Automates standards design through machine learning.
problem Manual, time-consuming standards design process.
method Reinforcement learning to optimize proposals.
result Streamlines standards design and innovation.
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48 results for “consensus module”
WISCA generates consensus explanations from conflicting model-agnostic interpretability methods.
problem Conflicting explanations from diverse interpretability algorithms.
method WISCA integrates class probability and normalized attributions to generate consistent explanations.
result WISCA consistently aligns with the most reliable individual method, improving explanation reliability.
Bayesian Consensus: Consensus Estimates from Miscalibrated Instruments under Heteroscedastic Noiseq-fin.ST
Bayesian consensus improves accuracy of forecasts from miscalibrated sources.
problem Aggregating predictions from miscalibrated and noisy sources.
method Bayesian approach to adjust for bias and noise, using hierarchical models.
result Bayesian consensus estimator is unbiased and more efficient than alternatives.
Bayesian framework for online consensus prediction from expert feedback.
problem Online classification with expert consensus prediction, cost-effective.
method General Bayesian framework for dynamic expert consensus estimation.
result Demonstrated superior performance on large-scale crowdsourced datasets.
In distributed machine learning, where agents collaboratively learn from diverse private data sets, there is a fundamental tension between consensus and optimality. In this paper, we build on recent algorithmic progresses in distributed deep learning to explore various consensus-optimality trade-offs over a fixed commu…
Paper analyzes convergence of decentralized algorithms with noise and bias.
problem Finite time convergence analysis of decentralized stochastic approximation schemes.
method Separated iterates into consensual parts and consensus error; bounded consensus error in terms of stationarity.
result Decentralized SA scheme converges at rate.
CB-APM uses analyst consensus as a bottleneck to interpret stock returns.
problem Tackles the challenge of understanding and predicting stock returns using professional beliefs.
method Embeds analyst consensus as a structural bottleneck, treating it as a sufficient statistic for market information.
result CB-APM portfolios exhibit strong monotonic return gradients and robust across different economic conditions.
Paper proves PI consensus algorithm converges exponentially under restricted secant inequality.
problem Proving convergence of PI consensus algorithm without convexity.
method Lyapunov theory, restricted secant inequality, rate-matching discretization, local pre-conditioning.
result Exponential convergence of PI consensus algorithm for non-convex functions.
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.
problem Reducing communication in decentralized optimization problems.
method Adaptive randomized communication-efficient algorithmic framework that periodically tracks disagreement error and selects influential edges for communication.
result Strong theoretical convergence guarantees and performance quantification under standard assumptions.
Consensus clustering fuses diverse basic partitions (i.e., clustering results obtained from conventional clustering methods) into an integrated one, which has attracted increasing attention in both academic and industrial areas due to its robust and effective performance. Tremendous research efforts have been made to t…
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 approach for 3D lung nodule segmentation using adaptive ROI and multi-view residual learning.
problem Inaccurate nodule segmentation due to fixed ROI and redundant structures.
method Two-stage approach: 2D ROI patch-wise investigation with adaptive ROI strategy, followed by 2D and 3D VOI investigation with deep residual U-Net.
result Significantly robust and accurate nodule segmentation compared to previous methods.
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.
problem Fragility of consensus-based MARL algorithms to adversarial attacks.
method Study of adversarial attacks on consensus-based MARL networks.
result Consensus-based MARL algorithms are vulnerable to adversarial manipulation.
Determinantal consensus clustering improves clustering robustness.
problem Robustness of clustering algorithms.
method Use of determinantal point processes (DPP) for random restart of clustering algorithms.
result Determinantal consensus clustering outperforms classical algorithms.
CoHiRF extends clustering methods to handle high-dimensional data efficiently.
problem Scalability limits of existing clustering methods.
method Hierarchical consensus framework operating on label assignments.
result Improves robustness and scalability to high-dimensional noise.
IMPACC improves consensus clustering for bioinformatics data.
problem Consensus clustering's inefficiency and lack of interpretability for large-scale data.
method Ensemble minipatch co-occurrences, adaptive sampling of observations and features.
result Significantly improved accuracy and interpretability with substantial computational savings.
A Theory of Fluctuations in Stock Pricescond-mat.other
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.
problem Conflicting visualizations from different dimension reduction methods.
method Multi-view learning to identify stable patterns across multiple views.
result Consensus visualization effectively identifies shared low-dimensional data structure.
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.
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…
Study on consensus formation in manifolds with curvature constraints.
problem Long-time behavior of solutions to nonlocal PDEs on Riemannian manifolds.
method Analytical and numerical methods applied to self-collective models.
result Sufficient conditions for consensus formation and convergence rates quantified.
New Ising models improve consensus clustering on specialized hardware.
problem Consensus clustering optimization problems.
method Formulated consensus clustering as Ising models and evaluated on specialized hardware.
result Our Ising models outperform existing techniques on consensus clustering.
Deep learning has demonstrated abilities to learn complex structures, but they can be restricted by available data. Recently, Consensus Networks (CNs) were proposed to alleviate data sparsity by utilizing features from multiple modalities, but they too have been limited by the size of labeled data. In this paper, we ex…
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.
problem Leveraging multiple annotators for data classification.
method Weighted ensemble approach using any trained classifier.
result Superior estimates for consensus labels and annotator quality.
