Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples. In this work, we propose a Meta Relational Learning (MetaR) framework to do the common but challenging few-sh…
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48 results for “meta prediction”
Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Predictionstat.ML
Meta-learning reduces set prediction size in conformal prediction for few-shot calibration.
problem Inefficient set prediction in conformal prediction for limited training data.
method Meta-learning approach using cross-validation-based conformal prediction.
result Meta-learning scheme reduces set prediction size and preserves formal guarantees.
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.
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.
Proposes active learning for meta-learning in graph node response prediction.
problem Difficulty in improving performance with meta-learning due to unbalanced observations.
method Combines graph convolutional neural networks and reinforcement learning for both prediction and node selection.
result Can predict responses and select nodes even for unseen response variables.
Meta-Graph learns to predict missing edges quickly from few samples.
problem Few-shot link prediction on graphs, especially when samples are sparse.
method Meta-learning framework that uses higher-order gradients and learned graph signatures.
result Meta-Graph can quickly adapt to new graphs using only a small sample of true edges.
This paper uses XAI techniques to explain meta-learning models.
problem Lack of understanding how meta-features contribute to model performance.
method XAI techniques applied to explain black-box surrogate models.
result Improved understanding of meta-features' importance and effect.
Meta-learning predicts stock trading volumes by learning from each stock's unique patterns.
problem Predicting trading volumes for different stocks using a universal model.
method Dual-process meta-learning framework that learns common patterns with a meta-learner and specific patterns with stock-dependent parameters.
result Improves performance of various baseline models in volume predictions.
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.
Boosting meta-trees improve decision tree performance.
problem Overfitting in decision trees.
method Boosting approach to construct multiple meta-trees.
result Ensembles of meta-trees prevent overfitting.
Meta-Neighborhoodscs.LG
Making an adaptive prediction based on one's input is an important ability for general artificial intelligence. In this work, we step forward in this direction and propose a semi-parametric method, Meta-Neighborhoods, where predictions are made adaptively to the neighborhood of the input. We show that Meta-Neighborhood…
Meta-ANOVA simplifies complex models for better interpretability.
problem Complex models are hard to interpret, limiting their use in fields needing accountability.
method Transforms black-box models into interpretable ANOVA models by screening unnecessary interactions.
result Meta-ANOVA provides an interpretable model for any prediction model, proving asymptotic consistency.
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.
Bayesian meta-learning predicts Alzheimer's disease progression.
problem Predicting individual Alzheimer's disease progression from limited data.
method Bayesian meta-learning approach that dynamically predicts disease score distributions.
result Bayesian meta-learner outperforms single-task models and deterministic meta-learners, especially for long-term predictions.
New algorithm constructs PAC prediction sets for meta-learning.
problem Uncertainty quantification in safety-critical systems.
method Proposes a novel algorithm to construct PAC prediction sets.
result Prediction sets satisfy a PAC guarantee with high probability over future tasks.
Several multi-target regression methods were devel-oped in the last years aiming at improving predictive performanceby exploring inter-target correlation within the problem. However, none of these methods outperforms the others for all problems. This motivates the development of automatic approachesto recommend the mos…
Bayesian meta-learning improves health prediction models across similar diseases.
problem Inter- and intra-task variability in healthcare predictions due to disease heterogeneity and patient differences.
method Bayesian meta-learning approach that models task similarity to mitigate negative transfer and improve generalizability.
result Significant generalizability improvements in stroke prediction tasks using electronic health record data.
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 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.
NeuroQuery synthesizes brain mapping evidence across diverse concepts.
problem Lack of comprehensive meta-analysis of human brain mapping across different mental processes and mechanisms.
method A multivariate model that predicts the spatial distribution of neurological observations given text describing an experiment, cognitive process, or disease.
result Captures relationships and neural correlates of 7,547 neuroscience terms across 13,459 neuroimaging publications.
Compared to humans, machine learning models generally require significantly more training examples and fail to extrapolate from experience to solve previously unseen challenges. To help close this performance gap, we augment single-task neural networks with a meta-recognition model which learns a succinct model code vi…
Meta-models predict model hyperparameters for NDT experiments.
problem Non-destructive testing experiments are isolated; this work connects them.
method Bayesian multilevel approach, capturing inter-task relationships.
result Meta-models encode knowledge between and within tasks for transfer learning.
This paper investigates different vector step-size adaptation approaches for non-stationary online, continual prediction problems. Vanilla stochastic gradient descent can be considerably improved by scaling the update with a vector of appropriately chosen step-sizes. Many methods, including AdaGrad, RMSProp, and AMSGra…
Study improves peptide design efficiency using active and meta-learning.
problem Designing novel functional peptides is inefficient due to low throughput and lack of data.
method Investigated active learning and meta-learning for optimizing peptide design experiments.
result Meta-learning improved average accuracy, but neither method outperformed random choice.
