The paper challenges the assumption that majority voting rights equate to 'effective control' in foreign ownership regulations.
problem The assumption that majority voting rights determine 'effective control' in foreign ownership regulations is flawed.
method The paper proposes and demonstrates a method for calculating 'effective control' based on voting thresholds and weights.
result The 'effective control' of a foreign minority stockholder can be higher than their shareholding size, challenging the assumption that majority voting rights equate to 'effective control'.
New voting strategies show committee-based consensus can scale efficiently.
problem Ensuring honest committees in committee-based consensus protocols.
method Empirical analysis of simpler voting strategies and their convergence to optimality.
result Simpler voting strategies converge to optimality exponentially quickly, ensuring robustness and efficiency.
Paper proposes a new method for social recommendation in online voting.
problem Challenges in recommending online voting based on social networks and voting content.
method Proposes TEWE for word and document representation and JTS-MF for voting recommendation.
result Proves the effectiveness of the proposed approach compared to state-of-the-art methods.
Delegated votes in Uniswap DAO favor parties with less self-owned votes and a16z-affiliated entities.
problem Incentives for vote delegation in decentralized governance systems.
method Analysis of Uniswap governance DAO using vote delegation data.
result Vote delegation patterns suggest window-dressing around decentralization and merit-based delegation.
Paper proposes a voting method to improve acoustic scene classification.
problem Improving acoustic scene classification accuracy.
method Punishment voting algorithm based on super categories construction.
result Punishment voting significantly improves classification performance.
A thermodynamic theory explains EU election vote distributions.
problem Describing EU election vote distributions.
method Tracing parallels between system energies of coupled nonlinear oscillators and party vote fractions.
result The Rayleigh-Jeans (RJ) theory well depicts EU vote results and candidate vote dispersion.
Study generalization of voting classifiers using margin-based bounds.
problem Understanding the generalization of ensemble classifiers like voting.
method Proved margin-based generalization bounds using PAC-Bayes theory and Dirichlet posteriors.
result Provided state-of-the-art guarantees on classification tasks.
Boosting improves accuracy by combining weak learners into a voting classifier.
problem Boosting's theoretical performance is sub-optimal, especially for voting classifiers.
method Proposes a randomized boosting algorithm that outputs voting classifiers with a single logarithmic dependency on sample size.
result Randomized boosting achieves a generalization error with a single logarithmic dependency on the sample size.
New voting rules protect against strategic voting by robust statistics.
problem Strategic voting can skew election outcomes.
method Revisit Mallows model, develop robust estimator.
result Efficient estimator achieves nearly optimal robustness.
We combine fine-grained spatially referenced census data with the vote outcomes from the 2016 US presidential election. Using this dataset, we perform ecological inference using distribution regression (Flaxman et al, KDD 2015) with a multinomial-logit regression so as to model the vote outcome Trump, Clinton, Other / …
SBO uses dual voting to build consensus in noisy feedback settings.
problem Collective decision-making under social influence.
method Dual voting system with noisy public votes and private interviews.
result Efficient consensus-building through noisy public votes, debiased by estimated social graph.
Majority-of-Three is Optimal
problem Optimality of Voting Learners
method Majority vote of three classifiers
result Proves optimality for the simplest voting scheme
A method for combining classifiers from multiple views using Bregman divergences.
problem Combining classifiers from multiple views with limited labeled data.
method Jointly learns view-specific and overall weighted majority vote classifiers using Bregman divergences.
result Empirical results show improved classifier performance with limited labeled data.
Optimal number of voters for a voting ensemble can be estimated from the distribution of classifier errors.
problem Finding the optimal number of voters for a voting ensemble to minimize error rate.
method Estimate the distribution of classifier errors and infer error rates for different numbers of voters.
result Lower-variance estimates of error rates can be obtained by inferring them for different numbers of voters.
The paper studies a stochastic majority vote approach to improve classifier accuracy.
problem Improving classifier accuracy over ensembles of classifiers.
method Minimizing a PAC-Bayes generalization bound with Dirichlet distributions.
result Achieves state-of-the-art accuracy and tight generalization bounds.
Novel analysis improves weighted majority vote in multiclass classification.
problem Improving the performance of weighted majority vote in multiclass classification.
method Analyzes expected risk of weighted majority vote, considering prediction correlations and provides a bound for efficient minimization.
result Minimization of the new bound typically does not degrade the test error of the ensemble.
