arXiv research

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

New aggregation strategy handles unbounded losses with regret bounds.

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

Paper tightens statistical aggregation results using local complexity.

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…

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

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…

Meta-strategy learns tuning parameters for online learning methods.

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

This paper addresses the problem of prediction with expert advice for outcomes in a geodesic space with non-positive curvature in the sense of Alexandrov. Via geometric considerations, and in particular the notion of barycenters, we extend to this setting the definition and analysis of the classical exponentially weigh…

Extends DAMs to Gaussian distributions for efficient pattern storage and retrieval.

Study online monotone density estimation with expert aggregation and log-optimal calibration.

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 …

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 …

Annealed Entropic Allocation improves ranking and selection by mitigating hard switching and improving finite-budget discrimination.

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…

We consider the problem of adaptation to the margin and to complexity in binary classification. We suggest an exponential weighting aggregation scheme. We use this aggregation procedure to construct classifiers which adapt automatically to margin and complexity. Two main examples are worked out in which adaptivity is a…

Novel Bayesian method for high-dimensional count data prediction.

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 …

New approach optimizes policies in adversarial MDPs using adversarial learning.

The paper introduces a new class of multivariate mixtures for actuarial applications.

We consider the design of prediction market mechanisms known as automated market makers. We show that we can design these mechanisms via the mold of \emph{exponential family distributions}, a popular and well-studied probability distribution template used in statistics. We give a full development of this relationship a…

Optimal fuzzy classification aggregation functions are weighted means.

Partial fusion combines neural networks to balance accuracy and efficiency.

The paper introduces a tensor-based approach to improve neural models' aggregation of structural context.

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

Production forecasting is a key step to design the future development of a reservoir. A classical way to generate such forecasts consists in simulating future production for numerical models representative of the reservoir. However, identifying such models can be very challenging as they need to be constrained to all a…

New method compresses non-Gaussian distributions exponentially.

Unified approach to aggregating models and preferences.

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.

In high dimensions we propose and analyze an aggregation estimator of the precision matrix for Gaussian graphical models. This estimator, called graphical Exponential Screening (gES), linearly combines a suitable set of individual estimators with different underlying graphs, and balances the estimation error and sparsi…

Boosting framework for vector-valued prediction with geometric stability.

Extends online learning to metric spaces using exponential weights.

Integrating adaptive learning rate and momentum techniques into SGD leads to a large class of efficiently accelerated adaptive stochastic algorithms, such as AdaGrad, RMSProp, Adam, AccAdaGrad, \textit{etc}. In spite of their effectiveness in practice, there is still a large gap in their theories of convergences, espec…

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

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.

We introduce a covariance matrix estimator that both takes into account the heteroskedasticity of financial returns (by using an exponentially weighted moving average) and reduces the effective dimensionality of the estimation (and hence measurement noise) via techniques borrowed from random matrix theory. We calculate…

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.

The paper calculates VaR and CTE for extreme and aggregate risks using FGM copula.

A new method for exponentially weighted moving models using approximations.

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

New LCM aggregator improves GNN performance and efficiency.

The article is devoted to investigating the application of aggregating algorithms to the problem of the long-term forecasting. We examine the classic aggregating algorithms based on the exponential reweighing. For the general Vovk's aggregating algorithm we provide its generalization for the long-term forecasting. For …