arXiv research

Self-regularizing RBMs learn optimal hidden units efficiently.

In the domain of the so called Econophysics some attempts already have been made for applying the theory of Thermodynamics and Statistical Mechanics to economics and financial markets. In this paper a similar approach is made from a different perspective, trying to model the limit order book and price formation process…

In this paper we study the continuum time dynamics of a stock in a market where agents behavior is modeled by a Minority Game and a Grand Canonical Minority Game. The dynamics derived is a generalized geometric Brownian motion; from the Black & Scholes formula the calibration of both the Minority Game and the Grand Can…

Bayesian inference reconstructs external potentials in DFT for many-particle systems.

We study analytically and numerically Minority Games in which agents may invest in different assets (or markets), considering both the canonical and the grand-canonical versions. We find that the likelihood of agents trading in a given asset depends on the relative amount of information available in that market. More s…

We use variational Gaussian approximations to analyze parametric models with unknown data-generating distributions.

In this paper the extended model of Minority game (MG), incorporating variable number of agents and therefore called Grand Canonical, is used for prediction. We proved that the best MG-based predictor is constituted by a tremendously degenerated system, when only one agent is involved. The prediction is the most effici…

Bayesian inference learns free energy landscapes from experimental data.

The existence of a phase transition with diverging susceptibility in batch Minority Games (MGs) is the mark of informationally efficient regimes and is linked to the specifics of the agents' learning rules. Here we study how the standard scenario is affected in a mixed population game in which agents with the `optimal'…

In lowest unique bid auctions, $N$ players bid for an item. The winner is whoever places the \emph{lowest} bid, provided that it is also unique. We use a grand canonical approach to derive an analytical expression for the equilibrium distribution of strategies. We then study the properties of the solution as a function…

Multi-headed ensembles boost model performance with faster training.

Ensemble learning is a methodology that integrates multiple DNN learners for improving prediction performance of individual learners. Diversity is greater when the errors of the ensemble prediction is more uniformly distributed. Greater diversity is highly correlated with the increase in ensemble accuracy. Another attr…

Transforms ensemble predictions to maintain interpretability.

Overparameterized ensembles don't offer generalization benefits over single large models.

New method creates diverse neural ensembles for better uncertainty estimation and robustness.

New method certifies joint adversarial robustness of model ensembles.

Embedded ensembles improve neural network performance efficiently.

A framework preserves uncertainty in ensemble distillation.

Study pitfalls of deep learning ensembles in uncertainty estimation.

New hyperparameter ensembles boost neural network performance and uncertainty.

ECV method optimizes ensemble parameters for randomized ensembles.

The paper explores dynamic ensembles for multi-step forecasting.

Bayesian deep ensembles improve prediction accuracy in various settings.

RCAM-based ensemble combines binary classifiers using similarity and vote scheme.

Heterogeneous ensembles built from the predictions of a wide variety and large number of diverse base predictors represent a potent approach to building predictive models for problems where the ideal base/individual predictor may not be obvious. Ensemble selection is an especially promising approach here, not only for …

High-capacity neural network ensembles often benefit more from high-capacity models than from increased diversity.

New method reduces ensemble size for linear bandits, achieving near optimal regret.

Jointly tuning ensemble models improves performance and uncertainty calibration.

Proposes a new method for deep ensembles that improves accuracy and calibration.

Paper analyzes uncertainty metrics in ensemble learning for healthcare AI.

Self-paced ensemble learning improves audio classification models.

We propose and evaluate alternative ensemble schemes for a new instance based learning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fuses instances into spheres, then bases classification on distance to spheres rather than distance to instances. The randomised nature of RSC makes it ideal for use in en…

Hydra distills ensemble models into a single model while preserving diversity and uncertainty.

A fast method estimates stability of ensemble feature selectors.

Ensemble++ uses shared-factor ensembles to scale Thompson Sampling for linear and nonlinear bandits.

Ensemble unsupervised anomaly detection using IRT for hidden ground truth.

New method improves ensemble quality by exploring the pre-train basin more effectively.

RobBoost optimizes a deep model ensemble for certified robustness.

Ensembles of deep neural networks significantly improve generalization accuracy. However, training neural network ensembles requires a large amount of computational resources and time. State-of-the-art approaches either train all networks from scratch leading to prohibitive training cost that allows only very small ens…

Efficient neural network ensembles improve image classification reliability and uncertainty quantification.

Paper analyzes ensemble Kalman updates for effective dimension and localization.

Ensembling DNNs improves minority group performance, leading to fairness.

Repulsive deep ensembles improve diversity and Bayesian inference.

This research tackles uncertainty in gradient boosting models using ensemble methods.

Fibonacci Ensembles use Fibonacci weights to improve ensemble learning, inspired by natural growth patterns.

Ensembling improves performance when classifiers disagree more than average.

Ensembles of models often yield improvements in system performance. These ensemble approaches have also been empirically shown to yield robust measures of uncertainty, and are capable of distinguishing between different \emph{forms} of uncertainty. However, ensembles come at a computational and memory cost which may be…

Ens-CGP synthesizes ensemble-based inference with Gaussian processes.