Meta-learning adapts models for unseen tasks across AI, robotics, and NLP.
problem Adapting models to unseen tasks efficiently and accurately.
method Black-box, metric-based, layered, and Bayesian approaches.
result Meta-learning enhances model generalization and adaptation to unseen tasks.
A concise review of recent few-shot meta-learning methods.
problem Mimicking human fast adaptation to new concepts based on prior knowledge.
method Categorized into four branches based on technical characteristics.
result Current challenges and future prospects identified.
A new meta-learning framework that assigns weights to source tasks based on target samples.
problem Learning initialization for target tasks with limited labeled examples.
method A general framework that assigns weights to the loss of different source tasks, which can depend on the target samples. Provides upper bounds and develops a learning algorithm based on minimizing the error bound with respect to an empirical IPM.
result Empirically, the weighted meta-learning algorithm finds better initializations than uniformly-weighted meta-learning algorithms.
Meta-learning algorithms produce feature extractors which achieve state-of-the-art performance on few-shot classification. While the literature is rich with meta-learning methods, little is known about why the resulting feature extractors perform so well. We develop a better understanding of the underlying mechanics of…
Meta-learning balances task-specific modeling and optimization complexity.
problem Balancing accurate task-specific modeling with ease of optimization in meta-learning.
method Theoretical and empirical analysis of trade-off between modeling and optimization in meta-learning.
result Explicit bounds on modeling and optimization errors for non-convex and linear regression problems.
Meta-learning bounds derived using PAC-Bayes theory for improved generalization.
problem Uncertainty in generalization performance for meta-learning with new tasks.
method PAC-Bayes relative entropy bounds and empirical risk minimization (ERM) method.
result Competitive generalization performance and rapid convergence with data-dependent prior.
Meta-learning improves neural networks by adapting learning algorithms.
problem Conventional AI approaches solve tasks from scratch, but meta-learning aims to improve the learning algorithm.
method Meta-learning adapts a learning algorithm based on multiple learning episodes.
result Meta-learning can tackle deep learning challenges like data and computation bottlenecks.
Meta learning works well with overparameterized models, a phenomenon called 'benign overfitting'.
problem Understanding why overparameterized models perform well in few-shot learning.
method Analyzed the generalization performance of gradient-based meta learning with an overparameterized meta linear regression model.
result Demonstrated that overparameterized meta learning can still generalize well, a phenomenon called 'benign overfitting'.
Meta-learning helps models learn quickly from few samples.
problem Deep learning requires many samples, which are hard to get.
method Meta-learning optimizes models to adapt quickly to new tasks.
result Meta-learning can improve model efficiency and adaptability.
When fitting Bayesian machine learning models on scarce data, the main challenge is to obtain suitable prior knowledge and encode it into the model. Recent advances in meta-learning offer powerful methods for extracting such prior knowledge from data acquired in related tasks. When it comes to meta-learning in Gaussian…
Meta learning can adapt fast but is vulnerable to adversarial attacks.
problem Vulnerability of meta learning to adversarial attacks.
method Formal definition of adversarial attacks unique to meta learning, proposing an attacking algorithm.
result Meta learning is vulnerable to adversarial attacks.
The goal of optimization-based meta-learning is to find a single initialization shared across a distribution of tasks to speed up the process of learning new tasks. Conditional meta-learning seeks task-specific initialization to better capture complex task distributions and improve performance. However, many existing c…
Modern meta-learning approaches for image classification rely on increasingly deep networks to achieve state-of-the-art performance, making batch normalization an essential component of meta-learning pipelines. However, the hierarchical nature of the meta-learning setting presents several challenges that can render con…
BOML unifies meta-learning methods into a common bilevel optimization framework.
problem Meta-learning methods with diverse modeling aspects.
method Modularized bilevel optimization library in Python.
result Unified solution for various meta-learning formulations.
learn2learn simplifies meta-learning research by providing a library and standardized interfaces.
problem Prototyping and reproducibility issues in meta-learning.
method Developed a library (learn2learn) with common routines and standardized interfaces.
result Fosters a community around standardized software for meta-learning research.
MARS meta-learns function scores for improved predictive accuracy and uncertainty.
problem Difficulty in specifying expressive priors for Bayesian meta-learning.
method Meta-learning the score function of data-generating process marginals in the function space.
result State-of-the-art predictive accuracy and improved uncertainty estimates.
