New method learns dynamic brain communication patterns across regions.
problem Current methods struggle with time-varying brain communications and scalability.
method Adaptive Delay Model (ADM) using Markovian Gaussian Processes.
result Captures dynamic neural communication patterns over time.
Paper presents an algorithm for optimal regret in communicating Markov decision processes.
problem Achieving optimal regret in Markov decision processes with a communicating assumption.
method The algorithm explicitly tracks the constant K(M) to learn optimally, balancing exploration, co-exploration, and exploitation.
result The algorithm achieves asymptotically optimal regret K ( M ) log ( T ) + o ( log ( T ) ) K(M) \log(T) + \mathrm{o}(\log(T)) K ( M ) log ( T ) + o ( log ( T )) for communicating Markov decision processes. New model for clustering dependent community Hawkes processes in temporal networks.
problem Modeling strong dependence and community structure in temporal networks.
method Dependent Community Hawkes (DCH) models combining stochastic block models and Hawkes processes.
result Spectral clustering error bound derived for DCH models.
Study on network-valued processes with asynchronous updates, proving consistency in community and changepoint estimation.
problem Understanding the behavior of network-valued stochastic processes with asynchronous updates.
method Analysis of concentration properties of aggregated adjacency and Laplacian matrices for lazy network-valued stochastic processes.
result Demonstrates consistency of estimators in community and changepoint estimation problems.
Study hypothesis testing under quantized samples with communication constraints, achieving near-optimal sample complexity.
problem Optimizing hypothesis testing with quantized samples and communication constraints.
method Developed a polynomial-time algorithm achieving near-optimal sample complexity under communication constraints.
result Achieved near-optimal sample complexity under communication constraints, with a logarithmic factor increase over unconstrained setting.
Online algorithm detects community structure in dynamic event streams.
problem Community detection in networks with temporal event streams.
method Continuous-time point process latent network models with fast online variational inference.
result Online inference achieves comparable community recovery to non-online methods but with computational gains.
Novel network models capture social interaction features like reciprocity and community.
problem Modeling temporal social interaction data with reciprocity and community structure.
method Self-exciting Hawkes point processes with conditional intensity function.
result Proposed model outperforms competing approaches for link prediction.
Community detection has been an active research area for decades. Among all probabilistic models, Stochastic Block Model has been the most popular one. This paper introduces a novel probabilistic model: RW-HDP, based on random walks and Hierarchical Dirichlet Process, for community extraction. In RW-HDP, random walks c…
Survey reviews code-switched speech and language processing.
problem Processing code-switched text and speech for multilingual communities.
method Reviews computational approaches and lists available resources.
result Essential for building intelligent agents that interact in multilingual settings.
A new model detects complex network communities using node attributes.
problem Lack of methods integrating node attributes for community detection in attributed networks.
method BCSBM model that integrates betweenness centrality and clustering coefficient of nodes.
result BCSBM model outperforms other methods in detecting various network structures.
DFM model detects communities in weighted networks without distributional assumptions.
problem Challenges in community detection for weighted networks.
method Distribution-Free Model (DFM) for weighted networks, using spectral clustering.
result Spectral clustering yields consistent community detection under DFM.
This paper introduces a new framework for collective online learning of Gaussian processes in massive multi-agent systems.
problem The inefficiency of centralized communication in distributed machine learning systems.
method A novel Collective Online Learning Gaussian Process framework that allows each agent to build its local model and exchange it with others via peer-to-peer communication.
result Empirical results demonstrate the efficiency of the framework on both synthetic and real-world datasets.
The paper optimizes learning Gaussian Processes in distributed systems with minimal communication.
problem Learning Gaussian Processes in distributed and communication-limited systems.
method Information-theoretic bounds and practical schemes like per-symbol quantization.
result The proposed methods outperform previous zero-rate distributed GP learning schemes.
We study the tradeoff between the statistical error and communication cost of distributed statistical estimation problems in high dimensions. In the distributed sparse Gaussian mean estimation problem, each of the m m m machines receives n n n data points from a d d d -dimensional Gaussian distribution with unknown mean θ θ θ w…
Improved distributed learning with reduced communication costs.
problem Efficient communication in resource-constrained environments for distributed learning.
method Proposed a cost-effective partial communication protocol.
result Communication cost is reduced to O ( log T ) O(\log T) O ( log T ) , improving significantly on full communication. Distributed Thompson sampling improves regret convergence in constrained communication networks.
problem Maximizing a black-box function with multi-agent Bayesian optimization under communication constraints.
method Distributed Thompson sampling using Gaussian processes, with theoretical bounds on regret convergence.
result Theoretical bounds on Bayesian average and simple regret depend on communication graph structure and are applicable in constrained networks.