A neural architecture learns and refines graph correspondences.
problem Learning structural correspondences between graphs.
method Two-stage neural architecture: node embeddings followed by message passing networks.
result Message passing scheme computes well-founded consensus for corresponding neighborhoods.
A new framework for clustering high-dimensional data using vertical shards.
problem Clustering high-dimensional data with the curse of dimensionality.
method Vertical Consensus Inference (VCI) that splits data into vertical shards for posterior inference.
result VCI can approximate inference on random partitions for high-dimensional data.
ANN clusters multi-view data by agglomerating subviews and avoiding postprocessing.
problem Challenges in multi-view clustering, especially subview agglomeration.
method Agglomerative Neural Network (ANN) using Constrained Laplacian Rank.
result ANN outperforms state-of-the-art methods in multi-view clustering.
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…
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 defines a new pseudometric for measuring consensus among subsets of relations.
problem Measuring consensus among subsets of relations.
method Introduces a new pseudometric and provides a concise restatement with a functor interpretation. Also, describes an algorithm to calculate the pseudometric bound efficiently.
result The pseudometric can be bounded without an expensive search of possible mappings, based solely on the dimensions of the relations.
Unified view connects CoCoA and ADMM for distributed ERM.
problem Connection between CoCoA and ADMM for distributed ERM.
method Unified primal-dual perspective reformulation.
result Unified ADMM variants perform at least as good as CoCoA in ridge-regularized ERM.
Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learningcs.LG
New optimization model converges to global minimizers on spheres.
problem Global optimization of nonconvex functions on spheres.
method Stochastic Kuramoto-Vicsek-type model with consensus dynamics and random perturbations.
result Proof of convergence to global minimizers under certain conditions.
Paper introduces robust methods for consensus ranking in AI systems.
problem Developing reliable ranking systems in AI despite contaminated data.
method Introduces robustness concepts and statistical methods for consensus ranking.
result Proposes extensions of breakdown point for consensus ranking.
New methods reveal consensus and dissensus in network partitions.
problem Degenerate community detection methods often yield multiple competing answers.
method Comprehensive set of methods to characterize and summarize complex populations of partitions.
result It is not possible to obtain a consistent answer from point estimates when the distribution is heterogeneous.
We consider grouping as a general characterization for problems such as clustering, community detection in networks, and multiple parametric model estimation. We are interested in merging solutions from different grouping algorithms, distilling all their good qualities into a consensus solution. In this paper, we propo…
Decentralized ranking consensus via gossip for robust and scalable systems.
problem Achieving reliable and resilient consensus on collective rankings in a decentralized setting.
method Random gossip communication for decentralized computation of global rankings.
result Robust and scalable consensus on collective rankings achieved through decentralized, local interactions.
A new topology improves decentralized learning efficiency and accuracy.
problem Finding efficient decentralized learning topologies with fast consensus and low maximum degree.
method Proposed the Base- Graph topology for decentralized learning.
result The Base- Graph enables faster convergence and better communication efficiency than the exponential graph.
Preventing early progression of epilepsy and so the severity of seizures requires an effective diagnosis. Epileptic transients indicate the ability to develop seizures but humans overlook such brief events in an electroencephalogram (EEG) what compromises patient treatment. Traditionally, training of the EEG event dete…
New method for summarizing ranking distributions using consensus ranking distributions.
problem Summarizing ranking distributions efficiently and accurately.
method Introducing consensus ranking distributions and a top-down tree-structured statistical algorithm.
result Optimal distortion can be expressed as a function of pairwise probabilities, enabling efficient learning methods.
Condorcet's Jury Theorem has been invoked for ensemble classifiers to indicate that the combination of many classifiers can have better predictive performance than a single classifier. Such a theoretical underpinning is unknown for consensus clustering. This article extends Condorcet's Jury Theorem to the mean partitio…
Although consistency is a minimum requirement of any estimator, little is known about consistency of the mean partition approach in consensus clustering. This contribution studies the asymptotic behavior of mean partitions. We show that under normal assumptions, the mean partition approach is consistent and asymptotic …
A method for learning embeddings from multi-view data using Gromov-Wasserstein.
problem Challenges in learning low-dimensional representations from multi-view relational data with differing geometries.
method Bary-GWMDS and Mean-GWMDS-C, Gromov-Wasserstein-based methods operating on distance matrices.
result Stable and geometrically meaningful embeddings learned from synthetic and real-world datasets.
The AIBC is an Artificial Intelligence and blockchain technology based large-scale decentralized ecosystem that allows system-wide low-cost sharing of computing and storage resources. The AIBC consists of four layers: a fundamental layer, a resource layer, an application layer, and an ecosystem layer. The AIBC implemen…
New algorithm improves decentralized learning in the presence of Byzantine faults.
problem Byzantine faults in decentralized learning on arbitrary graphs.
method Proposes ClippedGossip for Byzantine-robust consensus and optimization.
result First to provably converge to a specified neighborhood of the stationary point for non-convex objectives.