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General Machine Learning · Transfer, Multitask and Meta-learning

Jiajun Lai, Qi Liu, Shijie Li, Huaiguang Jiang

Continual learning requires models to mitigate catastrophic forgetting of prior knowledge while learning a sequence of tasks. Although existing methods based on orthogonal projection prevent interference by constraining parameter updates, they tend to limit plasticity as the task sequence progresses. The reliance on linear approximation further causes the projected gradients to deviate from the nonlinear manifold. To address these issues, we propose Metabolic Optimization for Continual Learning (MoCL), a rehearsal-free framework that strikes a balance between stability and plasticity. To capture the geometric manifold of prior knowledge, MoCL introduces a factorized subspace approximation that avoids expensive explicit matrix inversion. Given the heavy-tailed distribution of the Fisher Information Matrix, we employ a metabolic gating based on Tsallis entropy to suppress updates that conflict with historical knowledge. Theoretical and empirical analyses reveal that MoCL enables the model to converge to a shared low-loss region across sequential tasks. Extensive experimental results across multiple benchmarks demonstrate that MoCL outperforms state-of-the-art methods in both classification performance and efficiency.

General Machine Learning · Transfer, Multitask and Meta-learning

Lotta Mäkinen, Jorge Loria, Samuel Kaski

Meta-learning methods perform well on new within-distribution tasks but often fail when adapting to out-of-distribution target tasks, where transfer from source tasks can induce negative transfer. We propose a causally-aware Bayesian meta-learning method, by conditioning task-specific priors on precomputed latent causal task embeddings, enabling transfer based on mechanistic similarity rather than spurious correlations. Our approach explicitly considers realistic deployment settings where access to target-task data is limited, and adaptation relies on noisy (expert-provided) pairwise judgments of causal similarity between source and target tasks. We provide a theoretical analysis showing that conditioning on causal embeddings controls prior mismatch and mitigates negative transfer under task shift. Empirically, we demonstrate reductions in negative transfer and improved out-of-distribution adaptation in both controlled simulations and a large-scale real-world clinical prediction setting for cross-disease transfer, where causal embeddings align with underlying clinical mechanisms.

General Machine Learning · Transfer, Multitask and Meta-learning

Xingyu Yang, Hanzhang Qu, Ye Cao, Jianfu Cao

Adaptive control of nonlinear systems under unknown disturbances requires learning algorithms aligned with the downstream control objective. While control-oriented meta-learning addresses the mismatch between regression-based identification and tracking performance, existing methods rely on Euclidean or static algebraic geometries that fail to capture the distributional structure of system uncertainties. We propose a framework that lifts adaptation into Wasserstein space, measuring parameter estimation errors as the optimal transport cost between estimated and true system behaviors. By constructing a Wasserstein Bregman divergence over representative task distributions, we use meta-learning to jointly optimize nonlinear feature representations, control gains, and transport geometry. This adaptation law learns an adaptation geometry that captures structural properties of the underlying physical system, implementing a physically grounded, data-driven attention mechanism. Closed-loop tracking simulations demonstrate that our controller achieves optimal performance on both fully-actuated and underactuated nonlinear planar rotorcraft, maintaining robustness under significant distributional shifts between training and testing conditions.

General Machine Learning · Transfer, Multitask and Meta-learning

Wenqi Qiu, Yipeng Zhou, Lin Zhu, Laizhong Cui

Federated continual learning (FCL) of large language models on edge devices is constrained by a communication--stability--plasticity trilemma. We reveal a less-is-more phenomenon: beyond a moderate layer upload ratio, stability loss offsets saturated plasticity gains, so overall continual performance no longer improves. Moreover, layer-wise conflict is heavy-tailed and concentrates in a few layers; denser uplink increasingly includes these layers, which disproportionately drives forgetting and motivates selective sparse communication. Therefore, we introduce RieSelect, which treats stability as staying within a Fisher-metric safe basin around historical solutions. Under this safe-basin constraint, we derive a layer-wise conflict score and a closed-form certified safe step size for finite local updates, and formulate selective uplink as a knapsack-based utility--risk selection, balancing plasticity gains against stability risks. Extensive experiments show that, under a per-round uplink budget, RieSelect achieves the best performance across task orders. Beyond this matched-budget setting, under standard communication protocols, RieSelect improves average accuracy by 18.99–28.14 points while reducing total uplink by 53–115$\times$.

