On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,341 papers · 148 categories

Trend · papers per month

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48 results for “Grand-Canonical Ensemble”

Limit Order Book and its modelling in terms of Gibbs Grand-Canonical Ensembleq-fin.TR

Paper models stock price formation using Gibbs Grand-Canonical Ensemble.

problem Modeling stock price formation and temperature differences.

method Using Gibbs Grand-Canonical Ensemble, defining temperatures for bid and ask orders.

result Temperature difference between bid and ask orders correlates with VAO indicator.

Econophysics Thermodynamics Statistical Mechanics Limit Order Book Grand-Canonical Ensemble

2016-01-24abs ↗pdf ↗

Self-regularizing restricted Boltzmann machinescond-mat.dis-nn

Self-regularizing RBMs learn optimal hidden units efficiently.

problem Learning optimal number of hidden units in RBMs.

method Grand-canonical extension of RBMs with varying hidden units, using chemical potential to control size.

result Efficiently deduces optimal number of hidden units with small generalization error.

Restricted Boltzmann Machines Optimal Hidden Units Grand-canonical Ensemble Chemical Potential Mean-Field Theory

2019-12-09abs ↗pdf ↗

From Minority Game to Black & Scholes pricingq-fin.TR

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…

2012-05-11abs ↗pdf ↗

Physics-informed Bayesian inference of external potentials in classical density-functional theorycond-mat.stat-mech

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

problem Reconstructing external potentials in classical density-functional theory (DFT) for many-particle systems.

method Combines Bayesian inference with classical DFT to probabilistically reconstruct external potentials.

result Accurately infers external potentials and density profiles with uncertainty quantification.

Bayesian inference Density-functional theory Machine learning External potentials Many-particle systems

2023-09-13abs ↗pdf ↗

Multi-asset minority gamesphysics.soc-ph

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…

2006-03-19abs ↗pdf ↗

Variational Gaussian Approximation in Replica Analysis of Parametric Modelscond-mat.dis-nn

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

problem Analyzing inference and learning in parametric models with unknown or intractable data-generating distributions.

method Replica method with variational Gaussian approximation in grand canonical formalism.

result Stationarity conditions adaptively determine parameters of the trial Hamiltonian for each dataset.

replica method variational Gaussian approximation parametric models grand canonical formalism learning curves

2025-09-15abs ↗pdf ↗

Grand canonical minority game as a sign predictorstat.AP

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…

2013-09-13abs ↗pdf ↗

Physics-constrained Bayesian inference of state functions in classical density-functional theorycond-mat.stat-mech

Bayesian inference learns free energy landscapes from experimental data.

problem Characterize the free energy landscape of classical many-body systems from experimental data.

method Combines non-parametric Bayesian inference with physically-motivated constraints to automate the construction of approximate free energy functionals.

result Inference algorithms yield a probability distribution over free energy functionals, leading to highly accurate analytic expressions.

Bayesian inference statistical mechanics free energy many-body systems data-driven

2020-10-07abs ↗pdf ↗

On the transition to efficiency in Minority Gamesphysics.soc-ph

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'…

2007-12-03abs ↗pdf ↗

Equilibrium strategy and population-size effects in lowest unique bid auctionscs.GT

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…

2011-04-30abs ↗pdf ↗

Multi-headed Neural Ensemble Searchcs.LG

Multi-headed ensembles boost model performance with faster training.

problem Limited computational resources hinder ensemble search performance.

method Extend NES to multi-headed ensembles, leveraging end-to-end training and one-shot NAS methods.

result Multi-headed ensemble search finds robust ensembles 3 times faster with comparable performance.

