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 new meta-meta classification method tackles few-shot learning tasks.
problem Learning with limited data in small-data settings.
method Designing an ensemble of learners for a large set of problems, then learning how to combine them for a new problem.
result Meta-meta classification outperforms traditional meta-learning and ensembling approaches in one-shot learning tasks.
Develops HMRL for sparse reward RL problems, improving meta policy efficiency and transferability.
problem Difficulty in learning meta policies for sparse reward RL problems.
method Hyper-Meta RL framework with cross-environment meta state embedding and shaped meta reward.
result Improves meta policy generalization and efficiency for sparse reward RL problems.
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.
New method for incremental meta-learning reduces forgetting and improves performance.
problem Incremental meta-learning for few-shot classification tasks.
method Indirect Discriminant Alignment (IDA) with incremental updates.
result Incremental learning reduces forgetting and improves performance.
Meta-learning overfits both model and base learner; meta-augmentation helps.
problem Meta-learning overfits model and base learner.
method Use an information-theoretic framework to describe and demonstrate meta-augmentation.
result Meta-augmentation produces large complementary benefits to meta-regularization.
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.
Local nonparametric meta-learning improves meta-generalization across tasks.
problem Meta-learning struggles with global inductive biases and out-of-distribution tasks.
method Proposes a local, nonparametric meta-learning algorithm using meta-trained local learning rules.
result Improved meta-generalization and state-of-the-art results in robotics benchmarks.
Meta-learning, or learning to learn, is a machine learning approach that utilizes prior learning experiences to expedite the learning process on unseen tasks. As a data-driven approach, meta-learning requires meta-features that represent the primary learning tasks or datasets, and are estimated traditonally as engineer…
Meta-learning algorithms use past experience to learn to quickly solve new tasks. In the context of reinforcement learning, meta-learning algorithms acquire reinforcement learning procedures to solve new problems more efficiently by utilizing experience from prior tasks. The performance of meta-learning algorithms depe…
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.
Gradient-based meta-RL fails with incorrect task distributions, leading to instability and poor performance.
problem Gradient-based meta-RL's sensitivity to task distributions causes instability and poor performance.
method Proposes meta Active Domain Randomization (meta-ADR) to learn task distributions for gradient-based meta-RL.
result Meta-ADR improves stability and generalization of MAML on simulated locomotion and navigation tasks.
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.
G-Meta learns graph meta-learning from local subgraphs.
problem Learning from scarce data in graph tasks.
method Uses local subgraphs to transfer subgraph-specific information and learn transferable knowledge.
result G-Meta outperforms existing methods by up to 16.3% on seven datasets.
Paper tackles offline meta-reinforcement learning with a new algorithm.
problem Performing reinforcement learning on limited data from a new task.
method Meta-Actor Critic with Advantage Weighting (MACAW) algorithm.
result Achieves notable gains over prior methods on offline meta-RL benchmarks.
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.
MOCA enables meta-learning without task segmentation for online learning.
problem Meta-learning without task segmentation for online learning.
method MOCA combines meta-learning with online changepoint analysis.
result MOCA enables efficient meta-learning in time-varying tasks.
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'.
The paper proposes new strategies to exploit relationships between meta-tasks for better few-shot learning.
problem Few-shot learning struggles with domain gaps and poorly sampled data.
method Proposes exploiting relationships between meta-tasks to improve robustness and performance.
result Developed new learning objectives (MDA and MKD) to address domain gaps and improve robustness.
Meta Omnium benchmarks few-shot learning across diverse vision tasks.
problem Evaluating generalization of few-shot learning across multiple vision tasks.
method Introduction of Meta Omnium dataset and evaluation of meta-learning algorithms.
result Meta-learning algorithms can generalize across diverse vision tasks.
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.
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.
Meta-learning improves feature extraction for few-shot tasks.
problem Understanding why meta-learning models perform better on few-shot classification.
method Developed hypotheses and a regularizer to improve standard training routines.
result Meta-learned models outperform classical training routines in few-shot classification.
New method improves meta-learning performance by task-specific initialization.
problem Difficulties in generalizing and achieving theoretical guarantees in conditional meta-learning.
method Structured prediction approach for task-specific initialization.
result TASML improves performance of existing meta-learning models.
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.
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.
New symmetries improve meta-reinforcement learning's generalization.
problem Black-box meta reinforcement learning struggles with generalization.
method Introduce symmetries to a black-box meta RL system.
result Incorporating symmetries improves generalization to new environments.