In this paper, we propose majority voting neural networks for sparse signal recovery in binary compressed sensing. The majority voting neural network is composed of several independently trained feedforward neural networks employing the sigmoid function as an activation function. Our empirical study shows that a choice…
New algorithms minimize PAC-Bayesian C-Bound for majority voting, leading to scalable and accurate predictors.
problem Improving majority vote classifiers using PAC-Bayesian bounds.
method Directly optimizing PAC-Bayesian guarantees on the C-Bound with gradient descent.
result Self-bounding majority vote learning algorithms with scalable and accurate predictors.
Paper proposes a voting-based MARL approach for IoT systems.
problem Maximizing globally averaged returns in multi-agent IoT systems.
method Formulated as linear programming, proposed distributed primal-dual algorithm, voting mechanism for convergence.
result Distributed learning achieves sublinear convergence rate similar to centralized learning.
Mathematical analysis shows Brexit affects EU voting power in unexpected ways.
problem Effects of Brexit on EU voting power and distribution of power.
method Mathematical analysis using Penrose--Banzhaf Index and normal approximation.
result Non-monotonic effects of Brexit on EU voting power, exacerbated by EU population vector.
In his seminal work, Schapire (1990) proved that weak classifiers could be improved to achieve arbitrarily high accuracy, but he never implied that a simple majority-vote mechanism could always do the trick. By comparing the asymptotic misclassification error of the majority-vote classifier with the average individual …
TBIP uses texts to quantify lawmakers' political positions.
problem Quantifying lawmakers' political positions from speeches, tweets, etc.
method Unsupervised probabilistic topic model analyzing texts.
result TBIP separates lawmakers by party and infers ideal points close to vote-based.
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.
New method improves conformal prediction for ambiguous labels.
problem Uncertainty in expert voting labels affects CP guarantees.
method Develops Monte Carlo CP procedures approximating true posterior.
result Monte Carlo CP closes coverage gap with expert annotations.
Approach to detect emotion from speech using majority voting and selected features.
problem Detecting human emotion from speech.
method Majority voting technique over machine learning models (NN, DT, SVM, KNN).
result Majority voting technique achieves better accuracy than individual models.
We develop a model of issue-specific voting behavior. This model can be used to explore lawmakers' personal voting patterns of voting by issue area, providing an exploratory window into how the language of the law is correlated with political support. We derive approximate posterior inference algorithms based on variat…
New margin bound improves generalization for voting classifiers.
problem Improving generalization bounds for voting classifiers.
method Established a new margin-based generalization bound.
result Derives an optimal weak-to-strong learner with matching theoretical lower bound.
Ensemble CNNs improve mode classification in smartphone travel surveys.
problem Classifying transportation modes from smartphone travel survey data.
method Developed an ensemble of CNN models with different architectures and hyper-parameters, combined using average voting, majority voting, optimal weights, and a Random Forest meta-learner.
result The ensemble method with Random Forest as meta-learner achieved 91.8% accuracy, surpassing other methods.
VORACE uses random classifiers to vote for the best class, saving time and expertise.
problem Finding the best classifier for a dataset is costly and requires domain expertise.
method Randomly generated classifiers vote to determine the best class ranking.
result VORACE outperforms state-of-the-art methods on various datasets.
Successful attempts to predict judges' votes shed light into how legal decisions are made and, ultimately, into the behavior and evolution of the judiciary. Here, we investigate to what extent it is possible to make predictions of a justice's vote based on the other justices' votes in the same case. For our predictions…
Activists align with large fund preferences for success.
problem Aligning with large fund preferences increases activist success.
method Analyzed previous proxy voting behavior to estimate preferences and correlated them with activist success.
result Campaigns with higher alignment receive more votes and are more successful.
Voting ensemble of robust models improves robustness.
problem Improving robustness of defensive models against adversarial attacks.
method Hard-label voting ensemble of pretrained robust models.
result Voting ensemble can boost robust error over individual models.
