Study on limits of LLM-based multi-agent planning reliability.
problem Reliability limits of LLM-based multi-agent planning.
method Modeling LLM-based multi-agent architecture as a decision network, showing dominance by centralized Bayes decision maker.
result Optimizing multi-agent directed acyclic graphs under communication budget is equivalent to choosing a constrained experiment.
Paper uses deep learning to optimize resource allocation in ultra-reliable communications.
problem Optimizing resource allocation for ultra-reliable and low-latency communications.
method Unsupervised deep learning for joint power and bandwidth allocation.
result Deep learning finds an approximated optimal solution with QoS constraints.
Proposes deep-RL with GANs for ultra-reliable low-latency communication.
problem Resource allocation for URLLC with high reliability and low latency.
method Experienced deep-RL framework using GANs to pre-train and optimize resource allocation.
result Deep-RL framework achieves near-optimal reliability and latency under URLLC constraints.
We investigate the problem of reliable communication between two legitimate parties over deletion channels under an active eavesdropping (aka jamming) adversarial model. To this goal, we develop a theoretical framework based on probabilistic finite-state automata to define novel encoding and decoding schemes that ensur…
We investigate the problem of truth discovery based on opinions from multiple agents who may be unreliable or biased. We consider the case where agents' reliabilities or biases are correlated if they belong to the same community, which defines a group of agents with similar opinions regarding a particular event. An age…
This paper tackles URLLC in 6G networks with deep learning.
problem Stringent requirements on end-to-end delay and reliability for mission-critical applications.
method Develops a multi-level architecture combining theoretical models and real-world data, using deep transfer learning and federated learning.
result Demonstrates improved performance in URLLC for mission-critical applications.
Paper predicts interference for better LA in URLLC.
problem Improving LA for URLLC with strict latency and reliability.
method Exploits time correlation of interference for prediction.
result Predicted interference improves LA for URLLC.
Bayesian methods detect clusters in noisy data more reliably.
problem Noisy data distorts traditional clustering methods, leading to unreliable results.
method Bayesian community detection using Minimum Description Length principle.
result Bayesian methods identify more robust clusters in noisy data.
The design of codes for communicating reliably over a statistically well defined channel is an important endeavor involving deep mathematical research and wide-ranging practical applications. In this work, we present the first family of codes obtained via deep learning, which significantly beats state-of-the-art codes …
New AI model improves grid planning efficiency and reliability.
problem Improving distribution grid planning with AI for energy sustainability.
method Hyperstructures Graph Convolutional Neural Networks (Hyper-GCNNs) with attention mechanism.
result Hyper-GCNNs outperforms existing models in computational efficiency and accuracy.
Paper explores physics-informed deep learning for system reliability assessment.
problem Limited study on deep learning for system reliability assessment.
method Physics-informed deep learning approach for system reliability assessment.
result Physics-informed deep learning can alleviate computational challenges and combine measurement data and mathematical models.
New model predicts links in community-based networks robustly.
problem Link prediction in community-based networks with local clustering errors.
method Markov Stochastic Block Model (MSBM) with Hidden Markov Model (HMM) predictions.
result Misclassification error decays exponentially with relevant signal-to-noise ratio (SNR).
NeurIPS 2019 program improves reproducibility in machine learning.
problem Ensuring machine learning research results are reproducible and reliable.
method Code submission policy, reproducibility challenge, and checklist integration.
result Improved reproducibility standards across the machine learning community.
Study evaluates machine learning methods for large-scale network reliability, revealing ANN's and PR's performance.
problem Tackles the NP-hard problem of approximating binary-state network reliability for large-scale systems.
method Compares 20 machine learning methods across three reliability regimes and evaluates their performance on large-scale networks.
result Large-scale networks with arc reliability ≥ 0.9 exhibit near-unity system reliability, enabling computational simplifications.
New methods for distributed CP improve reliability in healthcare.
problem Post-hoc calibration of models in safety-critical domains.
method Quantile-based and histogram-based distributed conformal prediction via message passing.
result Improved reliability and coverage guarantees in decentralized settings.