General Machine Learning · Transfer, Multitask and Meta-learning

Guanglong Sun, Hongwei Yan, Liyuan Wang, Zhiqi KANG, Shuang Cui, Hang Su, Jun Zhu, Yi Zhong

To cope with uncertain changes of the external world, intelligent systems must continually learn from complex, evolving environments and respond in real time. This ability, collectively known as general continual learning (GCL), encapsulates practical challenges such as online datastreams and blurry task boundaries. Although leveraging pretrained models (PTMs) has greatly advanced conventional continual learning (CL), these methods remain limited in reconciling the diverse and temporally mixed information along a single pass, resulting in sub-optimal GCL performance. Inspired by meta-plasticity and reconstructive memory in neuroscience, we introduce here an innovative approach named **Me**ta **Po**st-Refinement (MePo) for PTMs-based GCL. This approach constructs pseudo task sequences from pretraining data and develops a bi-level meta-learning paradigm to refine the pretrained backbone, which serves as a prolonged pretraining phase but greatly facilitates rapid adaptation of representation learning to downstream GCL tasks. MePo further initializes a meta covariance matrix as the reference geometry of pretrained representation space, enabling GCL to exploit second-order statistics for robust output alignment. MePo serves as a plug-in strategy that achieves significant performance gains across a variety of GCL benchmarks and pretrained checkpoints in a rehearsal-free manner (e.g., 15.10%, 13.36%, and 12.56% on CIFAR-100, ImageNet-R, and CUB-200 under Sup-21/1K).

General Machine Learning · Transfer, Multitask and Meta-learning

Qi Ma, Chen-Yang Wang, Dehong Gao, Deng-Ping Fan

Prompt learning for vision-language models (VLMs) primarily follows end-to-end or decoupled routes to balance base and new task performance, but suffers a fundamental bottleneck: sample-wise optimization within task-specific feature spaces traps models in local optima, hindering global optimality. To address this, we identify a key insight that VLMs can be prompted within a Coupled Prompt Field-a shared space where base and new tasks are mutually constrained-and present AlignedNorm, which enforces the field coupling. By dynamically aligning the norms of prompts to VLMs' native scale, our method enables joint optimization of both tasks. Without complex designs, our method matches leading decoupled approaches on 15 datasets across 4 experimental settings, offering both a new perspective and a practical solution to the local-optima dilemma in prompt learning.

General Machine Learning · Transfer, Multitask and Meta-learning

Sihan Yang, Kexuan Shi, Weiyang Liu

Merging finetuned Large Language Models (LLMs) has become increasingly important for integrating diverse capabilities into a single unified model. However, prevailing model merging methods rely on linear arithmetic in Euclidean space, which often destroys the intrinsic geometric properties of pretrained weights, such as hyperspherical energy. To address this, we propose Orthogonal Model Merging (OrthoMerge), a method that performs merging operations on the Riemannian manifold formed by the orthogonal group to preserve the geometric structure of the model’s weights. By mapping task-specific orthogonal matrices learned by Orthogonal Finetuning (OFT) to the Lie algebra, OrthoMerge enables a principled yet efficient integration that takes into account both the direction and intensity of adaptations. In addition to directly leveraging orthogonal matrices obtained by OFT, we further extend this approach to general models finetuned with non-OFT methods (\eg, low-rank finetuning, full finetuning) via an Orthogonal-Residual Decoupling strategy. This technique extracts the orthogonal components of expert models by solving the orthogonal Procrustes problem, which are then merged on the manifold of the orthogonal group, while the remaining linear residuals are processed through standard additive merging. Extensive empirical results demonstrate the effectiveness of OrthoMerge in mitigating catastrophic forgetting and maintaining model performance across diverse tasks.

General Machine Learning · Transfer, Multitask and Meta-learning

Chang Liu, Ruotong Zhao, Li Gao, Yupei Zhang

Test-Time Adaptation (TTA) aims to reconcile model generalization in the presence of distribution shifts. Current TTA methods usually leverage sample uncertainty to select reliable samples for model adjustment via entropy minimization (EM). However, sample uncertainty often relies on a plausible metric and leaves many unreliable samples into EM, potentially leading to model collapse. Importantly, these excluded samples incur incomplete data features of the shifted distribution in TTA. This paper introduces SaTeen, a **S**tructural **A**lignment-based **Te**st-Tim**e** Adaptatio**n** (SaTeen) method, by two-fold aligning the structures of test samples with the reliable reference structures. Specifically, the two-fold alignments are 1) Intra-sample structure alignment, where SeTeen maximizes cross-entropy discrepancy between a sample (reference) and its structure-disrupted counterpart, with the assumption of stable dominant features; 2) Inter-sample structure alignment, where SeTeen minimizes the reconstruction error of test samples in the reference subspace spanned by the Incremental PCA (IPCA) on reliable samples, with the assumption of stale intrinsic data manifold. Our extensive experiments demonstrate SaTeen achieves the state-of-the-art performance across various scenarios for both TTA and continual TTA.

