This paper reviews forecast combinations over 50 years, highlighting their evolution and utility.
problem Improving forecast accuracy through combining multiple forecasts.
method Evolution of forecast combination methods, from simple to sophisticated.
result Forecast combinations have become a mainstream approach in forecasting.
Researchers combined linear classifiers using score functions and found simple and trimmed averages to be the best combination strategies.
problem Combining linear classifiers using their score functions.
method Two score functions tested; four combination strategies investigated; comparison with majority voting and model averaging.
result Simple and trimmed average combination strategies were the best.
Unified framework combines dependent microbiome tests.
problem Combining dependent microbiome association tests.
method Generalized meta-analysis framework for dependent tests.
result The vanilla Cauchy combination is a special case.
New Klein-Maskit theorems for Anosov subgroups.
problem Creating new Kleinian groups from old ones.
method Analogous combination theorems for Anosov subgroups.
result Construct new Kleinian groups using old ones for Anosov subgroups.
Generative model designs drug combinations for improved efficacy and reduced side effects.
problem Designing effective drug combinations to overcome resistance and reduce side effects.
method Developed a deep generative model using HVGAE and a novel reward system.
result Network-principled drug combinations show reduced toxicity and potential for new strategies.
Deep learning predicts synergistic drug combinations from multi-omics data.
problem Predicting effective drug combinations to overcome cancer drug resistance.
method AuDNNsynergy model integrating gene expression, copy number, genetic mutation data and drug properties.
result AuDNNsynergy model outperforms state-of-the-art approaches.
We prove a combination theorem for trees of (strongly) relatively hyperbolic spaces and finite graphs of (strongly) relatively hyperbolic groups. This gives a geometric extension of Bestvina and Feighn's Combination Theorem for hyperbolic groups and answers a question of Swarup. We also prove a converse to the main Com…
MAC combines models without locking them, improving ensemble performance.
problem Improving ensemble learning performance with flexibility.
method Model agnostic combination technique that dynamically combines models.
result MAC outperforms classical methods and competitive to boosting.
Survey of combination theorems in geometry and dynamics.
problem Combination theorems in hyperbolic geometry, group theory, and dynamics.
method Survey and focus on Thurston's contributions.
result Thurston's influence on combination theorems.
Combines relatively hyperbolic groups over a complex.
problem Combining relatively hyperbolic groups.
method Generalization of Martin's theorem for hyperbolic groups.
result Combination theorem for relatively hyperbolic groups.
Combines VaR and ES forecasts from a large pool of methods.
problem Combining forecasts from a large pool of VaR and ES methods.
method Adapted interval forecast combination methods, including trimmed means and mixtures approach.
result Trimmed mean combinations, mixtures method, and performance-based weighting delivered strong results.
Study improves drug synergy prediction using ensemble learning.
problem Predicting drug synergy in complex diseases.
method Investigated different compound representations and proposed an ensemble model.
result Ensemble model outperforms baseline models.
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.
The paper proposes a method to improve forecast combination accuracy using portfolio theory.
problem Improving forecast accuracy by combining multiple forecasts.
method Generates forecast combinations using a portfolio analogy, allowing negative weights for hedging.
result Demonstrates improved performance in weighted random forest forecasts.
Improved language identification accuracy through signal combination methods.
problem Enhancing speech recognition accuracy across multiple languages.
method Combining low-level acoustic signals with language-specific recognizer signals using lattice-based ensemble models and deep neural networks.
result Deep neural network model outperforms lattice-based ensemble model, reducing error rate from 5.5% to 4.3%.
A new method forecasts financial tail risks by combining and weighting quantiles.
problem Reducing uncertainty in financial tail risk forecasting.
method Two-step procedure: quantile combination followed by ES computation.
result The proposed framework outperforms individual models and simple approaches.
Combines response categories in multinomial logistic regression models.
problem Handling multiple response categories in logistic regression models.
method Penalized likelihood method with alternating direction method of multipliers.
result Encourages response category combination in the model.
