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.
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.
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.
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…
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.
Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem. Among the existing methods, Low-Rank Representation (LRR) based method is quite superior in terms of its effectiveness, intu…
Mobile agents classify images via reinforcement learning and consensus.
problem Image classification using multiple mobile agents.
method Proposed network architecture for local belief formation and feature extraction. Decentralized consensus protocol using reinforcement learning.
result Effectiveness of the proposed framework demonstrated on MNIST dataset.
Quantized Decentralized Gradient Descent (QDGD) solves distributed optimization with quantized communications.
problem Minimizing the sum of smooth and strongly convex functions over a network of distributed agents with quantized communications.
method Proposes QDGD algorithm combining quantized and local information for decentralized gradient descent.
result Achieves vanishing mean solution error under strong convexity and smoothness assumptions.
This paper tackles ranking preferences through local consensus, improving prediction accuracy.
problem Predicting individual preferences over a set of items based on observed characteristics.
method Proposes ranking median regression, introducing local consensus/median for efficient learning.
result Developed efficient methods for ranking median regression, achieving fast learning rates.
New algorithms optimize decentralized convex optimization with near optimal communication and computation.
problem Decentralized convex optimization in large-scale machine learning and sensor networks.
method Novel algorithms combining Nesterov's acceleration, multi-consensus, and gradient-tracking.
result Achieves optimal computation and near optimal communication complexity, matching lower bounds.
Algorithm for distributed computation of Wasserstein Barycenters over networks.
problem Computing Wasserstein Barycenters in a network of nodes with local interactions.
method Class-optimal algorithm for distributed computation over graph topology.
result Every node can reach the barycenter of all distributions in the network with local interactions.
The paper connects PSO and CBO methods using stochastic modeling and mean-field limits.
problem Global optimization problems with particle swarm optimization and consensus based optimization.
method Stochastic differential equations and mean-field approximation to derive macroscopic hydrodynamic equations.
result Derives mean-field approximation for PSO and links it to CBO methods.
Distributed learning method for multi-agent reinforcement learning with policy coordination.
problem Solving multi-agent reinforcement learning problems with coordination.
method Distributed off-policy actor critic with policy consensus.
result The proposed algorithm achieves asymptotic agreement on the global optimal policy function.
Proposes COLA, a communication-efficient algorithm for decentralized optimization.
problem Decentralized consensus optimization over a network.
method Linearization and communication-censoring strategy to reduce computation and communication costs.
result Proven convergence and established convergence rates for COLA.
Paper addresses FL over MAC with DP constraints, proposing a novel consensus scheme.
problem Federated learning over a multiple access channel with differential privacy constraints.
method Proposes a novel consensus scheme using digital distributed stochastic gradient descent (D-DSGD) with artificial noise to preserve DP.
result Demonstrates improved convergence rate and DP level for a given MAC capacity.
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.
Although many successful ensemble clustering approaches have been developed in recent years, there are still two limitations to most of the existing approaches. First, they mostly overlook the issue of uncertain links, which may mislead the overall consensus process. Second, they generally lack the ability to incorpora…
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.
New research shows sparse topologies can lead to faster convergence in distributed optimization.
problem The impact of worker communication topology on convergence speed in distributed optimization.
method Consensus-based distributed optimization methods with local averaging and correction based on local data.
result Sparse topologies can lead to faster convergence in distributed optimization without communication delays.
A new method for MARL with partial observations reduces communication overhead.
problem Inefficient MARL algorithms in large-scale problems due to state and action information sharing.
method Distributed zeroth-order policy optimization with local policy gradient estimation using consensus.
result The method converges to a policy that is a stationary point of the global objective function.
Decentralized optimization on dynamic manifolds with improved regret bound.
problem Optimizing on nonstationary Riemannian manifolds in decentralized systems.
method Decentralized projected Riemannian gradient descent with weighted Frechet mean consensus.
result Achieved dynamic regret bound of O ( T ( 1 + P T ) / ( 1 − σ 2 ( W ) ) ) {\cal O}(\sqrt{T(1+P_T)}/\sqrt{(1-σ_2(W))}) O ( T ( 1 + P T ) / ( 1 − σ 2 ( W )) ) . Agents learn locally, converge globally in online learning with kernels.
problem Multi-agent learning with limited data and communication.
method Local regression functions with consensus constraints, functional stochastic gradient descent, and greedy subspace projections.
result Agents' functions converge to a neighborhood of the globally optimal one as the penalty parameter increases.
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.
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.
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.
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.
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.
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.
Bayesian mixture models inference in federated learning
problem Inference of Bayesian mixture models in federated learning
method Consensus Monte Carlo approach
result Recovery of small clusters with greater accuracy than standard MCMC
New algorithms for collaborative reinforcement learning with limited communication.
problem Efficiently learning value functions in multi-agent systems with strict information constraints.
method Distributed gradient-based temporal difference algorithms with consensus schemes.
result Parameter estimates converge to ODEs with defined invariant sets under general assumptions.
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. 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.
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…
DADAM optimizes distributed adaptive learning with low communication costs.
problem High communication costs in parallelizing adaptive gradient methods.
method Consensus-based distributed adaptive moment estimation method.
result Outperforms centralized adaptive algorithms for certain loss functions.
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 method for decentralized learning reduces data and computation needs.
problem High complexity and data/compute requirements for neural network training.
method Asynchronous updates over unreliable network using Distributed Averaging Consensus.
result Models can be learned on highly biased datasets with intermittent communication.
The paper considers a class of multi-agent Markov decision processes (MDPs), in which the network agents respond differently (as manifested by the instantaneous one-stage random costs) to a global controlled state and the control actions of a remote controller. The paper investigates a distributed reinforcement learnin…
A new algorithm reduces training time for distributed machine learning by dynamically assigning backup workers.
problem Time-consuming synchronization phase due to slow workers (stragglers).
method Dynamic allocation of backup workers to minimize waiting time.
result Achieves linear speedup in convergence performance with more workers.
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.
COKE reduces communication in decentralized kernel learning.
problem Decentralized kernel learning challenges due to data-dependent decision variables.
method Random feature approximation and ADMM for iterative optimization; communication-censored algorithm.
result COKE achieves faster convergence and reduced communication load.
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.
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.
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…