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

• URL: http://arxiv.org/abs/2506.07247v1
• Date: Sun, 08 Jun 2025 18:05:31 GMT
• Title: Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models
• Authors: Ngoc-Quan Pham, Tuan Truong, Quyen Tran, Tan Nguyen, Dinh Phung, Trung Le,
• Abstract summary: Interactive Bayesian Distributional Robustness (IBDR) is a novel Bayesian inference framework that allows modeling the interactions between particles.<n>We evaluate IBDR's performance against various baseline methods using the VTAB-1K benchmark and the common reasoning language task.
• Score: 24.411836907813374
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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