Proxy-informed Bayesian transfer learning with unknown sources (2411.03263v3)

Abstract: Generalization outside the scope of one's training data requires leveraging prior knowledge about the effects that transfer, and the effects that don't, between different data sources. Transfer learning is a framework for specifying and refining this knowledge about sets of source (training) and target (prediction) data. A challenging open problem is addressing the empirical phenomenon of negative transfer, whereby the transfer learner performs worse on the target data after taking the source data into account than before. We first introduce a Bayesian perspective on negative transfer, and then a method to address it. The key insight from our formulation is that negative transfer can stem from misspecified prior information about non-transferable causes of the source data. Our proposed method, proxy-informed robust method for probabilistic transfer learning (PROMPT), does not require prior knowledge of the source data (the data sources may be "unknown"). PROMPT is thus applicable when differences between tasks are unobserved, such as in the presence of latent confounders. Moreover, the learner need not have access to observations in the target task (may not have the ability to "fine-tune"), and instead makes use of proxy (indirect) information. Our theoretical results show that the threat of negative transfer does not depend on the informativeness of the proxy information, highlighting the usefulness of PROMPT in cases where only noisy indirect information, such as human feedback, is available.

• The paper demonstrates how PROMPT employs proxy information to enable Bayesian transfer learning without relying on direct target outcome data.
• It leverages a relevance function for likelihood weighting, effectively mitigating negative transfer risks from uncertain source data.
• Empirical tests in synthetic regression and Gaussian process settings show PROMPT outperforms traditional models, underlining its practical impact.

Essay on "Proxy-informed Bayesian transfer learning with unknown sources"

The paper "Proxy-informed Bayesian transfer learning with unknown sources" presents a novel framework named PROMPT for addressing transfer learning scenarios where standard assumptions of fine-tuning in the target task and prior knowledge on source data tasks do not hold. The authors explore the capacity of probabilistic methods, specifically Bayesian inference, to generalize in these settings.

In conventional transfer learning, models are often optimized with the possibility of fine-tuning on a target task, which provides outcome data essential for the model to adapt. However, the authors propose that in many real-world applications, gathering additional outcome information is impractical or undesirable. For instance, a medical intervention that cannot be repeated for ethical or practical reasons. Another dimension the authors consider is the ambiguity surrounding the data’s origin in the source tasks – a notion that better aligns with realistic scenarios where multiple, unobserved confounders create data of uncertain provenance.

PROMPT innovates by leveraging "proxy" information, ancillary data that can guide learning about target-specific parameters without direct outcome data. This auxiliary data can provide insights that mitigate the absence of task-specific data, thereby enhancing prediction without explicit fine-tuning. For example, in a clinical setting, while precise treatment metrics at a new clinic might be unavailable, expert insights on analogous outcomes can function as viable proxies.

The core advancement of PROMPT within Bayesian transfer learning is the introduction of likelihood weighting through a relevance function, optimizing data from unknown sources by mimicking an "intervention" on source observations to better align them with target task characteristics. This involves reweighting source data based on its relevance to the task at hand, ensuring that the predictive estimates are robust against data misspecifications. The relevance function fundamentally minimizes negative transfer risk — a detrimental effect in which knowledge derived from inadequate source data impairs the model’s performance on the target task.

The paper contributes theoretical insights by framing Bayesian transfer learning conditions within an information-theoretic context. The authors utilize the concept of information gain to quantify how much understanding of the target generative processes (shared and task-specific parameters) is improved under the reweighted likelihood strategy compared to a classic, potentially misspecified Bayesian estimate. Empirically, PROMPT’s utility is demonstrated in synthetic linear regression and Gaussian process settings, where it reliably outperforms baseline models, especially under increased risks of negative transfer caused by multicollinearity or parameter misspecification.

An interesting theoretical result is how misspecification in the task parameters leads to adverse outcomes and how PROMPT addresses this by adjusting the data’s apparent source-ness. Such insights lay pathways for further refinement of Bayesian methods under complex real-world conditions with sparse data and non-random missingness.

Future avenues in AI research may include refining the proxy information acquisition methods or developing automated strategies to define the relevance functions, thus expanding PROMPT’s applicability. This could yield highly adaptable AI systems in environments characterized by data uncertainty and limited task-specific information. The practical implications are substantial, ranging from personalized medicine to adaptive machine learning systems in dynamic settings.

In conclusion, the paper advances the field of transfer learning by proposing PROMPT, a framework well-suited for domains where traditional transfer learning assumptions crumble. This contribution enriches Bayesian learning literature, anchoring proxy-aware models in practical issues of confounded data sources and limited fine-tuning feasibility, and opening paths for resilient AI within real-world decision-support systems.