Majority Vote is optimal for reliable data labeling under certain conditions.
problem Reliable data labeling requires aggregating multiple annotators' labels, but the optimality of Majority Vote is not well understood.
method Characterized conditions under which Majority Vote achieves the optimal label estimation error.
result Majority Vote optimally recovers labels for a given class distribution under tolerable annotation noise limits.
Crowdsourcing has become an effective and popular tool for human-powered computation to label large datasets. Since the workers can be unreliable, it is common in crowdsourcing to assign multiple workers to one task, and to aggregate the labels in order to obtain results of high quality. In this paper, we provide finit…
Paper proposes FVC for functional data classification.
problem Challenges in high-dimensional temporal data.
method Ensemble learning of diverse functional representations.
result FVC enhances predictive accuracy over individual models.
The paper improves confidence regions for band-limited functions using tighter norm bounds and majority voting.
problem Constructing reliable confidence regions for band-limited functions from noisy data.
method Improved norm bounds using Hoeffding's inequality and empirical Bernstein bound, majority voting to aggregate intervals.
result Confidence intervals retain their simultaneous coverage guarantee even when aggregated from random subsamples.
Simpler majority vote of three classifiers achieves optimal error bounds.
problem Developing an optimal PAC learning algorithm in the realizable setting.
method Returning the majority vote of three ERM classifiers.
result Achieves optimal in-expectation bound on error.
New model captures intransitive preferences without concave likelihood.
problem Complex human choices not accounted for by traditional models.
method Inspired by Condorcet method, Majority Vote model using RUMs.
result Three-dimensional model can represent strong, long intransitive cycles.
Blockchain fan tokens boost sports fan engagement by 50%.
problem Low fan engagement in sports decisions.
method Analyzed 3,576 fan token polls to identify determinants of participation.
result Fan tokens engage 4,003 participants per poll, 50% of token holders.
New bounds on majority voting's accuracy for multi-class classification problems.
problem Determining the accuracy of majority voting for multi-class classification.
method Analyzing the majority voting function under different voter conditions and distributions.
result The error rate of majority voting exponentially decays or grows with the number of voters under certain conditions.
EigenNetworks approximate time-varying graphs as linear combinations of fixed eigennetworks.
problem Challenges in tracking large, time-varying networks.
method Approximate time-varying networks as weighted linear combinations of eigennetworks, estimated through Principal Network Analysis.
result Smooth eigentrajectories reveal underlying patterns and potential structural shifts in networks.
Best-of-Majority improves inference performance in Pass@k settings.
problem Inference in difficult tasks often underperforms with single-shot selection methods.
method Combining majority voting and Best-of-N, Best-of-Majority restricts candidates to high-frequency responses.
result Best-of-Majority achieves minimax optimal regret and outperforms other methods.
Removing or filtering outliers and mislabeled instances prior to training a learning algorithm has been shown to increase classification accuracy. A popular approach for handling outliers and mislabeled instances is to remove any instance that is misclassified by a learning algorithm. However, an examination of which l…
New PAC-Bayesian bounds for multi-view learning using Rényi divergence.
problem Applying PAC-Bayesian theory to multi-view learning.
method Introducing novel PAC-Bayesian bounds based on Rényi divergence for multi-view learning.
result Efficient optimization algorithms that align with theoretical bounds.
Sampling more can make models more confident in wrong answers, not better.
problem The modal ceiling and correlation ceiling limit the benefit of increased sampling.
method Analyzes the trade-offs between sampling more and selecting the best answer.
result Extra sampling beyond a certain point does not improve model performance and can even degrade it.
Paper uses neural word embeddings to analyze UN speeches for policy preferences and voting behavior.
problem Analyzing policy preferences and paradigm shifts in international politics.
method Applied neural word embeddings (Word2vec) to UN General Debate speeches.
result Found statistical relation between speech semantic content and voting behavior, contrary to hypothesis.
New bound improves on weighted majority vote risk estimation.
problem Improving risk estimation for weighted majority vote.
method Novel Chebyshev-Cantelli inequality and PAC-Bayes-Bennett inequality.
result New bounds improve on existing methods.
Machine learning ensemble improves accuracy by considering minority answers as more likely true.
problem Ensemble methods often rely on majority voting, which can fail when the majority is wrong.
method Proposes Bayesian Truth Serum for classification problems, detecting surprising majority answers.
result Better classification performance achieved by considering minority answers as more likely true.