Deep Ensembles Neural Ensemble Search Multi-headed Ensembles End-to-End Training One-shot NAS

2021-07-09abs ↗pdf ↗

Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustnesscs.LG

This paper explores how diverse neural network ensembles improve prediction accuracy and robustness against deception.

problem Improving prediction accuracy and robustness of neural networks against adversarial attacks.

method Examines and measures ensemble diversity, develops algorithms for creating and combining diverse ensembles.

result Greater diversity in neural network ensembles leads to higher accuracy and robustness against deception.

ensemble learning deep neural networks deception robustness diversity

2019-08-29abs ↗pdf ↗

Deep interpretable ensemblesstat.ML

Transforms ensemble predictions to maintain interpretability.

problem Loss of interpretability in deep ensembles.

method Proposes transformation ensembles that aggregate predictions while preserving interpretability.

result Transformation ensembles yield better predictions than individual models and maintain interpretability.

ensemble learning interpretability deep learning uncertainty quantification minimax optimality

2022-05-25abs ↗pdf ↗

Theoretical Limitations of Ensembles in the Age of Overparameterizationstat.ML

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

problem Theoretical limitations of ensembles in overparameterized settings.

method Using ensembles of random feature (RF) regressors, the paper clarifies how modern ensembles differ from underparameterized counterparts.

result Infinite ensembles of overparameterized RF regressors become pointwise equivalent to single infinite-width RF regressors, and finite width ensembles converge to single models with the same parameter budget.

overparameterization ensemble generalization random feature neural networks

2024-10-21abs ↗pdf ↗

Neural Ensemble Search for Uncertainty Estimation and Dataset Shiftcs.LG

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

problem Creating more robust neural networks for uncertainty estimation and dataset shift.

method Automatically constructing ensembles with varying architectures.

result Ensembles with varying architectures outperform deep ensembles in accuracy, uncertainty calibration, and robustness.

neural ensembles uncertainty estimation dataset shift architecture search reproducibility

2020-06-15abs ↗pdf ↗

Certifying Joint Adversarial Robustness for Model Ensemblescs.LG

New method certifies joint adversarial robustness of model ensembles.

problem Ensuring robustness of model ensembles against adversarial attacks.

method Proposes a novel technique to certify joint robustness, building on prior work on single-model robustness certification.

result Demonstrates the effectiveness of certifying joint robustness of ensembles, improving understanding of ensemble defenses.

adversarial robustness model ensembles certification disjoint vulnerability spaces cost-sensitive training

2020-04-21abs ↗pdf ↗

Embedded Ensembles: Infinite Width Limit and Operating Regimesstat.ML

Embedded ensembles improve neural network performance efficiently.

problem Improving neural network performance with fewer resources.

method Analyzing the wide network limit of gradient descent dynamics using Neural-Tangent-Kernel.

result Embedded ensembles exhibit two regimes: independent and collective, affecting performance.

Embedded Ensembling Wide Network Limit Gradient Descent Independent Regime Collective Regime

2022-02-24abs ↗pdf ↗

Developing parsimonious ensembles using ensemble diversity within a reinforcement learning frameworkcs.LG

New algorithms improve ensemble diversity, leading to more accurate and smaller models.

problem Building accurate predictive models with diverse base predictors.

method Integrates ensemble diversity into a reinforcement learning framework for ensemble selection.

result Diversity-incorporating ensembles are more accurate and smaller in size.

ensemble diversity reinforcement learning predictive modeling model parsimony

2018-05-05abs ↗pdf ↗

A general framework for ensemble distribution distillationstat.ML

A framework preserves uncertainty in ensemble distillation.

problem Preserving uncertainty decomposition in ensemble distillation.

method General framework for distilling both regression and classification ensembles, preserving natural uncertainty decomposition.

result Framework maintains decomposition of predictive uncertainty.

ensemble distillation uncertainty decomposition neural networks predictive performance

2020-02-26abs ↗pdf ↗

Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learningstat.ML

Study pitfalls of deep learning ensembles in uncertainty estimation.

problem Pitfalls in in-domain uncertainty estimation and ensembling in deep learning.

method Exploration of standards for uncertainty quantification and broad study of ensembling techniques.

result Many sophisticated ensembling techniques are equivalent to a simple ensemble of few networks.

deep learning uncertainty estimation ensemble methods image classification pitfalls

2020-02-15abs ↗pdf ↗

Hyperparameter Ensembles for Robustness and Uncertainty Quantificationcs.LG

New hyperparameter ensembles boost neural network performance and uncertainty.

problem Improving neural network robustness and uncertainty quantification.