General Machine Learning · Transfer, Multitask and Meta-learning

Hanxiao Chen, Debarghya Mukherjee

We study clustered multitask learning in a semiparametric setting where tasks share a latent cluster structure in their target parameters but exhibit heterogeneous, potentially infinite-dimensional nuisance components. Such heterogeneity poses a major challenge for existing multitask learning methods, which typically rely on aligned feature spaces or homogeneous task structures. To address this challenge, we propose an *adaptive fused orthogonal estimator* that integrates Neyman-orthogonal losses with data-driven pairwise fusion penalties. Our framework leverages task-specific pilot estimates to calibrate the fusion penalties and combines adaptive aggregation with orthogonalization to mitigate the impact of nuisance-parameter estimation error. Theoretically, we show that the proposed estimator achieves exact recovery of the latent clustering with high probability and attains pooled parametric convergence rates proportional to cluster size. Moreover, we establish asymptotic normality and show that, asymptotically, our estimator matches the performance of an oracle procedure that knows the true clustering in advance. Empirically, we show that the proposed method consistently outperforms strong baselines in various simulation setups. A real-world application to U.S. residential energy consumption further demonstrates the effectiveness of our approach in uncovering meaningful regional clustering in electricity price elasticity, showcasing the efficacy of our method.

General Machine Learning · Transfer, Multitask and Meta-learning

Rujikorn Charakorn, Edoardo Cetin, Shinnosuke Uesaka, Robert Lange

Long input sequences are central to in-context learning, document understanding, and multi-step reasoning of Large Language Models (LLMs). However, the quadratic attention cost of Transformers makes inference memory-intensive and slow. While context distillation (CD) can transfer information into model parameters, per-prompt distillation is impractical due to training costs and latency. To address these limitations, we propose Doc-to-LoRA (D2L), a lightweight hypernetwork that meta-learns to perform approximate CD within a single forward pass. Given an unseen prompt, D2L generates a LoRA adapter for a target LLM, enabling subsequent queries to be answered without re-consuming the original context, reducing latency and KV-cache memory consumption during inference of the target LLM. On a long-context needle-in-a-haystack task, D2L successfully learns to map contexts into adapters that store the needle information, achieving near-perfect zero-shot accuracy at sequence lengths exceeding the target LLM’s native context window by more than 4x. On real-world QA datasets with limited compute, D2L outperforms standard CD while significantly reducing peak memory consumption and update latency. We envision that D2L can facilitate rapid adaptation of LLMs, opening up the possibility of frequent knowledge updates and personalized chat behavior. Code and checkpoints will be released upon publication.

General Machine Learning · Transfer, Multitask and Meta-learning

Feiyang Deng, Lingfeng Luo, Di Wang, Qinmengge Li, Lingxuan Kong, Kevin He

Accurate prediction in survival analysis with competing risks is challenged by rare event rates and limited effective sample sizes. Knowledge distillation offers a promising way to transfer information from an external teacher to improve a local student, but existing methods are overwhelmingly developed for uncensored outcomes and do not directly extend to survival analysis, where censored observations provide only partial information. Moreover, prior work often assumes that teacher and student share identical outcome definitions, whereas in competing risks settings, they may differ in outcome granularity and event definitions, further complicating knowledge transfer. To address these gaps, we propose DiSKD (Discrete Survival Knowledge Distillation), a deep learning framework for discrete-time competing risks that integrates teacher predictions via a cause-specific, time-dependent Kullback--Leibler divergence. DiSKD enables flexible and privacy-preserving transfer without requiring raw data sharing, remains robust to model misspecification or outcome-definition heterogeneity, and adaptively weights teacher guidance by emphasizing compatible teachers while down-weighting less relevant ones. Simulation studies and real-world applications demonstrate improved discrimination and calibration.

General Machine Learning · Transfer, Multitask and Meta-learning

Joonho Kim, Seyoung Park

We propose a transfer learning framework for estimating high-dimensional Ising models that characterize interactions between discrete binary variables from limited target samples and multiple auxiliary datasets of unknown relevance. Our algorithm, termed Trans-Ising, builds upon a two-stage procedure that first obtains an initial estimator via nodewise $\ell_1$-regularized logistic regression on the target data together with selected auxiliary samples, and then refines it using a target-only correction step with a folded-concave penalty to improve edge selection accuracy. To decide which auxiliary sources to transfer from and reduce the risk of negative transfer, we introduce a loss-based screening rule based on out-of-sample pseudolikelihood evaluation on held-out target data. We also establish asymptotic error bounds and selection consistency for the proposed estimator under standard high-dimensional conditions. Extensive simulations and real data applications show that Trans-Ising consistently outperforms competing methods.

