Optimizable Model Averaging for Ensembling Heterogeneous Model Outputs

summary

The gist

The gist The authors revisit classical Bayesian model averaging (BMA) to ensemble pre-trained and/or lightly-finetuned foundation models by introducing trainable linear classifiers and an optimizable

In short

The authors propose Optimizable Model Averaging (OMA) to improve classification performance by learning non-negative weights for heterogeneous foundation models. OMA minimizes regularized predictive entropy, which controls model adaptation. Experiments show OMA outperforms simple output averaging and single-model predictions on image and text classification tasks, demonstrating better performance-calibration trade-offs.

Key concepts

Bayesian Model Averaging (BMA): BMA is a method to combine the predictions of multiple models by weighting them based on their posterior probabilities. In this work, it uses trainable linear classifiers on frozen foundation model features to determine which model heads and features are best for a specific dataset, providing a principled way to ensemble diverse models.
Optimizable Model Averaging (OMA): OMA is a black-box approach that learns scalar weights for each candidate model. It optimizes an objective function that minimizes the expected entropy of predictions under the ensemble. This allows the system to dynamically adjust model contributions based on data, improving performance beyond simple output averaging.
Predictive Entropy Minimization: This is the core objective function for OMA. Minimizing predictive entropy aims to find an ensemble where the combined prediction distribution is as concentrated as possible around the true label. The authors note that this regularization helps control adaptation and improves calibration compared to methods that only focus on raw accuracy.
Regularization ($\lambda$): Regularization is used in OMA to control how much the ensemble adapts to the specific dataset. It is added as a penalty term during optimization, often using prior beliefs from zero-shot or MAP models. Tuning this parameter helps balance model diversity with data-specific performance, leading to better calibration results.

Terminology used across episodes

Reinforcement Learning (RL) — explained in 25 episodes
Federated Learning — explained in 17 episodes
Retrieval-Augmented Generation (RAG) — explained in 16 episodes
Interpretability — explained in 15 episodes
Quantum Fisher Information (QFI) — explained in 15 episodes
Sample Complexity — explained in 15 episodes

This episode discusses

The paper

Optimizable Model Averaging for Ensembling Heterogeneous Model Outputs · Read on arXiv

Department of Computer Science, University of British Columbia

Foundation model ensembles can improve accuracy and robustness, but full fine-tuning and parameter-space merging are often expensive or impossible when candidate models have different architectures. We propose Optimizable Model Averaging (OMA), a black-box approach that learns nonnegative model-combination weights, one scalar coefficient per model, to average the candidate models' predictive probabilities, leaving all model parameters frozen, by minimizing regularized predictive entropy. A central challenge is that minimizing predictive entropy can favour confidently incorrect models: increased confidence need not imply improved accuracy or calibration. We therefore examine regularization as a mechanism for controlling adaptation and empirically evaluate its effect on calibration, observing lower expected calibration error in selected configurations, alongside better performance-calibration trade-offs than model output averaging. As foundation models continue to advance, these approaches will enable the incorporation of future, potentially much better models to improve performance on challenging text and image classification tasks.

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "Optimizable Model Averaging for Ensembling Heterogeneous Model Outputs".

Jane: The gist The authors revisit classical Bayesian model averaging (BMA) to ensemble pre-trained and/or lightly-finetuned foundation models by introducing trainable linear classifiers and an optimizable model averaging scheme (OMA) to…

Tom: First, who's behind it and why it matters.

Paper summary: Jane: We’ve covered the main points of "Optimizable Model Averaging for Ensembling Heterogeneous Model Outputs," focusing on how it uses trainable linear classifiers and the OMA scheme to combine different foundation models intelligently.

Tom: The authors, Mijung Park and their team, essentially propose a principled way to ensemble pre-trained and lightly fine-tuned foundation models by learning model combination weights directly through optimization.

Lu: What this means in simpler terms is that instead of guessing which model is best, the system learns the optimal way to weight them based on what each model thinks about the data.

Meng: It's a structured approach to combining complex AI systems, which could make building robust classification tools much more systematic for developers.

Lalam: For us in culture and information systems, this points toward developing AI that is more reliable because it’s explicitly managing its uncertainty across different model outputs.

Jane: The title of the paper, "Optimizable Model Averaging for Ensembling Heterogeneous Model Outputs," really captures the essence—it's about making the averaging process itself optimizable and tailored to how different models are behaving.

Tom: So, if you’re listening and you’re wondering what this actually changes for classification tasks, it suggests a more efficient way to leverage a diverse set of AI models without needing massive retraining efforts for every combination.

Jane: It moves the conversation from just 'which model is best' to 'how do we optimally combine these different models given their individual uncertainties.'

Tom: That’s the big picture here. We’ll keep an eye on how this OMA approach scales when we start talking about even more complex, heterogeneous AI architectures down the road.

Lu: And honestly, it opens up a lot of avenues for exploring model diversity in ways that were previously too computationally demanding to explore fully.

Meng: From an engineering side, the focus on optimizing weights instead of retraining everything seems like a much more viable path forward for integrating these massive models into real applications.

Conclusion: Tom: So we’re wrapping up this look at Optimizable Model Averaging for Ensembling Heterogeneous Model Outputs by Mijung Park and their team, and what this whole setup actually means for how we use these big foundation models.

Jane: It boils down to them figuring out a way to combine different AI models—models that have different designs—without having to retrain everything from scratch every time they want something better.

Lu: They introduce this specific method, OMA, where you learn these weights that tell you how much influence each model should have on the final prediction, keeping all the actual model parameters frozen.

Meng: So it’s about using a clever mathematical trick to average out these different predictions in a way that’s computationally lighter than fine-tuning fifty models at once.

Lalam: And for us, this means we can get more reliable results from these complex systems because we're not just picking the one that looked best on a test set, but learning the right balance across all of them.

Tom: Exactly, it’s about making sure that when you use a diverse set of models, you actually get a better result than just picking the single most confident one.

Jane: The authors are showing that this optimization process works well for both images and text classification tasks, which is pretty broad.

Lu: They found that this approach improves performance on certain datasets significantly over simple output averaging, even when using models trained in different ways.

Meng: I’m interested in the practical side here—how much time does setting up this optimization take compared to just running a single model?

Lalam: It seems like it trades a little more setup work for a much more robust and adaptable final AI system.

Tom: Right, so they’re not just finding a static combination; they’re finding an adjustable one that adapts based on the data you feed it.

Jane: And the main thing is that this method lets us keep using those huge pre-trained foundation models without needing to do all that heavy lifting on them again.

Lu: It opens up possibilities for building even more complex AI pipelines where you might have a hundred different specialized models, and OMA could manage them all efficiently.

Meng: That’s interesting because it moves the challenge from training massive new things to learning smarter ways to use what we already have available.

Lalam: It really shows how improving the way we combine existing models can lead to a more versatile and trustworthy AI culture for everyone using these tools.

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