arXiv research

New algorithm for aggregate inference in HMMs with continuous observations.

A new algorithm estimates aggregate marginals from noisy data in an online manner.

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

Active learning method reduces labeling cost for regression models with aggregated data.

New LCM aggregator improves GNN performance and efficiency.

Aggregated variables can mask causal effects, turning unconfounded into confounded relations.

Multidimensional data have become ubiquitous and are frequently encountered in situations where the information is aggregated over multiple data atoms. The aggregation can be over time or other features, such as geographical location. We often have access to multiple aggregated views of the same data, each aggregated i…

This work falls within the context of predicting the value of a real function at some input locations given a limited number of observations of this function. The Kriging interpolation technique (or Gaussian process regression) is often considered to tackle such a problem but the method suffers from its computational b…

Aggregation distorts causal discovery results but recovery is possible with partial linearity or prior.

We study a variant of the stochastic $K$-armed bandit problem, which we call "bandits with delayed, aggregated anonymous feedback". In this problem, when the player pulls an arm, a reward is generated, however it is not immediately observed. Instead, at the end of each round the player observes only the sum of a number…

We use online convex optimization (OCO) for setpoint tracking with uncertain, flexible loads. We consider full feedback from the loads, bandit feedback, and two intermediate types of feedback: partial bandit where a subset of the loads are individually observed and the rest are observed in aggregate, and Bernoulli feed…

Revises GNN neighborhood aggregation for more accurate node classification.

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

This paper improves prediction accuracy for multi-input classification tasks using p-value aggregation.

New algorithm for efficient inference over tree-structured graphs.

Non-affine aggregation rules cannot preserve monotonicity in convex learning.

Study on stock portfolio concentration among Finnish households and investors.

Learning nonlinear dynamics from diffusion data is a challenging problem since the individuals observed may be different at different time points, generally following an aggregate behaviour. Existing work cannot handle the tasks well since they model such dynamics either directly on observations or enforce the availabi…

We consider the forecast aggregation problem in repeated settings, where the forecasts are done on a binary event. At each period multiple experts provide forecasts about an event. The goal of the aggregator is to aggregate those forecasts into a subjective accurate forecast. We assume that experts are Bayesian; namely…

New aggregation method improves GNN robustness to structural perturbations.

A new method combines Gaussian graphical models for better distributed Gaussian process predictions.

Improved meta-learning for dynamics using additional structured knowledge.

Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do machine learning from the need to store the data in the cloud. Existing work on federated learning with limited communication demonstrates how …

Learning nonlinear dynamics from aggregate data is a challenging problem because the full trajectory of each individual is not available, namely, the individual observed at one time may not be observed at the next time point, or the identity of individual is unavailable. This is in sharp contrast to learning dynamics w…

This paper introduces glocal explanations for expected goal models in soccer.

FedNNNN improves FL by adjusting model update vector norms.

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

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…

Study optimal policy regret in partially observable Markov games with adaptive opponents.

Using Monte Carlo simulation to calculate the Value at Risk (VaR) as a possible risk measure requires adequate techniques. One of these techniques is the application of a compound distribution for the aggregates in a portfolio. In this paper, we consider the aggregated loss of Gamma distributed severities and estimate …

We address the problem of estimating the parameters of a time-homogeneous Markov chain given only noisy, aggregate data. This arises when a population of individuals behave independently according to a Markov chain, but individual sample paths cannot be observed due to limitations of the observation process or the need…

Model infers functions for attributes using multi-aggregate datasets with knowledge transfer.

Multilayer networks are attracting growing attention in many fields, including finance. In this paper, we develop a new tractable procedure for multilayer aggregation based on statistical validation, which we apply to investor networks. Moreover, we propose two other improvements to their analysis: transaction bootstra…

Policy-GNN optimizes GNN aggregation for diverse node iterations.

New algorithm for collective Gaussian hidden Markov models inference.

This paper compares two loss functions for learning from aggregated responses and introduces an interpolating estimator.

Improved rank aggregation via spectral method reduces sample complexity.

Enhances survival analysis by separating population behavior from individual dynamics.

Augments GNNs with diversification to preserve node identity.

Message-passing neural networks (MPNNs) have been successfully applied to representation learning on graphs in a variety of real-world applications. However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoo…

SCHA-VAE generates novel data from limited examples using hierarchical context aggregation.

Paper proposes a robust method for federated ICA with geometric median aggregation.

Paper tackles learning win-win solutions in aggregation systems.

An approach to distributed machine learning is to train models on local datasets and aggregate these models into a single, stronger model. A popular instance of this form of parallelization is federated learning, where the nodes periodically send their local models to a coordinator that aggregates them and redistribute…

Develops GNNs for incomplete graphs, improving learning from missing node attributes.

Motivated by electricity consumption metering, we extend existing nonnegative matrix factorization (NMF) algorithms to use linear measurements as observations, instead of matrix entries. The objective is to estimate multiple time series at a fine temporal scale from temporal aggregates measured on each individual serie…

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…

This paper models default data to capture dynamic dependence across sectors.