Ondrej Bohdal

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 24

Top Topics

Diffusion Models

Medical Image Analysis

Domain Adaptation

Top Venues

Trans. Mach. Learn. Res.

Publications

Martin Ferianc, Ondrej Bohdal, Timothy M. Hospedales, Miguel R. D. Rodrigues
Navigating Noise: A Study of How Noise Influences Generalisation and Calibration of Neural Networks. Trans. Mach. Learn. Res. 2024 (2024)
Raman Dutt, Ondrej Bohdal, Sotirios A. Tsaftaris, Timothy M. Hospedales
FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis. ICLR (2024)
Ruchika Chavhan, Ondrej Bohdal, Yongshuo Zong, Da Li, Timothy M. Hospedales
Memorized Images in Diffusion Models share a Subspace that can be Located and Deleted. CoRR (2024)
Ondrej Bohdal, Da Li, Shell Xu Hu, Timothy M. Hospedales
Feed-Forward Latent Domain Adaptation. WACV (2024)
Yongshuo Zong, Ondrej Bohdal, Tingyang Yu, Yongxin Yang, Timothy M. Hospedales
Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models. CoRR (2024)
Yongshuo Zong, Ondrej Bohdal, Timothy M. Hospedales
VL-ICL Bench: The Devil in the Details of Benchmarking Multimodal In-Context Learning. CoRR (2024)
Raman Dutt, Pedro Sanchez, Ondrej Bohdal, Sotirios A. Tsaftaris, Timothy M. Hospedales
MemControl: Mitigating Memorization in Medical Diffusion Models via Automated Parameter Selection. CoRR (2024)
Ondrej Bohdal, Timothy M. Hospedales, Philip H. S. Torr, Fazl Barez
Fairness in AI and Its Long-Term Implications on Society. CoRR (2023)
Ondrej Bohdal, Da Li, Timothy M. Hospedales
Label Calibration for Semantic Segmentation Under Domain Shift. CoRR (2023)
Ondrej Bohdal, Yinbing Tian, Yongshuo Zong, Ruchika Chavhan, Da Li, Henry Gouk, Li Guo, Timothy M. Hospedales
Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn. CVPR (2023)
Ondrej Bohdal, Yongxin Yang, Timothy M. Hospedales
Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error. Trans. Mach. Learn. Res. 2023 (2023)
Raman Dutt, Ondrej Bohdal, Sotirios A. Tsaftaris, Timothy M. Hospedales
FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis. CoRR (2023)
Ondrej Bohdal, Da Li, Timothy M. Hospedales
Feed-Forward Source-Free Domain Adaptation via Class Prototypes. CoRR (2023)
Ondrej Bohdal, Yinbing Tian, Yongshuo Zong, Ruchika Chavhan, Da Li, Henry Gouk, Li Guo, Timothy M. Hospedales
Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn. CoRR (2023)
Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau, Giovanni Zappella
PASHA: Efficient HPO and NAS with Progressive Resource Allocation. ICLR (2023)
Martin Ferianc, Ondrej Bohdal, Timothy M. Hospedales, Miguel R. D. Rodrigues
Impact of Noise on Calibration and Generalisation of Neural Networks. CoRR (2023)
Ondrej Bohdal, Da Li, Shell Xu Hu, Timothy M. Hospedales
Feed-Forward Source-Free Latent Domain Adaptation via Cross-Attention. CoRR (2022)
Ondrej Bohdal, Lukas Balles, Beyza Ermis, Cédric Archambeau, Giovanni Zappella
PASHA: Efficient HPO with Progressive Resource Allocation. CoRR (2022)
Rui Li, Ondrej Bohdal, Rajesh K. Mishra, Hyeji Kim, Da Li, Nicholas D. Lane, Timothy M. Hospedales
A Channel Coding Benchmark for Meta-Learning. CoRR (2021)
Rui Li, Ondrej Bohdal, Rajesh K. Mishra, Hyeji Kim, Da Li, Nicholas D. Lane, Timothy M. Hospedales
A Channel Coding Benchmark for Meta-Learning. NeurIPS Datasets and Benchmarks (2021)
Ondrej Bohdal, Yongxin Yang, Timothy M. Hospedales
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization. CoRR (2021)
Ondrej Bohdal, Yongxin Yang, Timothy M. Hospedales
Meta-Calibration: Meta-Learning of Model Calibration Using Differentiable Expected Calibration Error. CoRR (2021)
Ondrej Bohdal, Yongxin Yang, Timothy M. Hospedales
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization. NeurIPS (2021)
Ondrej Bohdal, Yongxin Yang, Timothy M. Hospedales
Flexible Dataset Distillation: Learn Labels Instead of Images. CoRR (2020)