Unified MTL framework for heterogeneous data integrates shared and task-specific encoders.
problem Efficiently sharing information across multiple tasks with heterogeneous data.
method Dual-encoder framework with task-shared and task-specific encoders.
result Unified algorithm alternates learning task-specific and shared encoders and coefficients.
Paper extends multi-task Gaussian Cox processes for heterogeneous tasks.
problem Modeling multiple heterogeneous correlated tasks jointly.
method Data augmentation and mean-field approximation for non-conjugate Bayesian inference.
result Demonstrates improved performance and inference on synthetic and real data.
Study tackles contamination and heterogeneity in multi-task learning, improving robustness and personalization.
problem Challenges in integrating related tasks due to contamination and heterogeneity.
method Proposes a filtering-based robust multi-task gradient descent method to estimate global and clean task-specific minimizers.
result Demonstrates improved robustness and personalization compared to existing methods.
Paper introduces a new model to handle multi-task learning across different input domains.
problem Learning correlated tasks across varying input domains.
method Develops a novel heterogeneous stochastic variational linear model of coregionalization (HSVLMC) for multi-task learning.
result The proposed model outperforms existing models in diverse multi-task scenarios.
Optimal multitask learning method for sparse heterogeneous datasets.
problem Efficiently learning from multiple related datasets with sparse task-specific differences.
method MOLAR estimator, combining weighted median and shrinkage.
result Improves estimation error dependence on data dimension compared to task-wise least squares.
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.
VSAE learns from missing heterogeneous data by modeling latent dependencies.
problem Learning from partially-observed heterogeneous data with missingness.
method Variational selective autoencoder (VSAE) models joint distribution of observed, unobserved, and missing data.
result VSAE improves over state-of-the-art models in data generation and imputation tasks.
This survey reviews Heterogeneous Representation Learning (HRL) for diverse data types.
problem Challenges in learning from heterogeneous data types.
method Unified learning framework for modeling various learning settings.
result Unified mathematical framework for multi-view, transfer, privileged, and multi-task learning.
We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogeneous learning tasks, which correspond to different generative processes, often rely on data with a shared graph structure. Accordingly, our …
Meta learning is a promising solution to few-shot learning problems. However, existing meta learning methods are restricted to the scenarios where training and application tasks share the same out-put structure. To obtain a meta model applicable to the tasks with new structures, it is required to collect new training d…
MetaPerturb learns to improve generalization across different tasks and architectures.
problem Improving generalization on unseen data for diverse tasks and architectures.
method MetaPerturb is a meta-learned set-based perturbation function that improves generalization performance across heterogeneous tasks and architectures.
result MetaPerturb significantly outperforms baselines on most tasks and architectures with minimal increase in parameter size and no hyperparameters to tune.
FedGradNorm improves federated MTL by balancing task learning speeds.
problem Statistical and data heterogeneity in federated MTL.
method Dynamic-weighting normalization of gradient norms.
result FedGradNorm achieves faster training performance and balanced datasets.
Modeling student behaviors and multiple predictions for early intervention.
problem Predicting student outcomes and interactions among multiple tasks.
method Proposes a variant of LSTM and soft-attention mechanism for heterogeneous behaviors, and co-attention mechanism for task interactions.
result Demonstrated effectiveness in predicting student outcomes and interactions.
A new method improves semi-supervised learning by handling tasks with different attribute spaces.
problem Existing methods assume tasks share the same attribute space, limiting their applicability.
method Meta-learning approach that embeds labeled and unlabeled data in task-specific spaces using neural networks.
result Improves test performance on tasks with small labeled data using unlabeled and various task data.
Graph representation learning is to learn universal node representations that preserve both node attributes and structural information. The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering. When a graph is heterogeneous, the problem becomes more…
Graph attention networks improve performance on heterogeneous graphs.
problem Complex performance of GNNs on heterogeneous graphs.
method Integrating positional encodings into graph attention networks.
result Graph attention networks excel in node classification and link prediction.
Boosted tree method improves MTL in heterogeneous domains.
problem Improving MTL in diverse, domain-specific tasks.
method Two-stage approach: common model for shared features, specific models for task-specific instances.
result Enhanced multi-task learning performance with interpretability.
