AutoTransfer: Automated Knowledge Transfer in ML

• AutoTransfer is a family of frameworks that automates the transfer of knowledge across tasks, models, or domains using data-driven methods.
• It employs techniques such as automated routing, task similarity embeddings, and adaptive sample or policy transfer to improve performance and efficiency.
• Empirical results demonstrate significant improvements in test accuracy, convergence speed, and robustness across diverse datasets and applications.

AutoTransfer encompasses a family of frameworks and algorithms that automate knowledge transfer across tasks, models, or domains in machine learning. AutoTransfer methods are defined by their ability to select, adapt, or combine representations, architectures, or data from source domains to accelerate or improve learning in a target domain, under principled, often data-driven, selection or optimization schemes. Approaches under the AutoTransfer nomenclature have been developed for supervised learning, reinforcement learning, AutoML, transferability estimation, subject-invariant biosignal modeling, and deep neural architecture routing, cutting across both theoretical and empirical research.

1. Principles and Canonical Formulations

Central to AutoTransfer methodologies is the automatic—not manual—determination of what, when, and how to transfer knowledge from one or more source tasks to a target task. This is achieved through explicit similarity estimation, dynamic weighting or routing, data-centric distribution matching, or meta-learned initialization strategies.

Four representative paradigms include:

• Transfer via Automated Routing: Selection of optimal pathways for feature flow or architectural elements from source to target (e.g., adversarial bandit-based routing in deep nets (Murugesan et al., 2022)).
• Adaptive Sample or Policy Transfer in RL: Dynamic selection and weighting of source samples or policies, guided by Bellman-operator divergence or option-value learning (Lazaric et al., 2011, Yang et al., 2020).
• AutoML with Knowledge Transfer: Automatic computation of task similarity and design priors utilizing large-scale task–model banks and task embeddings, enabling efficient architecture or hyperparameter search (Cao et al., 2023, Wei et al., 2019).
• Regularized Subject Transfer Learning: Modular frameworks to select among regularization strategies promoting invariance (e.g., mutual information, MMD, adversarial censorship) with automated hyperparameter and method selection over a penalized risk (Smedemark-Margulies et al., 2021).

2. Core Methodologies

2.1 Task Similarity and Embedding

Task similarity assessment underpins transfer effectiveness. In AutoTransfer for GNN AutoML (Cao et al., 2023), a task embedding $\phi: t \mapsto e_t \in \mathbb{R}^d$ is constructed by extracting Fisher Information Matrix (FIM)–based statistics from multiple anchor architectures, concatenated, and projected via a learned multilayer perceptron, with cosines in embedding space reflecting the similarity of optimal architectures. This embedding is trained with a triplet ranking loss using Kendall correlation scores as supervision.

2.2 Automated Routing and Selection

The Auto-Transfer framework (Murugesan et al., 2022) utilizes an adversarial multi-armed bandit (AMAB) to automatically select, for each target network layer, a source layer and aggregation operator (add, weighted-add, linear-comb, etc.). The controller is updated online using EXP3.P bandit updates, maximizing validation-set reward gains from proposed routing decisions.

2.3 Distribution and Policy Matching

In reinforcement learning, AutoTransfer-adapted frameworks measure source-target similarity via Bellman-operator divergence. The Best-Average Transfer (BAT) algorithm (Lazaric et al., 2011) computes a convex mixture of source MDPs to minimize this divergence, dynamically optimizing sample usage at each fitted Q-iteration. Similarly, option-based adaptive policy transfer (Yang et al., 2020) leverages learned option-termination policies and option-value networks for dynamic switching and imitation from multiple source policies.

2.4 Automated Regularization and Hyperparameter Search

AutoTransfer for biosignals (Smedemark-Margulies et al., 2021) specifies multiple regularization objectives enforcing independence (e.g., $I(Z;S)$, $I(Z;S|Y)$, or complementary penalization between latent subspaces and nuisances), and employs a cross-validated, hands-off method to select among them and tune regularization coefficients.

3. Practical Algorithms and Implementation

3.1 GNN AutoML via Prior Mixtures

• Task-model bank construction: For $N$ tasks, paired with $n_i$ architectures each, collect performance $P(t^{(i)},a_j^{(i)})$ as a bank.
• Task embedding computation: For new task $t^*$, compute its embedding $e_{t^*}$.
• Top-K neighbor selection: Compute similarity $s(t^*,t^{(i)})$ (cosine similarity); select top $K$ neighbors.
• Design prior formation: Aggregate a similarity-weighted mixture of softmaxed design distributions from the neighbors:

$$\pi_{t^*}(a) = \frac{1}{Z} \sum_{i \in \mathcal{N}_K(t^*)} s(t^*, t^{(i)}) \pi_{t^{(i)}}(a)$$

• AutoML search initialization: Use $\pi_{t^*}$ as an informed prior for random search, TPE, or evolutionary search (Cao et al., 2023).