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.
A new method for efficient probabilistic meta-learning.
problem High-quality predictions with well-calibrated uncertainty estimates require large amounts of data.
method Amortised Inference in Bayesian Neural Networks (APOVI-BNN)
result The APOVI-BNN produces high-quality predictions with well-calibrated uncertainty estimates using significantly less data.
In this thesis, we study the problem of feature learning on heterogeneous knowledge graphs. These features can be used to perform tasks such as link prediction, classification and clustering on graphs. Knowledge graphs provide rich semantics encoded in the edge and node types. Meta-paths consist of these types and abst…
Meta Optimal Transport learns from past problems to solve similar OT problems faster.
problem Solving similar optimal transport problems repeatedly from scratch is inefficient.
method Amortized optimization to predict optimal transport maps from past solutions.
result Meta OT models can solve new problems faster than standard methods.
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.
Framework uses physics knowledge to improve spatiotemporal prediction with limited data.
problem Challenges in modeling physical systems with limited real-world data.
method Physics-aware meta-learning with auxiliary tasks, incorporating PDE-independent spatial and temporal modules.
result Framework outperforms in spatiotemporal prediction tasks with limited data.
Transformers learn to solve various tasks without explicit design.
problem Training models with minimal inductive bias.
method Meta-learning approach to train general-purpose in-context learning algorithms.
result Transformers can be meta-trained to solve a wide range of tasks.
Meta-learning improves GNN initializations for low-resource drug discovery.
problem Limited labeled data hinders deep learning in drug discovery.
method Model-Agnostic Meta-Learning (MAML) and its variants for graph neural networks initializations.
result Meta-initializations outperform multi-task pre-training baselines on 16 out of 20 tasks and all out-of-distribution tasks.
Stacked conformal predictionstat.ML
Stacked conformal prediction simplifies model validation.
problem Validating stacked predictive models efficiently.
method Meta-learner at the top of a stacked ensemble for approximate marginal validity.
result The method achieves approximate marginal validity without a separate calibration sample.
New method uses untrusted data for more precise causal analysis.
problem Causal questions with limited trusted data.
method Incorporates untrusted data and trains richer models.
result Tighter, sounder prediction intervals.
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.
The success of machine learning on a given task dependson, among other things, which learning algorithm is selected and its associated hyperparameters. Selecting an appropriate learning algorithm and setting its hyperparameters for a given data set can be a challenging task, especially for users who are not experts in …
Publication bias skews asset pricing research findings.
problem Bias in sharing and publishing research findings.
method Meta-studies and empirical Bayes corrections.
result Publication bias effects are minimal and not dominant.
FLAP adapts policies quickly to new tasks using shared linear representations.
problem Adapting policies to new tasks efficiently and effectively.
method FLAP uses a shared linear representation and a separate adapter network for quick adaptation.
result FLAP achieves up to 8X faster adaptation and significantly better performance on out-of-distribution tasks.
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…
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 bound on Bayes regret.
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.
For many machine learning algorithms, predictive performance is critically affected by the hyperparameter values used to train them. However, tuning these hyperparameters can come at a high computational cost, especially on larger datasets, while the tuned settings do not always significantly outperform the default val…
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 improves event prediction from short sequences.
problem Predicting events from short sequences is challenging.
method Meta-learning approach using recurrent neural networks and monotonic neural networks.
result Meta-learning enhances long-term prediction performance.
Memory-based models can learn to approximate Bayes-optimal predictors for non-stationary data.
problem Learning from non-stationary data with unobserved switching points.
method Memory-based neural models, including Transformers, LSTMs, and RNNs, trained to minimize log loss.
result Memory-based models can accurately approximate known Bayes-optimal algorithms and perform Bayesian inference over latent switching points.
Proposes a method to improve graph neural networks on heterogeneous graphs using meta-paths.
problem Improving graph neural networks on heterogeneous graphs with auxiliary tasks.
method Self-supervised auxiliary learning with meta-paths for heterogeneous graphs.
result Consistently improves link prediction and node classification on heterogeneous graphs.
A ranking network improves neural architecture search efficiency.
problem Efficiently search for neural network architectures without extensive training.
method Pairwise ranking loss for a performance predictor trained on task meta-features.
result The ranking network outperforms the performance predictor in architecture search.
Meta-learning neural networks to solve diverse PDEs efficiently.
problem Efficiently solving new PDE problems with minimal training.
method Neural network meta-learning of PDE problem representations.
result Meta-learned neural networks predict PDE solutions with high accuracy.