In this paper, we study the problem of author identification under double-blind review setting, which is to identify potential authors given information of an anonymized paper. Different from existing approaches that rely heavily on feature engineering, we propose to use network embedding approach to address the proble…
Meta-BO method clusters and learns from prior tasks to optimize heterogeneous functions.
problem Optimizing multiple functions with historical data and scalability issues.
method Clustering-based meta-learning, surrogate prototypes, adaptive weighting policies.
result Scalable and robust meta-BO method improves convergence to global optimum.
DyHATR learns dynamic heterogeneous networks for better link prediction.
problem Learning effective representations of dynamic heterogeneous networks for link prediction.
method Hierarchical attention for heterogeneous information and temporal RNN for evolutionary patterns.
result DyHATR significantly outperforms state-of-the-art baselines on link prediction tasks.
This paper evaluates heterogeneous information fusion using multi-task Gaussian processes in the context of geological resource modeling. Specifically, it empirically demonstrates that information integration across heterogeneous information sources leads to superior estimates of all the quantities being modeled, compa…
Machine learning approaches have been effective in predicting adverse outcomes in different clinical settings. These models are often developed and evaluated on datasets with heterogeneous patient populations. However, good predictive performance on the aggregate population does not imply good performance for specific …
New method for clustering tasks with heterogeneous data.
problem Clustered multitask learning with semiparametric and heterogeneous nuisances.
method Adaptive fused orthogonal estimator with Neyman-orthogonal losses and data-driven fusion penalties.
result Achieves exact clustering recovery and pooled parametric convergence rates.
SLiCE learns contextual node embeddings for link prediction in heterogeneous networks.
problem Link prediction requires specific contextual information not captured by static node embeddings.
method Self-supervised pre-training with localized attention mechanisms.
result SLiCE significantly outperforms existing methods on link prediction tasks.
Proposes a robust FMR model for handling sample heterogeneity.
problem Handling sample heterogeneity with a single regression model.
method Clusters samples and jointly models multiple incomplete mixed-type targets.
result Achieves state-of-the-art performance on synthetic and real-world data.
Self-supervised pretraining for heterogeneous hypergraphs improves graph-based tasks.
problem Capturing higher-order relations in heterogeneous hypergraphs.
method SPHH framework for self-supervised pretraining of heterogeneous HyperGNNs.
result SPHH consistently outperforms state-of-the-art baselines in various downstream tasks.
GeoERM learns shared representations on Riemannian manifolds for multi-task learning.
problem Heterogeneous and adversarial tasks in MTL.
method Geometry-aware MTL framework embedding shared representation on Riemannian manifold, optimizing via manifold operations.
result GeoERM improves estimation accuracy and stability, outperforming alternatives.
Framework for multi-task learning with semiparametric models and nuisance parameters.
problem Improving parameter estimation from diverse, heterogeneous datasets.
method Late fusion multi-task learning framework with two-step process: individual task learning followed by adaptive aggregation.
result The method achieves faster convergence rates compared to individual task learning when tasks share similar parametric components.
Many real-world datasets are labeled with natural orders, i.e., ordinal labels. Ordinal regression is a method to predict ordinal labels that finds a wide range of applications in data-rich domains, such as natural, health and social sciences. Most existing ordinal regression approaches work well for independent and id…
Personalized models explain TB treatment outcomes considering patient context.
problem Heterogeneity in TB treatment outcomes due to co-morbidities.
method Multi-task learning approach encoding patient context into personalized models.
result Identifies anemia, age of onset, and HIV as influential for treatment efficacy.
Paper proposes a novel MTL framework for personalized modeling of diverse individuals.
problem Personalized modeling of heterogeneous subpopulations with high-dimensional data.
method Low-rank tensor decomposition for multi-task learning.
result Superior performance compared to benchmarks in diverse subpopulation scenarios.
Reinforcement learning (RL) has achieved tremendous success as a general framework for learning how to make decisions. However, this success relies on the interactive hand-tuning of a reward function by RL experts. On the other hand, inverse reinforcement learning (IRL) seeks to learn a reward function from readily-obt…
PanRep learns universal node embeddings for heterogeneous graphs.
problem Learning universal node embeddings for heterogeneous graphs.
method Graph Neural Network (GNN) model with four decoders capturing different properties.
result PanRep outperforms unsupervised and supervised methods in node classification and link prediction.