Synthetic Combinations learns unit-specific causal outcomes for combinatorial interventions.
problem Estimating unit-specific causal outcomes for all combinations of p interventions in a heterogeneous setting. method Latent factor model with Fourier expansion sparsity, imposing structure across units and interventions.
result Synthetic Combinations provides consistent estimation with poly(r) * (N + s^2p) observations, outperforming previous methods.
New method combines score lists using joint CDFs, improving computation.
problem Combining non-comparable score lists over a common index set.
method New algorithm for computing joint CDF values, linear runtime.
result Improved computation of joint CDF values for N-dimensional order statistics.
Combines human and model predictions for improved accuracy.
problem Improving classification accuracy when both human and model predictions are imperfect.
method Uses confusion matrices and calibration to combine probabilistic model outputs with human class-level predictions.
result Human-model combinations consistently outperform either alone, with accuracy gains even with limited human input.
The abstract discusses combining risk measures without restrictions.
problem Developing a theory for combinations of risk measures under no restrictions.
method Developing and discussing results regarding preservation of properties and acceptance sets for combinations of risk measures.
result Representation of resulting risk measures from the properties of alternative functionals and combination functions.
New method improves Variational Auto-Encoders using convex combination of Inverse Autoregressive Flows.
problem Improving Variational Auto-Encoders (VAEs) for better performance.
method Introducing multiple lower-triangular matrices with ones on the diagonal and combining them using a convex combination to enrich a linear Inverse Autoregressive Flow.
result The proposed method outperforms other volume-preserving flows and is competitive with state-of-the-art linear normalizing flows.
Combines VaR and ES forecasts for cryptocurrency market risk management.
problem Improving tail risk forecasts in financial markets.
method Proposes semiparametric and parametric combination frameworks.
result Combined forecasts outperform individual VaR and ES forecasts.
Combines experience replay techniques to improve reinforcement learning.
problem Improving reinforcement learning algorithms.
method Combines CER, PER, and HER with DDPG and DQN.
result Effective combinations of these techniques in various environments.
NCoRE learns counterfactual representations for combined treatments.
problem Estimating individual response to multiple simultaneous interventions.
method Neural conditional representation with modulators for cross-treatment interactions.
result NCoRE significantly outperforms existing methods in counterfactual treatment effect estimation.
Paper introduces a new SVR model using a combined reward and penalty loss function.
problem Regression problem, particularly handling data points outside and inside ε-tube.
method Combined reward cum penalty loss function to penalize and reward data points.
result Experimental results support the model's properties and effectiveness.
Develops a new method for efficient probabilistic inference.
problem Efficient inference for models with dynamic computation graphs.
method Introduces combinator library for Probabilistic Torch framework.
result Models can be trained using stochastic methods that optimize variational or wake-sleep objectives.
Develops inference combinators for probabilistic programs using neural networks.
problem Creating efficient proposals for probabilistic program inference.
method Inference combinators using neural network parameterization of proposals.
result Correct by construction variational methods tailored to specific models.
We show in this short note that if a rational linear combination of Pontrjagin numbers vanishes on all simply-connected 4k-dimensional closed connected and oriented spin manifolds admitting a Riemannian metric whose Ricci curvature is nonnegative and nonzero at any point, then this linear combination must be a multip…
Combination theorem for PGF groups helps in constructing new examples and understanding their geometry.
problem Understanding the geometry of PGF groups and their combinations.
method Utilizing subsurface projection to control the geometry of fundamental groups of graphs of PGF groups.
result Combination theorem for PGF groups and other generalizations.
SEA model predicts heat demand combining neural network and ARIMA.
problem Predicting heat demand with periodicity.
method Combining Elman neural network and ARIMA models for seasonal and trend predictions.
result SEA model shows promising performance in heat demand prediction.
New methods combine model predictions to avoid linear mixtures' limitations.
problem Combining predictions from different models to avoid linear mixtures' limitations.
method Log-linear pooling (locking) and quantum superposition (quacking) to optimise model weights.
result Demonstrated locking method with illustrative example and practical application.
