Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models. - Daniel de Vassimon Manela; David Errington; Thomas Fisher; Boris van Breugel; Pasquale Minervini (EACL, 2021)

DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks. - Boris van Breugel; Trent Kyono; Jeroen Berrevoets; Mihaela van der Schaar (NeurIPS, 2021)

How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models. - Ahmed M. Alaa; Boris van Breugel; Evgeny S. Saveliev; Mihaela van der Schaar (ICML, 2022)

DAUX: a Density-based Approach for Uncertainty eXplanations. - Hao Sun 017; Boris van Breugel; Jonathan Crabbé; Nabeel Seedat; Mihaela van der Schaar (CoRR, 2022)

Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes. - Tennison Liu; Alex J. Chan; Boris van Breugel; Mihaela van der Schaar (CoRR, 2022)

Membership Inference Attacks against Synthetic Data through Overfitting Detection. - Boris van Breugel; Hao Sun 017; Zhaozhi Qian; Mihaela van der Schaar (AISTATS, 2023)

Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data. - Boris van Breugel; Zhaozhi Qian; Mihaela van der Schaar (ICML, 2023)

Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data. - Boris van Breugel; Nabeel Seedat; Fergus Imrie; Mihaela van der Schaar (NeurIPS, 2023)

What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization. - Hao Sun 017; Boris van Breugel; Jonathan Crabbé; Nabeel Seedat; Mihaela van der Schaar (NeurIPS, 2023)

Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data. - Boris van Breugel; Mihaela van der Schaar (CoRR, 2023)

Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in ultra low-data regimes. - Nabeel Seedat; Nicolas Huynh; Boris van Breugel; Mihaela van der Schaar (CoRR, 2023)

RadEdit: Stress-Testing Biomedical Vision Models via Diffusion Image Editing. - Fernando Pérez-García; Sam Bond-Taylor; Pedro P. Sanchez; Boris van Breugel; Daniel C. Castro; Harshita Sharma; Valentina Salvatelli; Maria T. A. Wetscherek; Hannah Richardson; Matthew P. Lungren; Aditya V. Nori; Javier Alvarez-Valle; et al. (ECCV (12), 2024)

Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models. - Yangming Li; Boris van Breugel; Mihaela van der Schaar (ICLR, 2024)

Position: Why Tabular Foundation Models Should Be a Research Priority. - Boris van Breugel; Mihaela van der Schaar (ICML, 2024)

Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes. - Nabeel Seedat; Nicolas Huynh; Boris van Breugel; Mihaela van der Schaar (ICML, 2024)

Why Tabular Foundation Models Should Be a Research Priority. - Boris van Breugel; Mihaela van der Schaar (CoRR, 2024)

LaTable: Towards Large Tabular Models. - Boris van Breugel; Jonathan Crabbé; Rob Davis; Mihaela van der Schaar (CoRR, 2024)

Position: All Current Generative Fidelity and Diversity Metrics are Flawed. - Ossi Räisä; Boris van Breugel; Mihaela van der Schaar (CoRR, 2025)

FPTQuant: Function-Preserving Transforms for LLM Quantization. - Boris van Breugel; Yelysei Bondarenko; Paul N. Whatmough; Markus Nagel (CoRR, 2025)

HadaNorm: Diffusion Transformer Quantization through Mean-Centered Transformations. - Marco Federici; Riccardo Del Chiaro; Boris van Breugel; Paul N. Whatmough; Markus Nagel (CoRR, 2025)