arXiv research

Optimal fuzzy classification aggregation functions are weighted means.

Partial fusion combines neural networks to balance accuracy and efficiency.

We improve prediction set coverage by assigning weights to individual sets.

Unified approach to aggregating models and preferences.

Paper analyzes sparse aggregation in GLMs with Kullback-Leibler risk bounds.

MANA-Net improves market predictions by dynamically weighting news sentiments.

New method preserves privacy by aggregating feature-vectors with weighted sums, ensuring label differential privacy.

This paper solves aggregation of Pareto optimal models by using Bayesian priors and weighted averaging.

New aggregation strategy handles unbounded losses with regret bounds.

CAggNet improves medical image segmentation by fusing coarse and fine features.

Paper tightens statistical aggregation results using local complexity.

We study the topological properties of the multinetwork of commodity-specific trade relations among world countries over the 1992-2003 period, comparing them with those of the aggregate-trade network, known in the literature as the international-trade network (ITN). We show that link-weight distributions of commodity-s…

A new method for faster prediction in distributed Gaussian processes.

No fair and strategy-proof automated market maker exists for more than two assets.

AEW estimator achieves optimal risk in expectation for large enough temperatures.

Current approaches for explaining machine learning models fall into two distinct classes: antecedent event influence and value attribution. The former leverages training instances to describe how much influence a training point exerts on a test point, while the latter attempts to attribute value to the features most pe…

DAGgr aggregates multiple DAGs to stabilize causal structure learning.

Enhances graph neural networks by considering feature similarities in node aggregation.

Paper tackles unknown participation in FL, proposing FedAU for better performance.

In this article, we aim at improving the prediction of expert aggregation by using the underlying properties of the models that provide expert predictions. We restrict ourselves to the case where expert predictions come from Kalman recursions, fitting state-space models. By using exponential weights, we construct diffe…

Study improves fair opinion aggregation by balancing voter attributes.

Federated learning obtains a central model on the server by aggregating models trained locally on clients. As a result, federated learning does not require clients to upload their data to the server, thereby preserving the data privacy of the clients. One challenge in federated learning is to reduce the client-server c…

Method aggregates models with different hyper-parameters to adapt to target domain.

AWARE improves graph prediction by aggregating walks with attention schemes.

We introduce a new recursive aggregation procedure called Bernstein Online Aggregation (BOA). The exponential weights include an accuracy term and a second order term that is a proxy of the quadratic variation as in Hazan and Kale (2010). This second term stabilizes the procedure that is optimal in different senses. We…

MELO predicts electricity loads by adapting to shifts without external indicators.

WildWood improves Random Forest predictions using bootstrap out-of-bag samples.

Aggregates probability models using Wasserstein space and variational approach.

New method improves LLM judge accuracy by accounting for dependencies in aggregated binary labels.

Crowdsourcing has become an effective and popular tool for human-powered computation to label large datasets. Since the workers can be unreliable, it is common in crowdsourcing to assign multiple workers to one task, and to aggregate the labels in order to obtain results of high quality. In this paper, we provide finit…

Given a finite family of functions, the goal of model selection aggregation is to construct a procedure that mimics the function from this family that is the closest to an unknown regression function. More precisely, we consider a general regression model with fixed design and measure the distance between functions by …

Improves quantile regression models by aggregating multiple models.

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. In this work, we present a Graph Convolutional Network (GCN) algorithm SWAG (Sample Weight and AGgregate), which combines efficient random walks and graph convolutions on weig…

This work analyzes aggregation strategies for Bayesian deep learning models in federated learning.

New method for disaggregate electricity demand forecasting at household level.

We introduce a new class of context dependent, incomplete information games to serve as structured prediction models for settings with significant strategic interactions. Our games map the input context to outcomes by first condensing the input into private player types that specify the utilities, weighted interactions…

Let $\cF$ be a set of $M$ classification procedures with values in $[-1,1]$. Given a loss function, we want to construct a procedure which mimics at the best possible rate the best procedure in $\cF$. This fastest rate is called optimal rate of aggregation. Considering a continuous scale of loss functions with various …

In order to improve forecasts, a decisionmaker often combines probabilities given by various sources, such as human experts and machine learning classifiers. When few training data are available, aggregation can be improved by incorporating prior knowledge about the event being forecasted and about salient properties o…

PEARL combines multiple representation learning methods to enhance model performance.

New method improves forecast accuracy using CRPS for probabilistic predictions.

CARE improves LLM aggregation by accounting for shared confounders.

We consider the problem of belief aggregation: given a group of individual agents with probabilistic beliefs over a set of uncertain events, formulate a sensible consensus or aggregate probability distribution over these events. Researchers have proposed many aggregation methods, although on the question of which is be…

BiTAT improves neural network quantization for edge devices by focusing on weight dependencies and disentangling them.

The paper quantifies uncertainty in aggregated machine learning metrics.

Multiple query criteria active learning (MQCAL) methods have a higher potential performance than conventional active learning methods in which only one criterion is deployed for sample selection. A central issue related to MQCAL methods concerns the development of an integration criteria strategy (ICS) that makes full …

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…

The paper tackles uncertainty quantification in multi-source settings.

Algorithm learns expert weights to minimize regret in adversarial setting.