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2020 – today
- 2024
[c11]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, Ozan Oktay, Maximilian Ilse:
RadEdit: Stress-Testing Biomedical Vision Models via Diffusion Image Editing. ECCV (12) 2024: 358-376- [c10]Yangming Li, Boris van Breugel, Mihaela van der Schaar:
Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models. ICLR 2024 - [c9]Boris van Breugel, Mihaela van der Schaar:
Position: Why Tabular Foundation Models Should Be a Research Priority. ICML 2024 - [c8]Nabeel Seedat, Nicolas Huynh, Boris van Breugel, Mihaela van der Schaar:
Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes. ICML 2024 - [i14]Boris van Breugel, Mihaela van der Schaar:
Why Tabular Foundation Models Should Be a Research Priority. CoRR abs/2405.01147 (2024) - [i13]Boris van Breugel, Jonathan Crabbé, Rob Davis, Mihaela van der Schaar:
LaTable: Towards Large Tabular Models. CoRR abs/2406.17673 (2024) - 2023
- [c7]Boris van Breugel, Hao Sun, Zhaozhi Qian, Mihaela van der Schaar:
Membership Inference Attacks against Synthetic Data through Overfitting Detection. AISTATS 2023: 3493-3514 - [c6]Boris van Breugel, Zhaozhi Qian, Mihaela van der Schaar:
Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data. ICML 2023: 34793-34808 - [c5]Boris van Breugel, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data. NeurIPS 2023 - [c4]Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar:
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization. NeurIPS 2023 - [i12]Boris van Breugel, Hao Sun, Zhaozhi Qian, Mihaela van der Schaar:
Membership Inference Attacks against Synthetic Data through Overfitting Detection. CoRR abs/2302.12580 (2023) - [i11]Boris van Breugel, Mihaela van der Schaar:
Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data. CoRR abs/2304.03722 (2023) - [i10]Boris van Breugel, Zhaozhi Qian, Mihaela van der Schaar:
Synthetic data, real errors: how (not) to publish and use synthetic data. CoRR abs/2305.09235 (2023) - [i9]Yangming Li, Boris van Breugel, Mihaela van der Schaar:
Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models. CoRR abs/2309.14068 (2023) - [i8]Boris van Breugel, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data. CoRR abs/2310.16524 (2023) - [i7]Nabeel Seedat, Nicolas Huynh, Boris van Breugel, Mihaela van der Schaar:
Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in ultra low-data regimes. CoRR abs/2312.12112 (2023) - [i6]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, Ozan Oktay, Maximilian Ilse:
RadEdit: stress-testing biomedical vision models via diffusion image editing. CoRR abs/2312.12865 (2023) - 2022
- [c3]Ahmed M. Alaa, Boris van Breugel, Evgeny S. Saveliev, Mihaela van der Schaar:
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models. ICML 2022: 290-306 - [i5]Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar:
DAUX: a Density-based Approach for Uncertainty eXplanations. CoRR abs/2207.05161 (2022) - [i4]Tennison Liu, Alex J. Chan, Boris van Breugel, Mihaela van der Schaar:
Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes. CoRR abs/2211.06138 (2022) - 2021
- [c2]Daniel de Vassimon Manela, David Errington, Thomas Fisher, Boris van Breugel, Pasquale Minervini:
Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models. EACL 2021: 2232-2242 - [c1]Boris van Breugel, Trent Kyono, Jeroen Berrevoets, Mihaela van der Schaar:
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks. NeurIPS 2021: 22221-22233 - [i3]Daniel de Vassimon Manela, David Errington, Thomas Fisher, Boris van Breugel, Pasquale Minervini:
Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models. CoRR abs/2101.09688 (2021) - [i2]Ahmed M. Alaa, Boris van Breugel, Evgeny Saveliev, Mihaela van der Schaar:
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models. CoRR abs/2102.08921 (2021) - [i1]Boris van Breugel, Trent Kyono, Jeroen Berrevoets, Mihaela van der Schaar:
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks. CoRR abs/2110.12884 (2021)
Coauthor Index
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