Guarantees for tuning step size using meta-gradient descent.
problem Choosing optimal parameters for optimization algorithms.
method Learning-to-learn approach with meta-gradient descent.
result Meta-gradient can explode/vanish, requiring careful meta-objective design.
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.
Modified Meta-TS for linear contextual bandits reduces regret.
problem Optimizing decision-making in dynamic environments with context vectors.
method Meta-TSLB algorithm for linear contextual bandits, analyzing Bayes regret.
result Derives an O ( ( m + log ( m ) ) n log ( n ) ) O((m+\log(m))\sqrt{n\log(n)}) O (( m + log ( m )) n log ( n ) ) bound on Bayes regret. ARML learns task relations to improve meta-learning efficiency.
problem Handling task heterogeneity in meta-learning.
method Automatically extracts cross-task relations and constructs a meta-knowledge graph.
result ARML outperforms state-of-the-art baselines in few-shot learning tasks.
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.
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.
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.
PACOH improves meta-learning with theoretical guarantees and practical efficiency.
problem Meta-learning's generalization to unseen tasks is poorly understood, especially with limited meta-training tasks.
method PAC-Bayesian framework for deriving generalization bounds and developing PAC-optimal meta-learning algorithms.
result PACOH yields state-of-the-art performance in predictive accuracy and uncertainty estimation.
This paper bounds meta-generalization gap using information theory.
problem Improving sample efficiency for new tasks in meta-learning.
method Information-theoretic upper bounds on meta-generalization gap for two meta-learning classes.
result Novel ITMI bounds for noisy iterative algorithms.
TaskNorm improves meta-learning performance by rethinking batch normalization.
problem Challenges in batch normalization for meta-learning with deep networks.
method Developed TaskNorm, a novel approach to batch normalization for meta-learning.
result TaskNorm consistently improves meta-learning performance across various datasets and meta-learning approaches.
Meta-ticket finds optimal sparse subnetworks for few-shot learning in randomly initialized neural networks.
problem Avoiding overfitting in few-shot learning for over-parameterized neural networks.
method Meta-learning approach to find optimal sparse subnetworks.
result Meta-ticket discovers sparse subnetworks that adapt to each task, achieving superior meta-generalization.
Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem. In this paper, we propose a novel Bayesian model-agnostic meta-learning method. The proposed method combines scalable gradient-based meta-learning with non…
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.
New meta-learning framework for minimizing simple regret in bandits.
problem Minimizing simple regret in a sequence of bandit tasks with unknown distributions.
method Developed Bayesian and frequentist meta-learning algorithms for bandits, analyzing their meta simple regret.
result Bayesian algorithm achieves i l d e O ( m / n ) ilde{O}(m / \sqrt{n}) i l d e O ( m / n ) meta simple regret, frequentist algorithm i l d e O ( m n + m / n ) ilde{O}(\sqrt{m} n + m/ \sqrt{n}) i l d e O ( m n + m / n ) . Study OOD generalization in meta-reinforcement learning using information theory.
problem Understanding how meta-reinforcement learning handles distribution shifts.
method Information-theoretic analysis of Markov Decision Processes and gradient-based algorithms.
result Established fine-grained generalization bounds for meta-reinforcement learning.
Meta-learning bound uses conditional mutual information.
problem Bounding generalization performance in meta-learning.
method Extends CMI framework to meta-learning with a meta-supersample.
result Explicit bound involving two CMI terms.
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.
New method improves meta-reinforcement learning efficiency.
problem Sample inefficiency in meta-reinforcement learning.
method Hindsight Foresight Relabeling (HFR) method.
result HFR improves performance on various meta-reinforcement learning tasks.
Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood. Existing methods either neglect credit assignment to pre-adaptation behavior or implement it naively. This leads to poor sample-efficiency during meta-training as well as ineffective task identification strategies. This paper provide…
Meta-learning is increasingly used to support the recommendation of machine learning algorithms and their configurations. Such recommendations are made based on meta-data, consisting of performance evaluations of algorithms on prior datasets, as well as characterizations of these datasets. These characterizations, also…
Most meta reinforcement learning (meta-RL) methods learn to adapt to new tasks by directly optimizing the parameters of policies over primitive action space. Such algorithms work well in tasks with relatively slight difference. However, when the task distribution becomes wider, it would be quite inefficient to directly…