New method for learning on heterogeneous graphs without meta-paths.
problem Learning on heterogeneous graphs is sensitive to meta-paths choice, leading to poor performance.
method Decompose heterogeneous graph into homogeneous relation-type graphs, combine higher-order representations, use attention mechanisms.
result Our model outperforms state-of-the-art baselines in vertex classification tasks on heterogeneous graph datasets.
Generative models unify heterogeneous data for multimodal fusion.
problem Learning effective representations of mixed numerical and categorical data.
method Bayesian approach with exponential family distributions and Laplace-Bernstein approximation.
result Generative models enable fusion of multimodal data for various machine learning tasks.
HGT models heterogeneous graphs with dedicated node and edge representations.
problem Infeasibility of existing GNNs for heterogeneous graphs.
method Node- and edge-type dependent parameters, relative temporal encoding, heterogeneous mini-batch graph sampling.
result HGT outperforms state-of-the-art GNNs by 9%--21% on various downstream tasks.
Estimates population mean from user-level data with privacy, accounting for heterogeneity.
problem Heterogeneous user data with varying numbers of data points and distributions.
method Simple model of heterogeneous user data, differential privacy mechanism for estimation.
result Asymptotic optimality of the proposed estimator and general lower bounds on error.
Algorithm uncovers treatment effect heterogeneity in educational RD designs.
problem Discovering sources of treatment effect heterogeneity in regression discontinuity designs.
method Causal supervised machine learning algorithm to build a 'regression discontinuity tree'.
result Algorithm uncovers various sources of heterogeneity in the impact of attending a better secondary school.
VAEM extends VAEs to handle mixed-type data heterogeneity.
problem Heterogeneous data with different types and marginal distributions.
method Two-stage training approach to handle mixed-type data.
result VAEM improves deep generative model performance on diverse tasks.
Heterogeneous network embedding (HNE) is a challenging task due to the diverse node types and/or diverse relationships between nodes. Existing HNE methods are typically unsupervised. To maximize the profit of utilizing the rare and valuable supervised information in HNEs, we develop a novel Active Heterogeneous Network…
A new federated learning framework for handling device heterogeneity.
problem Handling device heterogeneity in federated learning.
method Superquantile-based objective with parameterized levels of conformity, optimized using secure aggregation.
result The optimization algorithm converges to a stationary point.
Proposes PFWCP for multi-agent tasks with privacy and validity guarantees.
problem Challenges in uncertainty quantification for multi-agent settings.
method Personalized federated weighted conformal prediction (PFWCP) combining local density ratio weighting and weighted quantile aggregation.
result Asymptotically valid coverage guarantees for each agent in heterogeneous settings.
Paper introduces HGSL for heterogeneous graphs, improving edge type and weight recovery.
problem Learning structure in heterogeneous graphs with multiple node and edge types.
method Proposes H2MN model for DGPs and derives alternating optimization method.
result Demonstrates superior performance on synthetic and real-world datasets.
Distance metric learning (DML) plays a crucial role in diverse machine learning algorithms and applications. When the labeled information in target domain is limited, transfer metric learning (TML) helps to learn the metric by leveraging the sufficient information from other related domains. Multi-task metric learning …
Proposes a hybrid model for stock market report classification using graph neural networks.
problem Lack of unified node embeddings for heterogeneous graphs in text datasets.
method Transductive hybrid approach combining unsupervised node representation learning and supervised node classification/edge prediction.
result Demonstrates the model's ability to classify stock market technical analysis reports.
An increasing number of well-trained deep networks have been released online by researchers and developers, enabling the community to reuse them in a plug-and-play way without accessing the training annotations. However, due to the large number of network variants, such public-available trained models are often of diff…
LATTE tackles heterogeneous network embedding challenges with layer-stacked attention.
problem Aggregating higher-order indirect relations in heterogeneous networks.
method Layer-stacked ATTention Embedding (LATTE) that decomposes meta relations at each layer.
result LATTE achieves state-of-the-art performance on benchmark datasets.
MetaDVFS uses device and application metadata to improve DVFS efficiency.
problem Improving energy efficiency in mobile platforms with diverse applications and hardware.
method Formulates DVFS as a multi-task reinforcement learning problem and introduces MetaDVFS, leveraging metadata for knowledge transfer.
result MetaDVFS achieves up to 26% improvement in Quality of Experience and up to 17% improvement in Performance-Power Ratio.