New ensemble models classify mouse movement trajectories to assess survey question difficulty.
problem Assessing survey question difficulty based on respondents' interaction data.
method Ensemble models combining semi-metric-based weak learners to classify multivariate functional data.
result Improved survey data quality through better identification of respondent difficulty.
Paper uses ensemblers to predict sepsis early from patient records.
problem Early detection of sepsis in patients.
method Imputation and weak ensembler technique applied to 40k patient records.
result Model achieved 93.45% accuracy and 0.271 utility score.
Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta learning framework, ensemble techniques can easily be applied to many machine learning techniques. In this paper we propose a neural network extended with an ensemble loss function for text classification. …
We present a novel method - LIBRE - to learn an interpretable classifier, which materializes as a set of Boolean rules. LIBRE uses an ensemble of bottom-up weak learners operating on a random subset of features, which allows for the learning of rules that generalize well on unseen data even in imbalanced settings. Weak…
RaSE ensemble framework improves sparse classification accuracy.
problem Sparse classification challenges in high-dimensional data.
method Random Subspace Ensemble (RaSE) framework with subspace selection via RIC.
result RaSE achieves low misclassification rates and accurate feature ranking.
ATHENA builds flexible defenses against adversarial attacks.
problem Extensive research on adversarial attacks is domain-specific and cannot be easily extended.
method Designing an extensible framework based on diverse weak defenses.
result Comprehensive empirical study demonstrates the effectiveness of ATHENA.
Ant colonies and boosting algorithms both reduce bias and variance through adaptive mechanisms.
problem Understanding the mathematical principles behind ensemble learning and ant colony behavior.
method Developed a formal mapping between AdaBoost's adaptive reweighting and ant recruitment dynamics.
result Proved that the fundamental theorem of weak learnability has a direct analog in colony decision-making.
Improved multi-class AdaBoost algorithm with stronger weak learnability condition.
problem Multi-class classification problem with at least two labels.
method Recursive ensemble algorithm inspired by SAMME, strengthening weak learnability condition.
result Final hypothesis converges to correct label with probability 1 and generalization error bounds exponentially.
Boosting Nyström improves accuracy of matrix approximations.
problem Generating low-rank approximations of large matrices efficiently.
method Iteratively generate multiple weak Nyström approximations, combine them to form a strong approximation.
result Boosting Nyström yields more efficient and accurate low-rank approximations.
The paper offers a simple proof of Condorcet's jury theorem.
problem The relationship between majority voting and Condorcet's jury theorem.
method A simple derivation of Condorcet's jury theorem.
result Condorcet's jury theorem is more likely to choose correctly when individual votes are often correct and independent.
New framework detects model weaknesses in decision tree ensembles.
problem Detecting feature sensitivity in decision tree ensembles.
method Data-aware sensitivity framework using MILP and SMT.
result Realistic and interpretable examples of model weaknesses.
Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta-learning framework, ensemble techniques can easily be applied to many machine learning methods. Inspired by ensemble techniques, in this paper we propose an ensemble loss functions applied to a simple regre…
ProBoost boosts probabilistic classifiers by focusing on uncertain samples.
problem Improving probabilistic classifiers through targeted learning.
method ProBoost uses epistemic uncertainty to select challenging samples, increasing their weight for subsequent learners.
result ProBoost significantly improves classifier performance, especially with few weak learners.
In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We pr…
New ensemble method improves model stability exponentially.
problem Improving model stability for discontinuous base learners.
method Selecting the most frequently generated model from subsamples.
result Exponentially decaying tails for excess risk.
Paper proposes a new combined regression strategy for conditional survival prediction.
problem Improving survival prediction accuracy using conditional survival function.
method Uses regression-based weak learners with area-norm proximity measure to create an ensemble technique.
result The proposed model outperforms Random Survival Forest and selects important variables effectively.
New method interpolates training data and is consistent for various data distributions.
problem Establishing generalization guarantees for ensemble methods in the interpolating regime.
method Developed manifold-Hilbert kernel for Riemannian manifolds and used it in ensemble classification.
result Consistent ensemble classification method for broad data distributions.
