arXiv research

Study finds prediction markets do not converge on consensus beliefs.

CB-APM uses analyst consensus as a bottleneck to interpret stock returns.

We consider the problem of belief aggregation: given a group of individual agents with probabilistic beliefs over a set of uncertain events, formulate a sensible consensus or aggregate probability distribution over these events. Researchers have proposed many aggregation methods, although on the question of which is be…

Mobile agents classify images via reinforcement learning and consensus.

Bayesian framework for online consensus prediction from expert feedback.

Belief networks are a new, potentially important, class of knowledge-based models. ARCO1, currently under development at the Atlantic Richfield Company (ARCO) and the University of Southern California (USC), is the most advanced reported implementation of these models in a financial forecasting setting. ARCO1's underly…

This study models FOMC policy decisions using debate-based LLMs.

Agents learn implied volatility in financial markets, resolving theory-practice gap.

RL agents fail to generalize to unseen environments, even when dynamics are similar.

Community moderation drifts towards majority, study finds.

Unifies 18 definitions of surprise, classifies them into four categories.

WISCA generates consensus explanations from conflicting model-agnostic interpretability methods.

The paper explores trade-offs between consensus and optimality in collaborative deep learning.

Bayesian consensus improves accuracy of forecasts from miscalibrated sources.

This paper provides an embedding perspective to consensus clustering.

Paper analyzes convergence of decentralized algorithms with noise and bias.

Paper proves PI consensus algorithm converges exponentially under restricted secant inequality.

We use a cluster ensemble to determine the number of clusters, k, in a group of data. A consensus similarity matrix is formed from the ensemble using multiple algorithms and several values for k. A random walk is induced on the graph defined by the consensus matrix and the eigenvalues of the associated transition proba…

To devise efficient solutions for approximating a mean partition in consensus clustering, Dimitriadou et al. [3] presented a necessary condition of optimality for a consensus function based on least square distances. We show that their result is pivotal for deriving interesting properties of consensus clustering beyond…

A new algorithm reduces communication in decentralized optimization.

Modularity is a popular measure of community structure. However, maximizing the modularity can lead to many competing partitions, with almost the same modularity, that are poorly correlated with each other. It can also produce illusory "communities" in random graphs where none exist. We address this problem by using th…

We present here an introduction to Brainstorming approach, that was recently proposed as a consensus meta-learning technique, and used in several practical applications in bioinformatics and chemoinformatics. The consensus learning denotes heterogeneous theoretical classification method, where one trains an ensemble of…

A novel framework for consensus clustering is presented which has the ability to determine both the number of clusters and a final solution using multiple algorithms. A consensus similarity matrix is formed from an ensemble using multiple algorithms and several values for k. A variety of dimension reduction techniques …

Adversaries can manipulate cooperative MARL networks.

Determinantal consensus clustering improves clustering robustness.

CoHiRF extends clustering methods to handle high-dimensional data efficiently.

Online machine learns from signals robustly using consensus optimization.

IMPACC improves consensus clustering for bioinformatics data.

The distribution of price returns for a class of uncorrelated diffusive dynamics is considered. The basic assumptions are (1) that there is a "consensus" value associated with a stock, and (2) that the rate of diffusion depends on the deviation of the stock price from the consensus value. We find an analytical expressi…

Consensus dimension reduction combines multiple visualizations to identify shared patterns.

TCNs use multiple modalities to improve semi-supervised learning.

SBO uses dual voting to build consensus in noisy feedback settings.

The present paper considers distributed consensus algorithms that involve N agents evolving on a connected compact homogeneous manifold. The agents track no external reference and communicate their relative state according to a communication graph. The consensus problem is formulated in terms of the extrema of a cost f…

Generalized belief propagation converges to optimal solutions on graphs with motifs.

Study risk sharing with Lambda VaR under diverse beliefs.

Study on consensus formation in manifolds with curvature constraints.

New Ising models improve consensus clustering on specialized hardware.

New method learns belief representations for GAIL in POMDPs.

Belief propagation recovers backpropagation results.

New framework analyzes belief evolution in social networks.

Suppose that multiple experts (or learning algorithms) provide us with alternative Bayesian network (BN) structures over a domain, and that we are interested in combining them into a single consensus BN structure. Specifically, we are interested in that the consensus BN structure only represents independences all the g…

Crowdlab uses classifiers to estimate consensus labels and annotator quality.

A neural architecture learns and refines graph correspondences.

A new framework for clustering high-dimensional data using vertical shards.

ANN clusters multi-view data by agglomerating subviews and avoiding postprocessing.

The paper improves consensus-based optimization for private data sharing.

Belief Propagation solves a relaxed network flow problem.

This paper considers the problem of detection in distributed networks in the presence of data falsification (Byzantine) attacks. Detection approaches considered in the paper are based on fully distributed consensus algorithms, where all of the nodes exchange information only with their neighbors in the absence of a fus…