A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New voting strategies show committee-based consensus can scale efficiently.
New voting rules protect against strategic voting by robust statistics.
Best-of-Majority improves inference performance in Pass@ settings.
In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly…
Blockchain fan tokens boost sports fan engagement by 50%.
In this work, we addressed the issue of combining linear classifiers using their score functions. The value of the scoring function depends on the distance from the decision boundary. Two score functions have been tested and four different combination strategies were investigated. During the experimental study, the pro…
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.
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
No fair and strategy-proof automated market maker exists for more than two assets.
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…
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…
We describe a new model to simulate the dynamic interactions between market price and the decisions of two different kind of traders. They possess spatial mobility allowing to group together to form coalitions. Each coalition follows a strategy chosen from a proportional voting ``dominated'' by a leader's decision. The…
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.
We introduce a new class of context dependent, incomplete information games to serve as structured prediction models for settings with significant strategic interactions. Our games map the input context to outcomes by first condensing the input into private player types that specify the utilities, weighted interactions…
The paper analyzes how multiple classifiers' disagreement and polarization affect overall accuracy.
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…
Deep networks and forests perform differently with small samples.
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.
Unified framework for certifying LLM reliability without extra supervision.
New bounds on majority voting's accuracy for multi-class classification problems.
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…