New methods predict drug interactions using drug co-medication patterns and graph matching.
problem Predicting adverse drug reactions from drug combinations.
method Developed novel kernels over drug combinations using support vector machines and graph matching to measure similarities.
result Achieved an AUC of 0.912 on a real-world dataset.
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 datasets to improve model fitting with small sample sizes.
problem Improving model performance with limited samples from at least one dataset.
method Proposes a novel framework called Combine datasets based on Imputation (ComImp) and PCA-ComImp for combining datasets with missing data.
result Significant improvement in model accuracy, especially with small datasets and when combined with transfer learning.
New GM functions improve classifier ensemble accuracy.
problem Improving classifier ensemble accuracy.
method Using generalized mixture functions with dynamic weights.
result Gains in performance compared to traditional methods.
Estimating statistical models within sensor networks requires distributed algorithms, in which both data and computation are distributed across the nodes of the network. We propose a general approach for distributed learning based on combining local estimators defined by pseudo-likelihood components, encompassing a num…
Combines linear and spectral estimators for signal recovery in generalized linear models.
problem Signal recovery from generalized linear models with Gaussian sensing matrix.
method Optimal combination of a linear estimator and a spectral estimator using an AMP algorithm.
result Bayes-optimal combination of estimators improves signal recovery.
combo library simplifies model combination for various machine learning tasks.
problem Facilitating model combination in machine learning.
method Easy-to-use Python toolkit for aggregating models and scores.
result Unified and consistent way to combine models from multiple libraries.
Combining forecasts of 16 ED causes improves accuracy and stability.
problem Forecasting accuracy and stability for ED admissions is poor due to model uncertainty and limited data.
method High-dimensional forecast combinations of 16 cause-specific ED forecasts using extensive covariates.
result Forecast combinations yield forecast accuracies of 3.81%-23.54% across causes, outperforming individual models in 50% of scenarios.
LOL method simplifies forming linear combinations of latent variables.
problem Lack of general-purpose methods for manipulating latent variables.
method Latent Optimal Linear combinations (LOL) method.
result LOL simplifies creation of expressive low-dimensional representations.
IKA approximates kernels with linear combinations of chosen functions, outperforming Nyström method.
problem Efficient kernel approximation for large datasets.
method IKA method approximates kernels as a linear combination of user-defined functions.
result IKA consistently outperformed Nyström method on the STL-10 dataset.
A novel deep learning technique combines multiple modalities, improving performance.
problem Challenges in leveraging different modalities due to noise and conflicts.
method Proposes a deep neural network that multiplicatively combines information from different modalities.
result Consistent accuracy improvements on three multimodal classification tasks.
Constructs correspondences on hyperelliptic surfaces combining orbifold groups and Blaschke products.
problem Combining geometric and dynamical properties on hyperelliptic surfaces.
method Analytic combinations on Riemann sphere, algebraic characterization, and Teichmüller spaces.
result Explicit description of correspondences and injection into Hurwitz spaces.
ITCA optimizes label combination for ambiguous outcomes in multi-class classification.
problem Ambiguous outcome labels in real-world datasets hinder accurate multi-class classification.
method Information-theoretic classification accuracy (ITCA) and search strategies (greedy, breadth-first) guide label combination.
result ITCA improves prediction accuracy and identifies ambiguous labels across diverse applications.
This paper improves combine harvester performance using ANN-PSO hybrid model.
problem Improving performance of combine harvesters to minimize waste and reduce maintenance.
method Proposes a hybrid machine learning model combining artificial neural networks and particle swarm optimization.
result Demonstrates higher accuracy and stability in predicting optimal performance of combine harvesters.
Combines kernels to create flexible priors in BNNs for seasonal and trend data.
problem Creating flexible priors in Bayesian neural networks for complex data.
method Derives BNN architectures from kernel combinations and periodic functions.
result BNNs can produce periodic kernels useful for capturing seasonal and trend data.
For a linear combination of random variables, fix some confidence level and consider the quantile of the combination at this level. We are interested in the partial derivatives of the quantile with respect to the weights of the random variables in the combination. It turns out that under suitable conditions on the join…