arXiv research

Three-candidate plurality voting is stable for small correlations.

New findings on maximizing noise stability in partitions of Gaussian space.

One unexamined assumption in foreign ownership regulation is the notion that majority voting rights translate to 'effective control'. This assumption is so deeply entrenched in foreign investments law that possession of majority voting rights can determine the nationality of a corporation and its capacity to engage in …

Delegated votes in Uniswap DAO favor parties with less self-owned votes and a16z-affiliated entities.

A thermodynamic theory explains EU election vote distributions.

Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content …

Study generalization of voting classifiers using margin-based bounds.

Boosting improves accuracy by combining weak learners into a voting classifier.

It is shown that $3$ disjoint sets with fixed Gaussian volumes that partition $\mathbb{R}^{n}$ with nearly minimum total Gaussian surface area must be close to adjacent $120$ degree sectors, when $n\geq2$. These same results hold for any number $m\leq n+1$ of sets partitioning $\mathbb{R}^{n}$, conditional on the solut…

New voting rules protect against strategic voting by robust statistics.

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.

Majority-of-Three is Optimal

Optimizes neural networks for solving problems with pruning and ensembles of minimal structures.

In acoustic scene classification researches, audio segment is usually split into multiple samples. Majority voting is then utilized to ensemble the results of the samples. In this paper, we propose a punishment voting algorithm based on the super categories construction method for acoustic scene classification. Specifi…

New voting strategies show committee-based consensus can scale efficiently.

Optimal number of voters for a voting ensemble can be estimated from the distribution of classifier errors.

The paper studies a stochastic majority vote approach to improve classifier accuracy.

We tackle the issue of classifier combinations when observations have multiple views. Our method jointly learns view-specific weighted majority vote classifiers (i.e. for each view) over a set of base voters, and a second weighted majority vote classifier over the set of these view-specific weighted majority vote class…

Novel analysis improves weighted majority vote in multiclass classification.

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.

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.

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

New method improves conformal prediction for ambiguous labels.

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.

Traditionally, text generation models take in a sequence of text as input, and iteratively generate the next most probable word using pre-trained parameters. In this work, we propose the architecture to use images instead of text as the input of the text generation model, called StoryGen. In the architecture, we design…

The abstract warns against flawed empirical research in machine learning.

The ungrammatical sentence "The key to the cabinets are on the table" is known to lead to an illusion of grammaticality. As discussed in the meta-analysis by Jaeger et al., 2017, faster reading times are observed at the verb are in the agreement-attraction sentence above compared to the equally ungrammatical sentence "…

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.

Voting ensemble of robust models improves robustness.

We develop ensemble Convolutional Neural Networks (CNNs) to classify the transportation mode of trip data collected as part of a large-scale smartphone travel survey in Montreal, Canada. Our proposed ensemble library is composed of a series of CNN models with different hyper-parameter values and CNN architectures. In o…

Majority Vote is optimal for reliable data labeling under certain conditions.

The recent success of single-agent reinforcement learning (RL) in Internet of things (IoT) systems motivates the study of multi-agent reinforcement learning (MARL), which is more challenging but more useful in large-scale IoT. In this paper, we consider a voting-based MARL problem, in which the agents vote to make grou…

A mathematical analysis of the distribution of voting power in the Council of the European Union operating according to the Treaty of Lisbon is presented. We study the effects of Brexit on the voting power of the remaining members, measured by the Penrose--Banzhaf Index. We note that the effects in question are non-mon…

This paper proposes an approach to detect emotion from human speech employing majority voting technique over several machine learning techniques. The contribution of this work is in two folds: firstly it selects those features of speech which is most promising for classification and secondly it uses the majority voting…

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.

The paper improves confidence regions for band-limited functions using tighter norm bounds and majority voting.

Simpler majority vote of three classifiers achieves optimal error bounds.

Blockchain fan tokens boost sports fan engagement by 50%.

New bounds on majority voting's accuracy for multi-class classification problems.

Best-of-Majority improves inference performance in Pass@$k$ settings.

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.