method Designing ensembles over both weights and hyperparameters, stratified across random initializations.

result Hyper-deep and hyper-batch ensembles outperform deep and batch ensembles on various architectures.

neural networks hyperparameter optimization ensemble learning robustness uncertainty quantification

2020-06-24abs ↗pdf ↗

InfiniteBoost: building infinite ensembles with gradient descentstat.ML

InfiniteBoost builds an infinite ensemble using gradient descent.

problem Building high accuracy ensembles for various machine learning tasks.

method Combines gradient boosting and random forests properties using gradient descent to create an infinite ensemble.

result InfiniteBoost achieves high accuracy on regression, classification, and ranking tasks.

gradient descent ensemble methods infinite ensembles machine learning gradient boosting

2017-06-04abs ↗pdf ↗

Ensemble Pruning via Margin Maximizationstat.ML

Proposes a method to reduce ensemble size while maintaining accuracy.

problem Complexity and computational burden of ensemble models in large-scale data.

method Optimizes margin distribution to reduce ensemble size while increasing diversity.

result Pruned ensemble uses only a fraction of original classifiers with improved or similar generalization performance.

ensemble pruning margin maximization ensemble models computational burden predictive accuracy

2019-06-07abs ↗pdf ↗

Extrapolated cross-validation for randomized ensemblesstat.ME

ECV method optimizes ensemble parameters for randomized ensembles.

problem Efficient tuning of ensemble parameters in randomized ensembles.

method ECV (Extrapolated Cross-Validation) method for tuning ensemble and subsample sizes.

result ECV yields δ-optimal ensembles for squared prediction risk.

ensemble methods cross-validation randomized ensembles prediction risk surface protein abundances

2023-02-27abs ↗pdf ↗

Ensemble Distribution Distillationstat.ML

EnD $^2$ distills ensemble diversity into a single model.

problem Loss of ensemble diversity information in distillation.

method Prior Networks for distilling ensemble distribution into a single model.

result EnD

^2

retains both ensemble performance and diversity information.

ensemble distillation distribution distillation prior networks ensemble diversity classification performance

2019-04-30abs ↗pdf ↗

Multi-output Ensembles for Multi-step Forecastingstat.ML

The paper explores dynamic ensembles for multi-step forecasting.

problem Lack of research on dynamic ensembles for multi-step forecasting.

method Extensive experiments with 3568 time series and an ensemble of 30 multi-output models.

result Dynamic ensembles based on arbitrating and windowing perform best.

forecasting multi-step dynamic ensembles multi-output ensemble methods

2023-06-26abs ↗pdf ↗

A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generationstat.AP

A new ensemble technique improves power forecasting for renewable energy.

problem Improving accuracy in power forecasting for renewable energy.

method Coopetitive soft-gating in a multi-scheme ensemble.

result The technique outperforms other models on publicly available data sets.

power forecasting renewable energy ensemble methods coopetitive soft-gating weather forecasting

2018-03-16abs ↗pdf ↗

Bayesian Deep Ensembles via the Neural Tangent Kernelstat.ML

Bayesian deep ensembles improve prediction accuracy in various settings.

problem Improving prediction accuracy of deep ensembles in out-of-distribution settings.

method Introducing a randomised, untrainable function to each ensemble member, enabling a posterior predictive distribution interpretation.

result Bayesian deep ensembles make more conservative predictions and outperform standard ensembles in various tasks.

Bayesian deep ensembles Neural Tangent Kernel out-of-distribution prediction accuracy conservative predictions

2020-07-11abs ↗pdf ↗

Ensemble of Binary Classifiers Combined Using Recurrent Correlation Associative Memoriescs.LG

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

problem Improving binary classification accuracy through ensemble methods.

method RCAM-based ensemble combining classifiers using similarity and recurrent consult-vote scheme.

result RCAM-based ensemble outperforms individual classifiers and majority voting.

ensemble learning binary classification associative memory recurrent correlation majority vote

2020-09-18abs ↗pdf ↗

Making Tree Ensembles Interpretablestat.ML

The paper simplifies complex tree ensembles for better interpretability.

problem Limited interpretability of tree ensembles like random forest and boosted trees.