General Machine Learning · Transfer, Multitask and Meta-learning

Jean-Baptiste Fermanian, Batiste Le Bars, Aurélien Bellet

Personalized Federated Learning enables a collection of agents to collaboratively learn individual models without sharing raw data. We propose a new approach in which each agent optimizes a weighted combination of all agents' empirical risks, with the weights learned from data rather than specified a priori. The novelty of our method lies in formulating the estimation of these collaborative weights as a kernel mean embedding estimation problem with multiple data sources, leveraging tools from multi-task averaging to capture statistical relationships between agents. This perspective yields a fully adaptive procedure that requires no prior knowledge of data heterogeneity and automatically transitions between global and local learning regimes. By recasting the objective as a high-dimensional mean estimation problem, we derive finite-sample guarantees on local excess risks for a broad class of distributions, explicitly quantifying the statistical gains of collaboration. To address communication constraints inherent to federated settings, we also propose a practical implementation based on random Fourier features, which allows one to trade communication cost for statistical efficiency. Numerical experiments validate our theoretical results.

General Machine Learning · Transfer, Multitask and Meta-learning

Yebo Wu, Jingguang Li, Zhijiang Guo, Li Li

Federated fine-tuning presents a promising avenue for adapting Large Language Models (LLMs) to downstream tasks while preserving data privacy. However, the prohibitive computational and communication overhead of LLM adaptation inhibits its deployment on resource-constrained edge devices. In this paper, we propose SmartFed, a resource-efficient framework that circumvents expensive training from scratch by intelligently reusing knowledge embedded in existing LoRA modules. To fully exploit this potential and ensure scalability, we introduce the Mixture of Rank-Wise Experts (MoRE). MoRE decomposes LoRA modules into fine-grained rank-level experts, which are selectively activated based on input semantics and resource budgets. Furthermore, to optimize resource utilization, we propose Elastic Expert Quota Allocation (EEQA), a strategy that adaptively distributes expert capacity across parameter matrices based on their contribution to model performance. Extensive evaluations across multiple benchmarks demonstrate that SmartFed significantly outperforms state-of-the-art methods in both model performance and training efficiency.

General Machine Learning · Transfer, Multitask and Meta-learning

Sander de Haan, Yassine Taoudi-Benchekroun, Pau Vilimelis Aceituno, Benjamin F. Grewe

Catastrophic forgetting remains a fundamental challenge for neural networks when tasks are trained sequentially. In this work, we reformulate continual learning as a control problem where learning and preservation signals compete within neural activity dynamics. We convert regularization penalties into preservation signals that protect prior-task representations. Learning then proceeds by minimizing the control effort required to integrate new tasks while competing with the preservation of prior tasks. At equilibrium, the neural activities produce weight updates that implicitly encode the full prior-task curvature, a property we term the *continual-natural gradient*, requiring no explicit curvature storage. Experiments confirm that our learning framework recovers true prior-task curvature and enables task discrimination, outperforming existing methods on standard benchmarks without replay.

General Machine Learning · Transfer, Multitask and Meta-learning

Qingyang Zhu, Eric Oermann, Kyunghyun Cho

Bayesian predictive inference provides a principled framework for uncertainty quantification, data efficiency, and robust generalization. However, exact inference is often intractable, and scalable approximations may remain computationally expensive or require restrictive modeling assumptions that degrade predictive performance. Prior-Data Fitted and in-context learning networks have recently emerged as an amortized alternative by learning to map datasets directly to predictive distributions, but existing approaches are tightly coupled to the support of the training prior and lack explicit mechanisms for adapting to new priors at test time, resulting in limited robustness under distribution shift. We introduce a multi-task in-context learning framework for amortized hierarchical Bayesian predictive inference that explicitly represents prior information as a prefix of in-context datasets. A transformer trained on sequences of prior and target tasks learns to adapt its predictions across families of priors. On a suite of evaluations with increasing difficulty, including out-of-meta-distribution heavy-tailed priors and priors with high-dimensional latent structures, our method matches oracle Bayesian predictors while being orders of magnitude faster.