Paper defends against signal modulation detection attacks.
problem Minimizing intruder accuracy while maintaining receiver reliability.
method Adversarial perturbation of channel input symbols.
result Approach makes wireless communication secure with minimal performance loss.
Most real-world networks exhibit community structure, a phenomenon characterized by existence of node clusters whose intra-edge connectivity is stronger than edge connectivities between nodes belonging to different clusters. In addition to facilitating a better understanding of network behavior, community detection fin…
This work tackles community detection in networks with node attributes, achieving exact recovery.
problem Community detection in networks with correlated node attributes.
method Information-theoretic criterion and iterative clustering algorithm maximizing joint likelihood.
result Exact recovery of community labels under a general model for network and node attributes.
In this paper, a novel joint transmit power and resource allocation approach for enabling ultra-reliable low-latency communication (URLLC) in vehicular networks is proposed. The objective is to minimize the network-wide power consumption of vehicular users (VUEs) while ensuring high reliability in terms of probabilisti…
New system preserves message meaning in wireless networks, improving data rate.
problem Efficiently transmitting message meaning in wireless networks.
method Modeling semantics as hidden random variables, using Information Bottleneck for compression.
result 20 dB SNR improvement for semantic communication.
In standard graph clustering/community detection, one is interested in partitioning the graph into more densely connected subsets of nodes. In contrast, the "search" problem of this paper aims to only find the nodes in a "single" such community, the target, out of the many communities that may exist. To do so , we are …
A framework for multi-agent communication over noisy channels in reinforcement learning.
problem Effective communication between multiple agents in a noisy environment.
method A novel multi-agent partially observable Markov decision process (MA-POMDP) framework considering noisy communication channels.
result Jointly learned policies outperform separate learning of communication and decision making.
Optimizes communication in federated learning using rate-distortion theory.
problem Reduces communication cost in federated learning while maintaining model accuracy.
method Applies rate-distortion theory to model updates, proposing distortion as a proxy for accuracy.
result Near-optimal communication reduction, outperforming other methods on a FL benchmark.
Learning-based link scheduling improves network performance in millimeter-wave multi-connectivity.
problem Efficient link scheduling is crucial for maximizing network performance in millimeter-wave multi-connectivity.
method A learning-based approach to predict optimal link scheduling.
result The learning-based solution outperforms base line methods and approaches the optimal solution.
Synthetic experiments are crucial for assessing causal machine learning methods.
problem Current empirical evaluations of causal machine learning methods are insufficient and unreliable.
method Propose principles for conducting rigorous empirical analyses with synthetic data.
result Rigorous synthetic experiments are essential for building trust in causal machine learning methods.
One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph. In this paper, we propose AMOS…
Efficient algorithm for CLSBM reduces misclassification rate.
problem Reducing misclassification in community detection for CLSBM.
method Spectral-based algorithm for CLSBM, with theoretical misclassification bounds.
result Upper bound on misclassification rate of efficient algorithm.
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.
Paper learns tree-structured models from distributed data efficiently.
problem Learning tree-structured Gaussian graphical models from distributed data with limited communication.
method Communication-efficient strategies for central node to learn structure from distributed data.
result Tree structure can be reliably recovered even with limited information (signs of local data samples).
Proposes a secure communication method independent of eavesdropper's decoder.
problem Lack of practical security constraints in existing methods.
method Dual MINE-based neural secure communications model.
result Security performance is not affected by eavesdropper's decoding means.
The possibility of latency arbitrage in financial markets has led to the deployment of high-speed communication links between distant financial centers. These links are noisy and so there is a need for coding. In this paper, we develop a gametheoretic model of trading behavior where two traders compete to capture laten…
New model predicts hate speech with uncertainty estimates.
problem Detect and explain hate speech without restricting free speech.
method Adapted deep neural networks with Monte Carlo dropout.
result Models can reliably assess when texts are borderline.