method A post-processing method that approximates complex tree ensembles with a simpler, interpretable model using the EM algorithm.

result Complex tree ensembles can be approximated reasonably by simpler, interpretable models.

tree ensembles interpretability random forest boosted trees EM algorithm

2016-06-17abs ↗pdf ↗

Pathologies of Predictive Diversity in Deep Ensemblescs.LG

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

problem The performance of high-capacity neural network ensembles is often harmed by interventions that promote predictive diversity.

method A large-scale study of nearly 600 neural network classification ensembles, examining various interventions and architectures.

result Discouraging predictive diversity can be benign in large-network ensembles, and higher-capacity models often yield better performance than diverse architectures.

deep ensembles predictive diversity high-capacity models neural networks ensemble learning

2023-02-01abs ↗pdf ↗

MotherNets: Rapid Deep Ensemble Learningcs.LG

MotherNets speed up deep ensemble learning with shared costs.

problem Training deep neural network ensembles is computationally expensive and time-consuming.

method MotherNets train a single or small set of MotherNets to share data movement and computation costs across ensembles, generating target ensembles by transferring function from MotherNets.

result MotherNets provide a new Pareto frontier for accuracy vs. training cost, scaling improvements as ensemble size increases.

deep learning ensemble learning MotherNets training cost accuracy

2018-09-12abs ↗pdf ↗

Pooling homogeneous ensembles to build heterogeneous onescs.LG

Combines classifiers from different types to improve ensemble accuracy.

problem Improving ensemble accuracy by combining classifiers of different types.

method Builds heterogeneous ensembles by pooling classifiers from multiple homogeneous ensembles, using cross-validation or out-of-bag data for optimal composition.

result Optimal heterogeneous ensemble compositions can be determined using cross-validation or out-of-bag data.

ensemble methods heterogeneous ensembles classifier combination cross-validation bootstrap samples

2018-02-21abs ↗pdf ↗

On Joint Regularization and Calibration in Deep Ensemblescs.LG

Jointly tuning ensemble models improves performance and uncertainty calibration.

problem Improving both predictive performance and uncertainty calibration in deep ensembles.

method Investigated the impact of jointly tuning weight decay, temperature scaling, and early stopping.

result Jointly tuning ensemble models generally matches or improves performance, with significant variation across tasks.

deep ensembles ensemble optimization joint tuning uncertainty calibration machine learning

2025-11-06abs ↗pdf ↗

Ensemble sampling for linear bandits: small ensembles sufficestat.ML

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

problem Achieving near optimal regret in linear bandits with limited ensemble size.

method Ensemble sampling with a size of order

d \log T

for a

d

-dimensional stochastic linear bandit.

result Regret is at most

(d \log T)^{5/2} \sqrt{T}

, improving over linear scaling with

T

ensemble sampling linear bandits stochastic regret minimization logarithmic scaling

2023-11-14abs ↗pdf ↗

Weighted Clustering Ensemble: A Reviewcs.CV

This paper reviews weighted clustering ensemble methods.

problem Improving clustering results from individual methods.

method Different types of weights and approaches to determining weight values.

result Unified framework for selecting appropriate weighting mechanisms.

clustering ensemble weighted clustering consensus clustering ill-posed problem supervised domain

2019-10-06abs ↗pdf ↗

Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AIcs.LG

Paper analyzes uncertainty metrics in ensemble learning for healthcare AI.

problem Selecting appropriate uncertainty metrics for ensemble learners in healthcare AI.

method Rigorous analysis of two uncertainty metrics: ensemble mean and variance.

result Ensemble mean is preferable to ensemble variance for decision making in healthcare AI.

ensemble learning uncertainty metrics healthcare AI diabetic retinopathy machine learning

2020-07-12abs ↗pdf ↗

Improving Deep Ensembles by Estimating Confusion Matricescs.LG

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

problem Improving accuracy and calibration of deep ensembles.

method Estimates confusion matrices of ensemble members and weighs them according to their inferred performance.

result Empirically shows superiority of soft Dawid Skene over ensemble averaging.