New PAC-Bayesian framework for flexible meta-learning.
problem Limitations in transferring knowledge between tasks in meta-learning.
method PAC-Bayesian theory applied to learning the learning algorithm.
result Flexibility in meta-learning mechanisms and improved prediction quality.
Meta-learning algorithm improves AI efficiency by teaching itself.
problem Overcoming meta-optimization challenges in AI learning.
method Bootstraps a target from the meta-learner and optimizes the meta-learner by minimizing distance under a chosen metric.
result Achieves state-of-the-art performance on Atari ALE benchmark and demonstrates efficiency gains in multi-task learning.
Self-referential meta learning avoids explicit optimization by modifying itself.
problem Dependency on human engineering in meta learning algorithms.
method Investigates self-referential meta learning systems that modify themselves without explicit optimization.
result Self-referential neural networks can improve their own modifications without explicit optimization.
VMGP extends Gaussian processes for Bayesian meta-learning, improving uncertainty prediction.
problem Bayesian meta-learning for few-shot tasks with non-Gaussian uncertainty.
method VMGP (Variational Meta-Gaussian Processes) extends Gaussian processes to model non-Gaussian predictive posteriors.
result VMGP significantly outperforms existing Bayesian meta-learning methods on complex tasks.
Meta-learning performance improved by keeping support sets fixed.
problem Effect of support set diversity on meta-learning performance.
method Fixed support sets across tasks in meta-learning.
result Not only does not reduce performance, but almost always improves it.
VSML unifies meta learning concepts and enables simple backpropagation.
problem Improving and unifying meta learning concepts for neural networks.
method Unified approach using variable shared meta learning and simple weight-sharing.
result Simple backpropagation can be implemented and meta learned without gradient calculation.
Bayesian MAML outperforms MAML in meta learning tasks with theoretical guarantees.
problem Theoretical understanding of Bayesian MAML's superiority over MAML.
method Comparison of meta test risks between Bayesian MAML and MAML in meta linear regression.
result Bayesian MAML has provably lower meta test risks than MAML in both distribution agnostic and linear centroid cases.
Conditional meta-learning improves meta-learning performance in diverse task environments.
problem Meta-learning struggles with tasks that have heterogeneous complexity.
method Conditional meta-learning infers a task-specific meta-parameter vector.
result Conditional meta-learning outperforms standard meta-learning in diverse task environments.
Meta-learning framework uses task similarity through nonparametric kernel regression.
problem Limited tasks and outliers/dissimilar tasks hinder meta-learning performance.
method Nonparametric kernel regression to quantify and use task similarity.
result Meta-learning algorithm outperforms existing methods in task-limited settings.
Meta-learning improves OoD detection with minimal in-distribution data.
problem Efficient OoD detection with limited in-distribution data.
method Meta-learning in latent space with Gaussian mixture models.
result Meta-learning enhances OoD detection performance.
Meta-learning shows negative transfer between tasks, which MetaCRL addresses.
problem Negative transfer between tasks in meta-learning.
method Structural Causal Models (SCMs) and MetaCRL to eliminate task confounders.
result MetaCRL achieves state-of-the-art performance in various benchmark datasets.
EvoGrad improves efficiency in meta-learning and hyperparameter optimization.
problem Efficiently compute hypergradients for larger network architectures.
method Uses evolutionary techniques to estimate hypergradients without second-order derivatives or longer computational graphs.
result Significant improvements in efficiency, enabling scaling to bigger architectures.
Proposes a method to evaluate meta-learning performance based on task similarity.
problem Meta-learning performance evaluation ignores task similarity, leading to biased results.
method Generative approach using Latent Dirichlet Allocation to analyze task similarity.
result The proposed method provides an insightful evaluation of meta-learning algorithms, matching common intuition.
Surveying deep meta-learning to improve quick concept learning.
problem Limitation of deep neural networks in learning new concepts quickly.
method Categorizes deep meta-learning techniques into metric, model, and optimization-based approaches.
result Unified overview of current deep meta-learning techniques.
Recent years have witnessed an abundance of new publications and approaches on meta-learning. This community-wide enthusiasm has sparked great insights but has also created a plethora of seemingly different frameworks, which can be hard to compare and evaluate. In this paper, we aim to provide a principled, unifying fr…
Meta-learning performance is affected by how task diversity is allocated, not just overall variability.
problem Meta-learning performance degrades when task diversity is unevenly distributed.
method Decomposed task-specific regression effects into structurally informative and orthogonal components.
result Meta-learning prediction degrades when a larger fraction of task variability is orthogonal and non-informative.