End-to-end learning of codes for secure BPSK communication in Gaussian wiretap channel.
problem Secure communication in the presence of an adversary for BPSK modulation.
method Learning finite-length codes using deep neural networks (DNNs) with mutual information estimation.
result Learned codes achieve near-optimal security performance, as demonstrated by numerical results.
Paper proposes unsupervised learning for optimizing deep neural networks in time-sensitive applications.
problem Inaccurate optimization solutions from supervised learning for real-time applications.
method Proposes unsupervised deep learning to learn latent function with optimization constraints as supervision.
result DNN can optimize variable problems in ultra-reliable communications without supervision labels.
POTA improves short text clustering by generating reliable pseudo-labels.
problem Limited discriminative representations in short texts.
method POTA uses instance-level attention and optimal transport for semantic consistency and cluster structure.
result POTA outperforms state-of-the-art methods in short text clustering.
Study neural communication systems with bandwidth-limited channels.
problem Reliable message transmission despite noisy channels.
method Jointly model compression and error correction with neural networks; introduce prior for missing information; use auxiliary latent variables.
result Joint neural communication systems outperform separate models under expected information loss.
Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment. Su…
Spatial statisticians and quantitative investors use the same mathematical object: a Schur complement, damped by one parameter.
problem The Schur complement is used in both spatial modeling and portfolio allocation, but the parameters are different.
method The Schur complement is interpreted as reliability shrinkage of a conditional Gaussian.
result The Schur complement is the same in both applications.
One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the dat…
SAEs struggle with feature consistency across runs, hindering MI reliability.
problem Inconsistency of learned SAE features across different training runs.
method Propose using the Pairwise Dictionary Mean Correlation Coefficient (PW-MCC) to measure feature consistency.
result High levels of feature consistency (0.80 for TopK SAEs on LLM activations) are achievable with appropriate architectural choices.
Study examines human factors in radiographic testing to improve inspection performance.
problem Insufficient consideration of human and organizational factors in NDT.
method CREAM method applied to analyze and model HOF on radiogram interpretation tasks.
result Model CREAM well-adapted for estimating HOF impact on NDT performances.
Paper proposes evaluating clinical models under domain shifts for reliability.
problem Clinical models often fail to generalize to new environments due to domain shifts.
method Develops realistic scenarios based on disease landscapes for multi-label classification.
result Deep clinical models can fail to generalize in specific data regimes.
Paper proposes RL algorithms for optimizing cellular network performance.
problem Improving cellular network performance against wireless impairments.
method Formulated as a reinforcement learning problem and developed two algorithms: PC and SON.
result RL algorithms outperform industry standards in simulated environments.
The paper applies communication theory to improve language model reranking.
problem Ensuring safety and accuracy in language model outputs.
method Drawing parallels between communication theory and language model reranking, the authors propose a protocol to improve reliability.
result The proposed protocol can achieve asymptotically error-free performance in noisy communication scenarios.
An active learning approach reduces AoI violation in vehicular networks.
problem Dynamic nature of vehicular networks makes resource allocation challenging.
method Gaussian process regression (GPR) for online decentralized active learning.
result Significant improvement in AoI violation probability with at least 50% reduction.
A feature-rich Bitcoin trading assistant using reinforcement learning.
problem Predicting and analyzing Bitcoin market trends for long-term profits.
method Reinforcement learning with technical indicators and Twitter sentiment scores.
result Average profit of 69% over 685 days, including the Covid-19 period.
New algorithm for multi-player bandits with collision-dependent rewards.
problem Stochastic multi-player multi-armed bandits with collision-dependent reward distributions.
method Error-Correction Collision Communication (EC3) algorithm.
result EC3 algorithm achieves optimal regret approaching centralized MP-MAB regret.
New method for community detection in sparse directed SBMs with exact recovery guarantees.
problem Exact recovery in sparse directed SBMs, especially with growing communities.
method Two-stage procedure: neighborhood-smoothing followed by K-means clustering. result Exact recovery of all community labels with probability tending to one under mild sparsity and separation conditions.