deep learning ensemble methods crowdsourcing confusion matrices soft labels

2025-03-10abs ↗pdf ↗

Self-paced ensemble learning for speech and audio classificationcs.SD

Self-paced ensemble learning improves audio classification models.

problem Improving performance of individual models in speech and audio classification.

method A self-paced ensemble learning scheme where models learn from each other over several iterations.

result SPEL significantly outperforms baseline ensemble models.

ensemble learning self-paced speech recognition audio classification pseudo-labeling

2021-03-22abs ↗pdf ↗

Hydra: Preserving Ensemble Diversity for Model Distillationcs.LG

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

problem Loss of ensemble diversity and uncertainty in distilled models.

method Single multi-headed neural network with shared body network.

result Hydra improves distillation performance and preserves ensemble diversity and uncertainty.

ensemble distillation model compression uncertainty quantification multi-headed neural networks

2020-01-14abs ↗pdf ↗

Ensembles of Random Sphere Cover Classifierscs.LG

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…

2014-09-17abs ↗pdf ↗

Fast Estimation Method for the Stability of Ensemble Feature Selectorscs.LG

A fast method estimates stability of ensemble feature selectors.

problem Improving stability of ensemble feature selectors for better prediction.

method Simulator of a feature selector to estimate stability.

result Reduces computation time for estimating stability.

feature selection ensemble methods stability fast estimation computational cost

2021-08-03abs ↗pdf ↗

Ensemble Pruning based on Objection Maximization with a General Distributed Frameworkcs.LG

A new method balances accuracy and diversity in ensemble pruning.

problem Balancing accuracy and diversity in ensemble learning.

method Formalizing ensemble pruning as an objection maximization problem based on information entropy, proposing a distributed framework.

result Achieves less time-consuming execution with minimal accuracy degradation.

ensemble pruning objectives maximization distributed framework information entropy accuracy

2018-06-13abs ↗pdf ↗

Scalable Exploration via Ensemble++cs.LG

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

problem Computational challenges in Thompson Sampling for large-scale or non-conjugate settings.

method Ensemble++ with shared-factor architecture and random linear combinations.

result Ensemble++ achieves comparable regret to exact Thompson Sampling with significantly smaller ensemble sizes.

Thompson Sampling ensemble methods bandits regret minimization scalability

2024-07-18abs ↗pdf ↗

Unsupervised Anomaly Detection Ensembles using Item Response Theorystat.ML

Ensemble unsupervised anomaly detection using IRT for hidden ground truth.

problem Challenges in constructing an ensemble from unsupervised anomaly detection methods.

method Use Item Response Theory to compute latent traits and construct an ensemble that downplays noisy methods.

result Demonstrated effectiveness of IRT ensemble on extensive data repository.

unsupervised anomaly detection ensemble methods Item Response Theory latent trait anomaly detection

2021-06-11abs ↗pdf ↗

To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learningcs.LG

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

problem Limited diversity in ensembles trained from a single pre-trained checkpoint.

method Proposed StarSSE modification of Snapshot Ensembles for transfer learning.

result Stronger ensembles and uniform model soups achieved.

transfer learning ensembles pre-train basin Snapshot Ensembles StarSSE

2023-03-06abs ↗pdf ↗

Enhancing Certifiable Robustness via a Deep Model Ensemblestat.ML

RobBoost optimizes a deep model ensemble for certified robustness.

problem Improving the robustness of deep models.

method Optimizes a neural network's robustness certificate through model selection and weighting.

result RobBoost forms a more robust ensemble with better certified robustness and clean accuracy.

deep learning certified robustness model ensemble optimization MNIST

2019-10-31abs ↗pdf ↗

Fast and reliable uncertainty quantification with neural network ensembles for industrial image classificationcs.LG

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

problem Uncertainty in neural network predictions for industrial image classification.

method Investigated efficient neural network ensembles (snapshot, batch, multi-input multi-output) for image classification reliability and uncertainty quantification.

result Batch ensemble is a cost-effective and competitive alternative to deep ensembles, offering savings in training and test time.

neural networks image classification uncertainty quantification efficient ensembles batch ensemble

2024-03-15abs ↗pdf ↗