Improves GP expert combination for big data analysis.
problem Scaling Gaussian process experts for big data.
method Transductive combination of independently trained GP experts, providing theoretical justification for gPoE-GP.
result Empirical validation of an improved combination method over gPoE-GP.
Algorithm combines expert forecasts for long-term time series prediction.
problem Long-term time series prediction with expert advice.
method Develops algorithms to combine expert forecasts for long-term prediction, proving adversarial regret bounds.
result Obtains smoothing mechanism to protect against trend changes, noise, and outliers.
In this work, we propose a generalized product of experts (gPoE) framework for combining the predictions of multiple probabilistic models. We identify four desirable properties that are important for scalability, expressiveness and robustness, when learning and inferring with a combination of multiple models. Through a…
Combines expert models using Kullback-Leibler divergence to create a combined model.
problem Combining expert views on stochastic processes.
method Minimizes weighted Kullback-Leibler divergence to create a barycentre model.
result Existence and uniqueness of the barycentre model with explicit representation.
Combines neural networks and expert rules for concept-based learning.
problem Extending concept-based learning with machine learning models.
method Form constraints for joint probability distribution and represent feasible set as a convex polytope.
result Neural networks can be trained to satisfy expert rules without violating them.
New method calibrates Gaussian product experts for better predictions.
problem Erratic predictions and uncalibrated uncertainty in Gaussian product experts.
method Calibration via tempered softmax and Wasserstein barycenter for predictions.
result Improved predictions with better mean and uncertainty quantification.
ARGUE combines expert networks for anomaly detection.
problem Anomaly detection without labeled data.
method Gated mixture-of-experts architecture combining expert networks.
result Prior knowledge about normal data distribution is valuable.
The paper combines Bitcoin price models with expert corrections for better predictions.
problem Improving Bitcoin price predictions using statistical and expert insights.
method Linear regression models combined with expert corrections, utilizing Bayesian approach for fat-tailed distributions.
result Better price prediction results compared to using either model or expert opinion alone.
This paper considers the challenge of evaluating a set of classifiers, as done in shared task evaluations like the KDD Cup or NIST TREC, without expert labels. While expert labels provide the traditional cornerstone for evaluating statistical learners, limited or expensive access to experts represents a practical bottl…
Bayesian models combine experts with a flexible gating mechanism for complex data.
problem Theoretical properties of Bayesian mixture-of-experts models with softmax gating remain unexplored.
method Investigated asymptotic behavior of posterior distribution for density estimation, parameter estimation, and model selection.
result Established posterior contraction rates for density estimation and parameter estimation, providing insights for practical model design.
New method combines adaptive learning rate and flexible prediction for online loss aggregation.
problem Online aggregation of unbounded losses using shifting experts.
method Adapted AdaHedge algorithm with Fixed Share meta-algorithm for signed unbounded losses.
result Improved shifting regret and validity of regret bounds in adversarial setting.
In mixtures-of-experts (ME) model, where a number of submodels (experts) are combined, there have been two longstanding problems: (i) how many experts should be chosen, given the size of the training data? (ii) given the total number of parameters, is it better to use a few very complex experts, or is it better to comb…
BOA improves financial forecasting by combining expert models.
problem Challenges in choosing between multiple machine learning models for financial forecasting.
method Online aggregation of expert models using Bernstein Online Aggregation (BOA) procedure.
result BOA leads to better portfolio performance, higher Sharpe Ratio, and lower shortfall.
SLEEPER combines deep learning with expert rules for accurate sleep staging.
problem Manual sleep staging is tedious and requires expert time.
method SLEEPER uses convolutional neural networks and expert rules to generate interpretable models.
result SLEEPER achieves comparable accuracy to human experts and deep neural networks.
EAML combines human and machine learning for better predictions.
problem Limited data and trust in machine learning models.
method Automated method guiding expert knowledge integration into machine models.
result Improved predictions and generalizability with less data.
Combining distributed Gaussian Processes with deep CNNs improves performance on action recognition.
problem Improving performance on action recognition datasets.
method Combining distributed Gaussian Processes with multi-stream deep CNNs, treating each CNN as an expert and combining predictions using a Product of Experts (PoE) framework.
result Improves performance on HMDB-51 dataset by 0.4\% compared to hand-crafted feature frameworks.