General Machine Learning · Transfer, Multitask and Meta-learning

Stefano Woerner, Seong Joon Oh, Christian Baumgartner

Current meta-learning methods are constrained to narrow task distributions with fixed feature and label spaces, limiting applicability. Moreover, the current meta-learning literature uses key terms like “universal” and “general-purpose” inconsistently and lacks precise definitions, hindering comparability. We introduce a theoretical framework for meta-learning which formally defines practical universality and introduces a distinction between algorithm-explicit and algorithm-implicit learning, providing a principled vocabulary for reasoning about universal meta-learning methods. Guided by this framework, we present TAIL, a transformer-based algorithm-implicit meta-learner that functions across tasks with varying domains, modalities, and label configurations. TAIL features three innovations over prior transformer-based meta-learners: random projections for cross-modal feature encoding, random injection label embeddings that extrapolate to larger label spaces, and efficient inline query processing. TAIL achieves state-of-the-art performance on standard few-shot benchmarks while generalizing to unseen domains. Unlike other meta-learning methods, it also generalizes to unseen modalities, solving text classification tasks despite training exclusively on images, handles tasks with up to 20× more classes than seen during training, and provides orders-of-magnitude computational savings over prior transformer-based approaches.

General Machine Learning · Transfer, Multitask and Meta-learning

Yuchao Zhang, Eric Wang, Fan Zhang, Haoxuan Li, Yisen Wang, Zhouchen Lin, Jun Wang, QIRUI MI, Mengyue Yang

Memory-based test-time adaptation (TTA) assigns streaming test samples into class-specific memory slots based on pseudo-labels predicted by models like CLIP, and retrieves them to facilitate subsequent predictions under distribution shift. However, this process introduces two challenges: ❶ **Each sample is hard-assigned to a single class based on CLIP's prediction**, where inaccurate CLIP prediction leads to memory contamination that biases subsequent prediction. ❷ **Samples are evicted under biased selection due to fixed memory capacity**, which risks discarding informative samples and undermining the efficacy of the memory. To address these challenges, we propose **A$^{2}$Memory** (**A**ttributive-**A**ssociative **Memory** for Test-time Adaptation). For challenge ❶ , we propose *Attribute-centric Memory Construction* that builds prior textual representations from class-shared representative and diverse visual attributes, and applies soft assignment to generate surrogate visual representations. For challenge ❷, we design *Class-wise Associative Memory* that dynamically compresses streaming samples into fixed-capacity memory through gradient-based optimization and data-dependent retention, then retrieves sample-adaptive class prototypes for reliable inference. Extensive experiments demonstrate consistent improvements over state-of-the-art methods across 15 benchmarks.

General Machine Learning · Transfer, Multitask and Meta-learning

HuiYu Yi, Xu Zhiming, Dunwei Tu, Zhicheng Wang, Baile Xu, Furao Shen

The Nearest Class Mean (NCM) classifier is widely favored in Class-Incremental Learning (CIL) for its superior resistance to catastrophic forgetting compared to Fully Connected layers. While Neural Collapse (NC) theory supports NCM's optimality by assuming features collapse into single points, non-linear feature drift and insufficient training in CIL often prevent this ideal state. Consequently, classes manifest as complex manifolds rather than collapsed points, rendering the single-point NCM suboptimal. To address this, we propose Hierarchical-Cluster SOINN (HC-SOINN), a novel classifier that captures the topological structure of these manifolds via a ``local-to-global'' representation. Furthermore, we introduce Structure-Topology Alignment via Residuals (STAR) method, which employs a fine-grained pointwise trajectory tracking mechanism to actively deform the learned topology, allowing it to adapt precisely to complex non-linear feature drift. Theoretical analysis and Procrustes distance experiments validate our framework's resilience to manifold deformations. We integrated HC-SOINN into seven state-of-the-art methods by replacing their original classifiers, achieving consistent improvements that highlight the effectiveness and robustness of our approach. Code is available at \url{https://anonymous.4open.science/r/icml2026-9B60}.

General Machine Learning · Transfer, Multitask and Meta-learning

Vincent Herrmann, Jürgen Schmidhuber

This position paper argues that truly open-ended intelligence is bottlenecked by the challenge of *interestingness*: the ability to prospectively identify which tasks or data hold the potential for future progress. We formalize interestingness as an inductive heuristic for future compression progress and investigate its predictability using tools from Kolmogorov Complexity and Algorithmic Statistics. By analyzing complexity-runtime profiles under various priors over computable objects, we demonstrate that the *inductive property of interestingness*—the capacity for past compression progress to signal future discovery—is theoretically viable. However, we show that this property is highly sensitive to the underlying distribution of objects. We conclude by calling for a move beyond human-in-the-loop filtering or data creation, and a shift toward *introspective* models that can explicitly assess their own potential for insight. Furthermore, we advocate the engineering of scale-free synthetic environments, providing a principled roadmap for the development of truly autonomous open-ended systems.