedi; Ahmad Beirami; Jindong Gu; Hamid Palangi; Tomas Pfister (CoRR, 2025)

MesaNet: Sequence Modeling by Locally Optimal Test-Time Training. - Johannes von Oswald; Nino Scherrer; Seijin Kobayashi; Luca Versari; Songlin Yang; Maximilian Schlegel; Kaitlin Maile; Yanick Schimpf; Oliver Sieberling; Alexander Meulemans; Rif A. Saurous; Guillaume Lajoie; et al. (CoRR, 2025)