The paper analyzes bootstrap ensemble classifiers in high-dimensional settings.
problem Performance of bootstrap ensemble classifiers in high-dimensional data.
method Random Matrix Theory applied to LSSVM ensemble.
result Strategies to optimize performance of LSSVM ensemble.
New theory improves understanding of ensemble learning systems.
problem Improving the effectiveness of ensemble learning systems.
method Generalized Fano's inequality to account for information loss in model combination.
result Reveals strengths and weaknesses of ensemble systems on accuracy and diversity.
A new ensemble learning method called Residual Likelihood Forests improves performance and reduces model size.
problem Improving machine learning classification performance with compact models.
method Sequential optimization of conditional likelihoods in a boosting-like framework, combining multiplicatively.
result Significant performance improvements and reduced model size compared to other ensemble methods.
Quantum algorithm improves ensemble classification with reduced memory and time requirements.
problem High memory and computational time requirements in ensemble methods.
method Quantum superposition, entanglement, and interference to build an ensemble of classification models.
result Exponential growth of ensemble size with linear increase in depth of circuit.
SETrLUSI combines diverse knowledge from multiple domains for faster convergence.
problem Handling diverse knowledge from multiple domains in transfer learning.
method Stochastic Ensemble Multi-Source Transfer Learning Using Statistical Invariant (SETrLUSI).
result SETrLUSI accelerates convergence and outperforms related methods.
We probe the character of knotting in open, confined polymers, assigning knot types to open curves by identifying their projections as virtual knots. In this sense, virtual knots are transitional, lying in between classical knot types, which are useful to classify the ambiguous nature of knotting in open curves. Modell…
Improved deep hedging with ensemble uncertainty quantification.
problem Uncertainty in deep hedging models hinders their deployment.
method Trained an ensemble of LSTM networks to quantify uncertainty in deep hedging under Heston volatility and proportional transaction costs.
result The ensemble's disagreement provides a strong predictive confidence measure for hedge performance.
Boosting and other ensemble methods combine a large number of weak classifiers through weighted voting to produce stronger predictive models. To explain the successful performance of boosting algorithms, Schapire et al. (1998) showed that AdaBoost is especially effective at increasing the margins of the training data. …
E2Tree explains random forest models in regression tasks.
problem Lack of transparency in random forest models.
method E2Tree extends random forest to regression by explaining model predictions through graphical representation and dissimilarity measures.
result E2Tree provides a transparent explanation of random forest models in regression tasks.
Collaborative filtering (CF) and content-based filtering (CBF) have widely been used in information filtering applications. Both approaches have their strengths and weaknesses which is why researchers have developed hybrid systems. This paper proposes a novel approach to unify CF and CBF in a probabilistic framework, n…
Infinite BART model selects number of trees and allows different functions for clusters.
problem Regression and classification analysis with automatic tree selection and cluster-specific functions.
method Incorporates an Indian Buffet process prior to select a subset of decision trees for each observation.
result Infinite BART model outperforms classic BART on simulated and real datasets.
Proposes a deep tree-ensemble model for multi-output prediction.
problem Lack of efficient solutions for multi-output prediction.
method Integrates tree-embeddings into deep tree-ensembles for structured output prediction.
result Superior performance in multi-label classification and multi-target regression tasks.
Class imbalance problem has been a challenging research problem in the fields of machine learning and data mining as most real life datasets are imbalanced. Several existing machine learning algorithms try to maximize the accuracy classification by correctly identifying majority class samples while ignoring the minorit…
Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data. We present a new machine learning framework called LARSEN-ELM for overcoming this problem. In our pa…
Adversarial examples are perturbed inputs designed to fool machine learning models. Adversarial training injects such examples into training data to increase robustness. To scale this technique to large datasets, perturbations are crafted using fast single-step methods that maximize a linear approximation of the model'…
Hybrid quantum-classical model boosts S&P 500 prediction accuracy to 60.14%.
problem Challenges in financial market prediction, especially high noise and non-stationarity.
method Combines quantum sentiment analysis, Decision Transformer, and model selection strategies.
result Achieved 60.14% directional accuracy on S&P 500, a 3.10% improvement.
