Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models

Ngoc-Quan Pham, Tuan Truong, Quyen Tran, Tan Minh Nguyen, Dinh Phung, Trung Le
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:49259-49276, 2025.

Abstract

We introduce Interactive Bayesian Distributional Robustness (IBDR), a novel Bayesian inference framework that allows modeling the interactions between particles, thereby enhancing ensemble quality through increased particle diversity. IBDR is grounded in a generalized theoretical framework that connects the distributional population loss with the approximate posterior, motivating a practical dual optimization procedure that enforces distributional robustness while fostering particle diversity. We evaluate IBDR’s performance against various baseline methods using the VTAB-1K benchmark and the common reasoning language task. The results consistently show that IBDR outperforms these baselines, underscoring its effectiveness in real-world applications.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-pham25b, title = {Promoting Ensemble Diversity with Interactive {B}ayesian Distributional Robustness for Fine-tuning Foundation Models}, author = {Pham, Ngoc-Quan and Truong, Tuan and Tran, Quyen and Nguyen, Tan Minh and Phung, Dinh and Le, Trung}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {49259--49276}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/pham25b/pham25b.pdf}, url = {https://proceedings.mlr.press/v267/pham25b.html}, abstract = {We introduce Interactive Bayesian Distributional Robustness (IBDR), a novel Bayesian inference framework that allows modeling the interactions between particles, thereby enhancing ensemble quality through increased particle diversity. IBDR is grounded in a generalized theoretical framework that connects the distributional population loss with the approximate posterior, motivating a practical dual optimization procedure that enforces distributional robustness while fostering particle diversity. We evaluate IBDR’s performance against various baseline methods using the VTAB-1K benchmark and the common reasoning language task. The results consistently show that IBDR outperforms these baselines, underscoring its effectiveness in real-world applications.} }
Endnote
%0 Conference Paper %T Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models %A Ngoc-Quan Pham %A Tuan Truong %A Quyen Tran %A Tan Minh Nguyen %A Dinh Phung %A Trung Le %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-pham25b %I PMLR %P 49259--49276 %U https://proceedings.mlr.press/v267/pham25b.html %V 267 %X We introduce Interactive Bayesian Distributional Robustness (IBDR), a novel Bayesian inference framework that allows modeling the interactions between particles, thereby enhancing ensemble quality through increased particle diversity. IBDR is grounded in a generalized theoretical framework that connects the distributional population loss with the approximate posterior, motivating a practical dual optimization procedure that enforces distributional robustness while fostering particle diversity. We evaluate IBDR’s performance against various baseline methods using the VTAB-1K benchmark and the common reasoning language task. The results consistently show that IBDR outperforms these baselines, underscoring its effectiveness in real-world applications.
APA
Pham, N., Truong, T., Tran, Q., Nguyen, T.M., Phung, D. & Le, T.. (2025). Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:49259-49276 Available from https://proceedings.mlr.press/v267/pham25b.html.

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