3.2 AMAB-based Routing

• Forward hooks: Extract intermediate representations from source and target at all layers.
• Routing decision: At each target layer, the AMAB chooses a source layer (or NULL) and an aggregation method.
• Validation reward: Selection evaluated via difference in validation loss ($r_t$) between unaugmented and routed networks.
• Bandit update: Update controller with epoch-wise reward in an EXP3.P scheme (Murugesan et al., 2022).

3.3 RL Sample Transfer

• Bellman divergence computation: Use auxiliary target-state samples and simulated rollouts to estimate convex mixtures of source Bellman operators that best approximate the target Bellman image at each FQI iteration.
• BAT/BTT adaptive mixture selection: Minimize trade-off between estimation error (more samples) and transfer error (task mismatch).
• Empirical regime: BTT increasingly favors target data as available, conservatively using only those sources that minimize propagated error (Lazaric et al., 2011).

3.4 Automated Censoring Regularization

• Penalty candidates: Adversarial classification (cross-entropy), MIGE, MMD, BEGAN, pairwise MMD.
• Hyperparameter selection: Cross-validation on held-out subjects; selection criteria based on robust quantiles of balanced accuracy.
• Encoder and classifier: EEGNet-style encoder (temporal and spatial convolutions), softmax classifier.
• Training protocol: Alternate adversarial/critic and encoder/classifier updates; joint minimization of task loss and independence-promotion penalty (Smedemark-Margulies et al., 2021).

4. Theoretical Guarantees and Empirical Performance

Theoretical analysis of sample transfer in RL yields finite-sample bounds on propagation error, with transfer error strictly controlled by Bellman-operator divergence and mixture selection accuracy (Lazaric et al., 2011). In the GNN AutoTransfer regime, FIM-based task embedding is empirically shown to preserve oracle Kendall correlations between tasks and best architectures, and the similarity-weighted design prior yields test accuracy speedups of nearly an order of magnitude in trial count compared to standard AutoML baselines (Cao et al., 2023). In neural process–based HPO (meta-learned surrogates), transfer across datasets results in converging to near-optimal configurations in an order of magnitude fewer trials than traditional SMBO approaches (Wei et al., 2019).

In the AMAB-based routing framework (Murugesan et al., 2022), performance surpasses previous feature-matching and attention-transfer baselines by 5–15 percentage points on several public datasets, especially in small-data regimes, and qualitative analysis shows interpretable re-weighting of salient task-relevant features.

In subject transfer learning, AutoTransfer’s hyperparameter and method selection almost always matches or nearly matches the best regularizer in cross-validation, and significantly improves the worst-case subject accuracy (Smedemark-Margulies et al., 2021).

5. Limitations, Extensions, and Best Practices

AutoTransfer frameworks rely critically on the quality of source–target similarity estimation. For target tasks far from the existing bank (e.g., in GNN AutoML), the transferred prior can be uninformative, requiring expansion of the task-model bank or fallback to uniform baselines (Cao et al., 2023). Bandit-based routers have $\mathcal O((N+1)M|\mathcal M|)$ action space growth, which can become computationally challenging for deep architectures. Cross-task evaluations highlight that no single censoring method dominates universally in subject transfer; hence, robust selection is necessary (Smedemark-Margulies et al., 2021).

Best practices include periodic retraining or updating of task embeddings and design priors, continual expansion of source knowledge banks, and routine monitoring for out-of-distribution similarity evaluations.

6. Representative Applications and Datasets

AutoTransfer methodologies have demonstrated empirical advances across domains:

• Graph Neural Network AutoML: GNN-Bank-101 with 120,000 task-architecture pairs; benchmarks on node- and graph-classification datasets (e.g., AmazonComputers, PROTEINS, OGB-Arxiv) (Cao et al., 2023).
• Computer Vision Transfer Routing: CUB200, Stanford Dogs, MIT67, Stanford40, CIFAR100, STL-10, with ImageNet/TinyImageNet as sources (Murugesan et al., 2022).
• RL Transfer: Synthetic chain-walks, MuJoCo Reacher, gridworlds, pinball maze (Lazaric et al., 2011).
• EEG/EMG/ECoG Subject Transfer: EEG-ErrP, EEG-RSVP, EMG-ASL, ECoG-Faces (Smedemark-Margulies et al., 2021).
• Meta-HPO: 100 OpenML datasets, CIFAR-10, MNIST, SVHN (Wei et al., 2019).

7. Connections to Transferability Estimation and Emerging Directions

AutoTransfer principles intersect with fast, theoretically justified model selection via transferability estimation. Example: TransRate efficiently computes the mutual information–based transferability of model-layer pairs on a new task through coding-rate log-determinant operations and integrates into AutoTransfer ranking and selection loops (Huang et al., 2021). Possible future extensions involve richer task descriptors (spectral graph signatures, global network properties), automated anchor selection, joint source–target optimization with differentiable routing controllers, and application to modalities such as text and biological signals.