SparCML improves machine learning scalability by handling sparse data efficiently.
problem Scalability bottleneck in machine learning due to global allreduce operations.
method Design and implement communication protocols for sparse data, extending MPI.
result SparCML extends MPI to support sparse data, improving scalability.
The study detects deceptive language in business communication using AI.
problem Deceptive language in business communication.
method Combining classical rhetoric, communication psychology, and linguistic theory with computational textual analysis and transformer models.
result Detection accuracies of over 99% achieved in controlled settings.
TUCRL efficiently explores and exploits in non-communicating MDPs without prior knowledge.
problem Efficient exploration-exploitation in non-communicating Markov Decision Processes (MDPs).
method Introduces TUCRL, the first algorithm for efficient exploration-exploitation in any finite MDP without prior knowledge.
result Derives a O ~ ( D e x t t t C Γ e x t t t C S e x t t t C A T ) \widetilde{O}(D^{ exttt{C}} \sqrt{Γ^{ exttt{C}} S^{ exttt{C}} AT}) O ( D e x ttt C Γ e x ttt C S e x ttt C A T ) regret bound for weakly-communicating MDPs. This paper explores ML in power line communications, from modeling to diagnostics.
problem Improving efficiency and diagnostics in power line communications.
method Discusses classical ML models and their application to PLC at various layers.
result Demonstrates how ML can enhance various aspects of PLC.
Study uniform learnability of binary classification networks with communication.
problem Learning a network with communication between vertices from uniform ergodic Random Graph Process.
method Introduced structural Rademacher complexity and used martingale method and Marton's coupling.
result Uniform learnability as worst-case theoretical limits for binary classification problems.
Paper reduces communication costs in distributed LMS algorithms.
problem High communication costs in distributed LMS algorithms.
method Introduces a diffusion LMS strategy to reduce communication costs.
result Significantly reduces communication costs without compromising performance.
New model predicts network events better than existing ones.
problem Existing models can't capture complex network structures.
method Proposed MULCH model using multivariate Hawkes processes.
result MULCH model outperforms other models in predictions and generation.
Paper proposes MLPCD for protein community detection in large PPI networks.
problem Identifying reliable protein communities from large-scale PPI networks.
method Integrates Gene Expression Data and uses Multi-source Learning with cloud computing.
result Demonstrates superior performance compared to existing methods.
Sketch-based approach detects community events in evolving networks.
problem Community detection in time-varying networks.
method Maintains a small sketch graph to capture essential community structure.
result Efficiently identifies six key community events during network evolution.
Optimal client sampling reduces communication in federated learning.
problem Efficiently aggregate model updates from distributed clients in federated learning.
method Model weights as an Ornstein-Uhlenbeck process to estimate uncommunicated updates; optimal client sampling strategy.
result Significant reduction in communication with competitive or superior performance.
Agents collaboratively learn optimal policies in MDPs with limited capabilities.
problem Learning optimal policies in MDPs with heterogeneous agents and limited communication.
method Introduced concepts of leakage probabilities and proposed Federated-Q protocol (FedQ) for collaborative learning.
result FedQ protocol effectively aggregates knowledge and modifies learning problems for further training.
GraphHull models networks with clear multi-scale explanations of community structure.
problem Lack of self-explainable models in graph machine learning.
method Two-level convex hulls with global archetypes and local prototypes.
result GraphHull models networks with clear multi-scale explanations.
Parallel training speeds up neural network training, but process communication costs limit efficiency.
problem Efficiently training complex neural networks in real-time.
method Implemented Network Parallel Training using Cannon's Algorithm for matrix multiplication.
result Increasing the number of processes speeds up training until communication costs become prohibitive.
Study compares community detection methods in various networks.
problem Determine which community detection method is best for specific network types.
method Comprehensive empirical analysis of multiple methods on diverse network categories.
result Identifies different types of communities produced by various methods.
Study recovers community structure from coarse graph measurements.
problem Community recovery from low-resolution graph measurements.
method Formalized coarsening process of graph measurements, developed conditions for perfect recovery.
result Simple and closed-form asymptotic conditions for perfect recovery of coarse graph communities.
