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 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.
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.
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.
The paper explores trade-offs between consensus and optimality in collaborative deep learning.
problem The tension between consensus and optimality in distributed machine learning.
method Proposes i-CDSGD and g-CDSGD algorithms to navigate consensus-optimality trade-offs.
result The proposed algorithms converge for both convex and nonconvex objectives.
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…
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.
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.
Online machine learns from signals robustly using consensus optimization.
problem Signal classification in online settings.
method Reproducing kernel Hilbert space with consensus optimization.
result Robust signal classification achieved.
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.
This paper provides an embedding perspective to consensus clustering.
problem Consensus clustering combines multiple clustering results.
method Transfer categorical partitions to binary coding, spectral embedding, etc.
result Unified two major categories of consensus clustering and connected it to graph embedding.
TCNs use multiple modalities to improve semi-supervised learning.
problem Limited labeled data in semi-supervised learning.
method TCNs combine adversarial training and latent representation similarity to reach consensus.
result TCNs outperform benchmarks with 20-200 labeled samples.
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.
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 O ( log T / T ) {\cal O}(\log T/ \sqrt{T} ) O ( log T / T ) rate. 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…
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.
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.
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…
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.
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.
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.
The paper proposes a method to evaluate clustering models using ensemble techniques.
problem Lack of robust cluster validity scores for unsupervised learning.
method Cluster ensemble aggregation techniques and normalized mutual information.
result The method can highlight standout clustering and hyperparameter configurations in an ensemble.
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.
The paper proposes a consensus algorithm to improve deep neural network interpretability and accuracy in mortality prediction.
problem The black-box nature and overgeneralization of deep neural networks in healthcare applications.
method An ( n n n , k k k ) consensus algorithm that is insensitive to adversarial examples and can reliably reject out-of-distribution samples. result The consensus algorithm improves both prediction accuracy and interpretability of deep neural network models in mortality prediction.
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.
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.
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…
Consensus Monte Carlo clusters big data with shared anchors.
problem Clustering and feature allocation in large datasets.
method Bayesian nonparametric models, Dirichlet process, Indian buffet process, consensus Monte Carlo.
result Valid for various sampling models and priors.
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…
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.
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.
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.
The paper improves consensus-based optimization for private data sharing.
problem Privacy leakage in distributed optimization algorithms.
method Adding random noise to messages to achieve ( ε , δ ) (ε, δ) ( ε , δ ) -differential privacy. result The method converges with a bounded mean-squared error while maintaining privacy.
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.
PACO uses consensus to improve overlapping patch processing.
problem Improving overlapping patch processing to avoid averaging issues.
method Develops a variational signal processing framework with ADMM-based algorithm for consensus.
result Efficient consensus solving for arbitrary patch decompositions.
New ADMM convergence rates for graph consensus problems identified.
problem Understanding ADMM convergence rates over graph consensus problems.
method Characterization of ADMM convergence using graph topology and random walks.
result ADMM convergence rate is faster than GD by a square root factor for certain graphs.
High-probability bound for distributed stochastic approximation tracking error.
problem Analyzing the convergence of distributed stochastic approximation schemes.
method Analysis using ODE approach to stochastic approximation.
result High probability bound for tracking error between iterates and limiting differential equation.
FedCBO solves clustered federated learning by optimizing groups of users without knowing their structure.
problem Training models for multiple users with privacy and communication constraints, especially in clustered settings.
method FedCBO uses a particle system approach inspired by consensus-based optimization to train models for each user group.
result FedCBO outperforms other methods in training models for clustered federated learning.
Develop intrinsic consensus-based optimization framework on Riemannian manifolds with bounded curvature.
problem Nonconvex optimization on manifolds
method Intrinsic consensus-based optimization on Riemannian manifolds with bounded curvature
result Global convergence of the mean-field equation toward a global minimizer of the objective function.
The task of clustering a set of objects based on multiple sources of data arises in several modern applications. We propose an integrative statistical model that permits a separate clustering of the objects for each data source. These separate clusterings adhere loosely to an overall consensus clustering, and hence the…
Distributed algorithm finds global solutions for low-rank matrices.
problem Finding global solutions for low-rank matrices in distributed systems.
method Distributed Gradient Descent (DGD+) with LOCAL variables.
result DGD+LOCAL converges to global minimizer with exact consensus.
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.