arXiv research

A new procedure aggregates models to predict data from multiple clusters.

Simpler GNNs with low-rank non-parametric aggregators perform well on graph benchmarks.

This paper addresses the problem of rank aggregation, which aims to find a consensus ranking among multiple ranking inputs. Traditional rank aggregation methods are deterministic, and can be categorized into explicit and implicit methods depending on whether rank information is explicitly or implicitly utilized. Surpri…

This paper compares rank aggregation methods for partial label ranking.

We address the problem of aggregating an ensemble of predictors with known loss bounds in a semi-supervised binary classification setting, to minimize prediction loss incurred on the unlabeled data. We find the minimax optimal predictions for a very general class of loss functions including all convex and many non-conv…

FABLE incorporates instance features into PWS label models for improved performance.

We propose a clustering-based iterative algorithm to solve certain optimization problems in machine learning, where we start the algorithm by aggregating the original data, solving the problem on aggregated data, and then in subsequent steps gradually disaggregate the aggregated data. We apply the algorithm to common m…

SADA safely combines predictions from various models for semi-supervised learning.

The paper introduces a new loss function to prevent overfitting in semi-supervised graph networks.

The study improves sentiment analysis of 10-K filings, revealing aggregation effects on accuracy and correlation with market outcomes.

A variety of graph neural networks (GNNs) frameworks for representation learning on graphs have been recently developed. These frameworks rely on aggregation and iteration scheme to learn the representation of nodes. However, information between nodes is inevitably lost in the scheme during learning. In order to reduce…

Aggregates diverse zero-shot LLM outputs for better corporate disclosure classification.

AdaCAD improves semi-supervised classification by focusing on intra-class nodes.

This work analyzes two methods for combining multiple binary labels in bipartite ranking.

Paper improves model accuracy with self-supervised learning.

Sharp-SSL uses random projections to identify important variables for semi-supervised learning.

Introduces SoRR for aggregating losses in supervised learning.

Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle general ensembles of training data. This paper presents a unifying framework for supervised classification with general ensembles of training dat…

Graph convolutional networks (GCNs) have been successfully applied in node classification tasks of network mining. However, most of these models based on neighborhood aggregation are usually shallow and lack the "graph pooling" mechanism, which prevents the model from obtaining adequate global information. In order to …

Paper proposes a new model for fine-grained review classification using weak supervision.

Paper proposes a supervised similarity framework for corporate bonds using RF proximities.

While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of disease, only have access to outputs at a much coarser level than that of the inputs. Aggregation of outputs makes generalization to new in…

Learning algorithms that aggregate predictions from an ensemble of diverse base classifiers consistently outperform individual methods. Many of these strategies have been developed in a supervised setting, where the accuracy of each base classifier can be empirically measured and this information is incorporated in the…

Consider a classification problem where we do not have access to labels for individual training examples, but only have average labels over subpopulations. We give practical examples of this setup and show how such a classification task can usefully be analyzed as a weakly supervised clustering problem. We propose thre…

Trajectory-level supervision allows efficient offline reinforcement learning.

U-aggregation combines multiple models without labels for better risk prediction.

This paper proposes a method to learn graph representations by partitioning edges into communities.

Graph convolutional network (GCN) is an emerging neural network approach. It learns new representation of a node by aggregating feature vectors of all neighbors in the aggregation process without considering whether the neighbors or features are useful or not. Recent methods have improved solutions by sampling a fixed …

Study improves fair opinion aggregation by balancing voter attributes.

Optimal and safe semi-supervised learning estimator for high-dimensional data.

Bayesian algorithms improve crowdsourcing with label and instance constraints.

Many real world problems can now be effectively solved using supervised machine learning. A major roadblock is often the lack of an adequate quantity of labeled data for training. A possible solution is to assign the task of labeling data to a crowd, and then infer the true label using aggregation methods. A well-known…

The paper extends multiple instance learning to multiclass and regression problems.

Census data provide detailed information about population characteristics at a coarse resolution. Nevertheless, fine-grained, high-resolution mappings of population counts are increasingly needed to characterize population dynamics and to assess the consequences of climate shocks, natural disasters, investments in infr…

Framework fine-tunes foundation models with semi-supervised learning for downstream tasks and latent spaces.

GNNs improve semi-supervised node regression, but why? We explain.

End-to-end approach for weak supervision improves downstream model performance.

PhyDNN uses physics knowledge to improve drag force prediction models.

GraphMix improves GNNs for semi-supervised learning.

New algorithm detects community labels in networks using unlabeled data.

New analysis reveals masked self-supervised learning's effectiveness in extracting data structure.

Contrastive regularization improves semi-supervised learning by better propagating confident pseudo-labels.

Boosting weak learners to strong ones from aggregate labels is possible for LLP but not for MIL.

Bipartite graphs have been used to represent data relationships in many data-mining applications such as in E-commerce recommendation systems. Since learning in graph space is more complicated than in Euclidian space, recent studies have extensively utilized neural nets to effectively and efficiently embed a graph's no…

Improves understanding of PWS by calculating influence of sources and data.

Deep Partition Aggregation defends against poisoning attacks with provable certificates.

Unsupervised method learns hierarchical graph representations without labels.

New financial dataset and model detect claims affecting market returns.