Meta-learning improves Gaussian process uncertainty estimation.
problem Poor uncertainty estimation in Gaussian processes with deep kernels.
method Meta-learning to calibrate deep kernel GPs using task-specific uncalibrated and calibrated distributions.
result Improves uncertainty estimation performance with high regression performance.
Meta-learning algorithms are studied for their data efficiency in personalized federated learning.
problem Data efficiency in meta-learning algorithms.
method Algorithmic stability techniques to derive bounds on transfer risk and a new evaluation framework.
result Different meta-learning methods require varying amounts of supervision for effective generalization.
ST-MAML tackles task ambiguity in meta-learning by encoding tasks with stochastic representations.
problem Handling tasks from multiple distributions is challenging for meta-learning due to task ambiguity.
method ST-MAML uses a stochastic neural network module to encode tasks and propagate task representations to revise input variable encoding.
result ST-MAML matches or outperforms state-of-the-art methods on various tasks.
This paper improves meta-learning by developing new PAC-Bayes bounds.
problem Meta-learning generalization gap across multiple tasks.
method Upper bounding convex functions linking environment and task-level losses.
result New PAC-Bayes bounds for meta-learning with improved algorithms.
F-PACOH improves meta-learners' reliability in uncertain regions.
problem Overconfident uncertainty estimates in meta-learning.
method Meta-learning priors as stochastic processes in function space, directly steering predictions towards high epistemic uncertainty.
result Significantly outperforms other meta-learners in Bayesian Optimization.
Meta-learning enables a model to learn from very limited data to undertake a new task. In this paper, we study the general meta-learning with adversarial samples. We present a meta-learning algorithm, ADML (ADversarial Meta-Learner), which leverages clean and adversarial samples to optimize the initialization of a lear…
One popular trend in meta-learning is to learn from many training tasks a common initialization for a gradient-based method that can be used to solve a new task with few samples. The theory of meta-learning is still in its early stages, with several recent learning-theoretic analyses of methods such as Reptile [Nichol …
New analysis improves generalization bounds for meta-learning.
problem Improving generalization in meta-learning algorithms.
method Information-theoretic analysis of MAML and its stochastic variant.
result Data-dependent generalization bound is tighter and non-vacuous.
Meta-learning is a promising strategy for learning to efficiently learn within new tasks, using data gathered from a distribution of tasks. However, the meta-learning literature thus far has focused on the task segmented setting, where at train-time, offline data is assumed to be split according to the underlying task,…
Unified framework improves meta-learning generalization bounds.
problem Limited sharpness of existing meta-generalization bounds.
method Unified information-theoretic derivation for single-step bounds.
result Unified bounds exhibit tighter scaling and computational advantages.
This paper reduces labeling costs for meta-learning in wireless systems.
problem Expensive labeling in meta-learning for wireless systems.
method Bayesian active task selection mechanism.
result Reduces the number of labeling steps required for meta-learning.
A new meta-learning BO approach that bypasses surrogate models and directly learns task utility.
problem Scalability issues and sensitivity to task similarity in existing meta-learning BO methods.
method Directly learns the utility of queries across tasks, models task uncertainty, and includes an auxiliary model for robust adaptation.
result Demonstrates strong anytime performance and outperforms state-of-the-art methods in various benchmarks.
Probabilistic active meta-learning improves data efficiency in robotics.
problem Data-efficient learning in robotics where data collection is expensive.
method Conceptualizing meta-learning with a probabilistic latent variable model for sequential task selection.
result Improves data efficiency compared to baselines on simulated robotic experiments.
We extend first-order model agnostic meta-learning algorithms (including FOMAML and Reptile) to image segmentation, present a novel neural network architecture built for fast learning which we call EfficientLab, and leverage a formal definition of the test error of meta-learning algorithms to decrease error on out of d…
Meta-learning with network pruning reduces overfitting and improves few-shot learning.
problem Overfitting in meta-learning models with over-parameterized neural networks.
method Network pruning to control capacity and explicitly reduce generalization gap.
result Uniform concentration analysis shows the benefit of network capacity constraint.
Meta-learning interpretable decision trees with synthetic data.
problem Lack of efficient, scalable methods for generating synthetic data for decision tree meta-learning.
method Synthetic generation of near-optimal decision trees using the MetaTree transformer architecture.
result Meta-learning of decision trees achieves performance comparable to real-world data or optimal decision trees, with significant computational cost reduction.