The paper analyzes how market prices respond to information processing and non-linear dynamics.
problem Understanding how market prices change in response to information.
method Logistic Continuous Wavelet Transformation method applied to SP 500 market data.
result Identifies patterns in market dynamics and describes them using a new theory of reflexive communication.
UR-FUNNY dataset aids in understanding multimodal humor.
problem Understanding humor in a multimodal context is understudied.
method Developed a multimodal dataset (UR-FUNNY) for humor detection.
result UR-FUNNY opens the door to multimodal humor detection research.
CHIP model detects communities in continuous-time networks efficiently.
problem Detecting communities in large, timestamped networks.
method Spectral clustering on aggregated adjacency matrix of Hawkes process model.
result Consistent community detection for growing networks with efficient estimation.
VEC-SBM detects communities using side information like texts and images.
problem Community detection in social networks with side information.
method Proposes a novel algorithm based on iterative refinement techniques.
result Optimally recovers latent communities with side information.
This article improves communication efficiency in distributed ML over wireless networks.
problem Achieving high ML inference accuracy at scale with zero communication latency.
method Optimizing communication payload types, techniques, scheduling, and ML architectures.
result Communication-efficient and distributed learning frameworks are presented.
Paper sets fundamental limits for distributed covariance estimation with constrained communication.
problem Estimating high-dimensional covariance matrices in a feature-split setting with limited communication.
method Developed a Conditional Strong Data Processing Inequality (C-SDPI) to establish minimax lower bounds and an optimal estimation protocol.
result Achieved nearly optimal estimation protocol with sample and communication requirements matching lower bounds up to logarithmic factors.
A new method speeds up community detection in graphs.
problem Efficiently detecting communities in large graphs.
method Subsampled semidefinite programming for faster clustering.
result Statistical limits of sketching for community detection established.
I2C enables agents to learn efficient communication without redundancy.
problem Redundant broadcast communication in multi-agent cooperation.
method I2C learns a prior for agent-agent communication via causal inference and reinforcement learning.
result I2C reduces communication overhead and improves multi-agent cooperative performance.
Paper explores DNNs in modulation recognition with channel effects and adversarial attacks.
problem Automatic modulation recognition in wireless communications.
method Deep neural networks applied to channel models and adversarial perturbations.
result Impact of channel variations and adversarial attacks on DNN performance.
This study analyzes satellite communication latency using a stochastic geometry model.
problem Latency analysis of LEO satellite relay communication systems.
method Stochastic geometry framework with spherical BPP models, suboptimal satellite relay selection strategy.
result Derives distance distributions and analytical expressions for transmission delays.
ComEx protocol reduces communication costs in cooperative bandits.
problem Minimizing communication costs in cooperative bandits while maintaining optimal performance.
method Developed ComEx protocol to reduce communication from Θ ( T ) Θ(T) Θ ( T ) to O ( log T ) O(\log T) O ( log T ) messages. result Achieves state-of-the-art performance with significantly reduced communication cost.
The paper examines efficient algorithms for linear regression over resource-limited networks.
problem Efficient communication in distributed learning over resource-limited networks.
method Developed algorithms for communication-efficient learning of linear regression tasks.
result The algorithms enable a tradeoff between communication and learning with theoretical performance guarantees.
This paper reviews techniques for distributed learning with non-convex models.
problem Optimizing non-convex models in distributed learning.
method Selective review of recent techniques for batch and streaming data.
result Trade-offs between computation and communication costs in distributed learning.
This study connects Gaussian processes and RKHS, bridging two machine learning communities.
problem Understanding the relationship between Gaussian processes and RKHS.
method Examining connections and equivalences in regression, interpolation, and other topics.
result Established the equivalence between Gaussian Hilbert space and RKHS.
Proposes CCCVAE for better single-cell clustering with cell-cell communication.
problem Improving single-cell RNA sequencing clustering by incorporating cell-cell communication.
method Integrates cell-cell communication into a variational autoencoder framework.
result Empirical results show CCCVAE outperforms standard VAEs in clustering performance.
New algorithm reduces sample and communication complexities in federated Q-learning.
problem Optimal Q-function learning in federated Q-learning with limited communication.
method Introduced Fed-DVR-Q algorithm for order-optimal sample and communication complexities.
result Complete characterization of sample-communication complexity trade-off.
A hierarchical gamma process infinite edge partition model is proposed to factorize the binary adjacency matrix of an unweighted undirected relational network under a Bernoulli-Poisson link. The model describes both homophily and stochastic equivalence, and is scalable to big sparse networks by focusing its computation…