HS-MoE selects sparse experts using adaptive priors and data-adaptive gating.
problem Sparse expert selection in mixture-of-experts architectures.
method Combines horseshoe prior with input-dependent gating for data-adaptive sparsity.
result Data-adaptive sparsity in expert usage.
Expert augmentation improves hybrid model generalization.
problem Limited generalization of hybrid models outside training distribution.
method Introducing expert augmentation to improve hybrid model performance.
result Expert augmentation improves generalization of hybrid models.
Improved cumulative regret for sequence prediction with limited expert advice.
problem Minimizing cumulative regret in sequence prediction with limited information.
method Convex combination of experts with limited observation, achieving constant regret.
result Strategies achieve constant regret independent of the horizon T, improving over standard bounds.
New method combines FMEA and Bayesian Network for root cause analysis in lithium-ion battery production.
problem Complex cause-effect relationships in lithium-ion battery production.
method Combining FMEA with Bayesian Network to detect and resolve inconsistencies.
result Holistic method builds large-scale cross-process Bayesian Failure Network for root cause analysis.
A fast method combines deep mixtures of sparse GPs for flexible modeling.
problem Flexible modeling with changing output densities.
method Designing gating network with DNN for selecting sparse GPs, using CCR algorithm.
result The method outperforms competing methods in accuracy and uncertainty quantification.
The article improves prediction by aggregating Kalman recursions online.
problem Improving expert aggregation in prediction models.
method Using exponential weights and state-space models to aggregate Kalman recursions.
result New algorithms outperform existing methods in Kalman recursion expert aggregation.
Improved time series forecasting with expert loss integration.
problem Enhancing time series forecasting accuracy and efficiency.
method Adaptive Mixture-of-Experts framework with expert-specific loss integration and online learning.
result Significantly improved forecasting accuracy and computational efficiency.
Bayesian method combines expert and user rankings using copulas.
problem Combining expert and user rankings for accurate predictions.
method Bayesian inference with copula modeling latent variables.
result Predictive distribution of user rankings can be approximated accurately.
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…
Hybrid RL learns from expert state sequences without full action data.
problem Learning from expert state sequences without full action data.
method Tensor-based model to infer unobserved actions; hybrid RL objective.
result Hybrid RL outperforms pure RL and tensor-based action inference.
Online L2D algorithm for multiclass classification with varying experts.
problem Handling streaming data, changing expert availability, and shifting expert distribution.
method First online L2D algorithm with O ( ( n + n e ) T 2 / 3 ) O((n+n_e)T^{2/3}) O (( n + n e ) T 2/3 ) and O ( ( n + n e ) T ) O((n+n_e)\sqrt{T}) O (( n + n e ) T ) regret guarantees. result Effective extension of standard L2D to settings with varying expert availability and reliability.
MoE-F combines LLMs online for better time-series prediction.
problem Combining multiple LLMs for online time-series prediction.
method Time-adaptive stochastic filtering techniques to combine experts.
result MoE-F achieves 17% absolute and 48.5% relative F1 measure improvement.
Combines expert knowledge and data for efficient probability distribution inference.
problem Inferring discrete probability distributions using limited data and expert knowledge.
method A novel estimator that weights expert knowledge and empirical data.
result The proposed estimator is always more efficient than either expert or data alone.
A method for interpretable topic discovery using expert knowledge.
problem Discovering interpretable latent topics in text from expert knowledge.
method Combination of information bottleneck and Total Correlation Explanation (CorEx) with relevance variables specified by experts.
result Anchored CorEx produces more coherent and interpretable topics.
We investigate online classification with paid stochastic experts. Here, before making their prediction, each expert must be paid. The amount that we pay each expert directly influences the accuracy of their prediction through some unknown Lipschitz "productivity" function. In each round, the learner must decide how mu…
The paper proposes a new model to capture specialized brain regions using mixture of regression experts.
problem Learning a forward mapping that relates stimuli to brain activation assumes all regions respond similarly, ignoring brain specialization.
method Clustering brain regions, learning different linear regression models for each cluster, using a mixture of linear experts.
result The proposed model predicts brain activation more accurately than conventional models.
A new model for Gaussian process experts tackles scalability and uncertainty issues.
problem Scalability and excessive number of experts degrade predictive performance and increase uncertainty.
method Nested partitioning scheme infers the number of components, a generalised GP framework accommodates multiple response types, and a factorised exponential family structure handles multiple input types.
result Effectiveness demonstrated on synthetic data and an Alzheimer's challenge dataset.