Ensemble learning is a statistical paradigm built on the premise that many weak learners can perform exceptionally well when deployed collectively. The BART method of Chipman et al. (2010) is a prominent example of Bayesian ensemble learning, where each learner is a tree. Due to its impressive performance, BART has rec…
Treeging combines regression trees and kriging for spatial and space-time prediction.
problem Improving spatial and space-time prediction accuracy.
method Combines regression trees with kriging's covariance structures.
result Treeging outperforms kriging and random forest in various scenarios.
The study forecasts hourly intraday electricity prices using ensemble methods.
problem Weak-form efficiency of hourly German Intraday Continuous Market prices.
method Probabilistic forecasting with ensemble trajectories, generalized additive model, and lasso penalty.
result The mixture model outperforms benchmarks in forecasting price distribution and volatility.
GP-BART improves BART's predictive performance by incorporating Gaussian process priors.
problem Lack of smoothness and explicit covariance structure in BART.
method GP-BART extends BART with Gaussian process priors for tree predictions.
result GP-BART outperforms traditional models in various applications.
Adaptive framework generates challenging adversarial scenarios for autonomous vehicles.
problem Lack of efficient and adaptable evaluation methods for autonomous vehicles.
method Adaptive evaluation framework using ensemble models and nonparametric Bayesian clustering.
result Adversarial scenarios significantly degrade tested autonomous vehicles' performance.
This paper tackles imbalanced classification with weakly supervised oversampling.
problem Imbalanced classification in high-dimensional datasets.
method Weakly supervised SMOTE, cost-sensitive NCA, bootstrap ensemble.
result Improved classification performance on synthetic and real-world datasets.
During times of extreme market turmoil, it is acknowledged that there is a tendency towards "flight to safety". A strong (weak) safe haven is defined as an asset that has a significant positive (negative) return in periods where another asset is in distress, while hedge has to be negatively correlated (uncorrelated) on…
Study the limits of discrete DPPs to continuous DPPs as set size grows.
problem Characterize the behavior of discrete DPPs as they approach continuous DPPs.
method Non-asymptotic characterization of the limit in terms of weak coherency.
result Sufficient conditions for weak coherency are identified.
Survey on Evidential Deep Learning for uncertainty estimation in deep neural networks.
problem Uncertainty estimation in deep neural networks with overhead or limited diversity.
method Evidential Deep Learning, parameterizing distributions over distributions.
result Single model and forward pass uncertainty estimation with a unified notation.
Learning a natural language interface for database tables is a challenging task that involves deep language understanding and multi-step reasoning. The task is often approached by mapping natural language queries to logical forms or programs that provide the desired response when executed on the database. To our knowle…
Systematic trading strategies are algorithmic procedures that allocate assets aiming to optimize a certain performance criterion. To obtain an edge in a highly competitive environment, the analyst needs to proper fine-tune its strategy, or discover how to combine weak signals in novel alpha creating manners. Both aspec…
Developing effective and efficient recommendation methods is very challenging for modern e-commerce platforms. Generally speaking, two essential modules named "Click-Through Rate Prediction" (\textit{CTR}) and "Conversion Rate Prediction" (\textit{CVR}) are included, where \textit{CVR} module is a crucial factor that a…
Unified approach to aggregating models and preferences.
problem Consistent aggregation of models and preferences.
method Formal definition and weighted averaging of models and preferences.
result All rational aggregation rules are weighted averages of highest-ranked models/experts.
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.
We propose a deep-learning approach based on generative adversarial networks (GANs) to reduce noise in weak lensing mass maps under realistic conditions. We apply image-to-image translation using conditional GANs to the mass map obtained from the first-year data of Subaru Hyper Suprime-Cam (HSC) survey. We train the co…