When dealing with time series with complex non-stationarities, low retrospective regret on individual realizations is a more appropriate goal than low prospective risk in expectation. Online learning algorithms provide powerful guarantees of this form, and have often been proposed for use with non-stationary processes …
Reinforcement learning improves online matching by combining expert policies.
problem Efficient decision-making in complex systems like cloud services and marketplaces.
method Combines reinforcement learning with expert policies, using advantage-based weight updates.
result The orchestrated policy converges faster and yields higher efficiency than individual experts and conventional RL.
A mixture of experts model predicts brain activation from word stimuli.
problem Classical encoding models ignore connections among brain regions.
method Mixture of experts capturing ROI-specific brain activity patterns.
result Model predicts entire brain activation with high spatial accuracy.
A new tracking method using expert selection and feature fusion.
problem Efficient visual tracking with multiple component trackers.
method Pre-event selection of experts based on past performance and feature fusion.
result Superior performance compared to ensembled trackers on public datasets.
A method merges two pretrained diffusion experts to improve image quality and likelihood.
problem Trade-off between image quality and data likelihood in diffusion models.
method Combining two pretrained diffusion experts by switching between them along the denoising trajectory.
result The merged model consistently matches or outperforms its base components, improving or preserving both likelihood and sample quality.
Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utility-based framework is established for their analysis. This differs from the usual approach of defining static betting functions. It is sho…
FCMs model feedback in complex systems, combining expert knowledge and statistical learning.
problem Modeling feedback in complex, interconnected systems.
method Fuzzy cognitive maps (FCMs) as fuzzy signed directed graphs, allowing for feedback loops and nonlinear dynamics.
result FCMs can better represent domain knowledge and simulate policy scenarios compared to DAGs.
Paper optimizes combining expert predictions using CRPS loss.
problem Optimizing combining expert predictions in online learning.
method Combines probabilistic forecasts using CRPS loss function in the prediction with expert advice framework.
result Time-independent upper bound for the regret of the Vovk's aggregating algorithm using CRPS as a loss function is obtained.
New framework combines imitation and reinforcement learning for faster, cheaper decision-making.
problem Sequential decision-making with sparse rewards and long time horizons.
method Hierarchical guidance framework integrating imitation and reinforcement learning at different levels.
result Significantly faster and more label-efficient learning compared to existing methods.
Paper uses a mix of deep and kernel learning to personalize sepsis treatment.
problem Managing sepsis in ICU patients due to individual variability.
method A mixture-of-experts framework combining kernel-based and deep reinforcement learning.
result The mixture-based approach outperforms individual methods on a large sepsis patient cohort.
Combines parametric and nonparametric models for better off-policy evaluation.
problem Improving off-policy evaluation in reinforcement learning.
method Mixture-of-experts approach combining parametric and nonparametric models.
result Mixture-based approach outperforms individual models and state-of-the-art estimators.
L2D-CD learns to defer expert recommendations in causal discovery.
problem Combining expert knowledge with data-driven results in causal discovery when expert recommendations may contradict data.
method Adapting learning-to-defer algorithms for pairwise causal discovery, L2D-CD learns a deferral function to select between expert recommendations and data-driven methods.
result L2D-CD outperforms both causal discovery methods and the expert used in isolation, identifying domains where the expert's performance is strong or weak.
Improved text-conditioned regression using LLMs and diffusion-based neural processes.
problem Major error cascades and computational inefficiency in LLMs for short sequences.
method Combining LLM predictive densities with a diffusion-based neural process.
result Better-calibrated predictions and locally consistent trajectories.
Improves accuracy and fairness in prediction systems with multiple domain experts.
problem Designing unbiased and accurate deferral systems with multiple experts.
method Proposes a framework for learning a classifier and deferral system that chooses to defer to multiple human experts.
result Significantly improves accuracy and fairness of final predictions compared to baselines.
A scalable Gaussian process model using a mixture-of-experts approach.
problem Training Gaussian process models is computationally expensive and not scalable.
method A mixture-of-experts model with low-dimensional matrix inversions and importance sampling.
result The model offers comparable performance to Gaussian process regression at a lower computational cost.